Development and classification of autonomous vehicle's ambiguous driving scenario

Human drivers are gradually being replaced by highly automated driving systems, and this trend is expected to persist. The response of autonomous vehicles to Ambiguous Driving Scenarios (ADS) is crucial for legal and safety reasons. Our research focuses on establishing a robust framework for developing ADS in autonomous vehicles and classifying them based on AV user perceptions. To achieve this, we conducted extensive literature reviews, in-depth interviews with industry experts, a comprehensive questionnaire survey, and factor analysis. We created 28 diverse ambiguous driving scenarios and examined 548 AV users' perspectives on moral, ethical, legal, utility, and safety aspects. Based on the results, we grouped ADS, with all of them having the highest user perception of safety. We classified these scenarios where autonomous vehicles yield to others as moral, bottleneck scenarios as ethical, cross-over scenarios as legal, and scenarios where vehicles come to a halt as utility-related. Additionally, this study is expected to make a valuable contribution to the field of self-driving cars by presenting new perspectives on policy and algorithm development, aiming to improve the safety and convenience of autonomous driving.

What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors

The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.

Quantification of safety improvements and human-machine tradeoffs in the transition to automated driving

The assumption of reduced human error-related crashes with increasing levels of automation in pursuing Level 5 automation lacks empirical evidence. As automation levels rise, human error-induced safety hazards are anticipated to decrease, while machine error-induced hazards will increase. However, a quantitative index capturing this tradeoff is absent. Additionally, theoretical modeling of safety improvements during the transition to automated driving remains unexplored, particularly concerning reducing human error-related hazards. These limitations impede the understanding of safety from human and machine perspectives for Automated Vehicle (AV) specialists and manufacturers. This research addresses these gaps by investigating safety performance associations between human and machine factors using the "Human-Machine conflict reduction ratio" (H/M ratio), a novel metric. The study aims to establish safety improvements related to human errors under various automation levels. Sixty participants completed driving tasks on a driving simulator at Levels 0, 4, 3, and 2. Safety performance measures, including conflict frequency and severity, were computed. As a result, Level 4 exhibits the largest decrease (93.3%) compared to manual driving, followed by Level 2 (70.7%) and Level 3 (40.5%). The H/M ratio measures the tradeoff between reducing human and machine error-induced hazards, with Level 2 demonstrating the highest ratio, followed by Levels 4 and 3. Safety performance is evaluated by considering all possible types of human errors at each automation level. Theoretical models from a human factor's perspective are employed to estimate safety improvements at each level. This research contributes to a comprehensive understanding of safety in the "human-machine cooperative driving" phase, offering insights to AV industry practitioners and stakeholders.

Effect of seat back angle on preferred seat pan inclination for the development of highly automated vehicles

Recent studies on occupants' safety in reclined positions suggest that a more inclined seat pan could be needed to reduce the occurrence of submarining. This study aimed to investigate whether a more inclined seat pan would also be comfortable for occupants. Eighteen volunteers participated in the experiment. They were asked to self-select seat pan inclination for seat back angles from 20 to 60 degrees using a reconfigurable experimental seat from two initial seat pan angles (10 and 40 degrees from the horizontal). On average, preferred seat pan angle varied from 11.3(±2.1, standard deviation) to 29.9(±6.8), 12.5(±3.8) to 37.4(±3.7), and 12.8(±4.8) to 38.6(±2.7) degrees for seat pan angles of 20, 40, and 60 degrees respectively. The shear force analysis suggests that the seat pan inclination might be self-selected to reduce the forward shear, while a high inclination angle with a noticeable backward shear was also preferred.Practitioner summary: Preferred range of seat pan inclination for different seat back angles studied for the development of highly automated vehicles. The present work provides quantitative guidelines for specifying comfortable seating in a reclined position.

The Utilitarian Virtual Self - Using Embodied Personalized Avatars to Investigate Moral Decision-Making in Semi-Autonomous Vehicle Dilemmas

Embodied personalized avatars are a promising new tool to investigate moral decision-making by transposing the user into the "middle of the action" in moral dilemmas. Here, we tested whether avatar personalization and motor control could impact moral decision-making, physiological reactions and reaction times, as well as embodiment, presence and avatar perception. Seventeen participants, who had their personalized avatars created in a previous study, took part in a range of incongruent (i.e., harmful action led to better overall outcomes) and congruent (i.e., harmful action led to trivial outcomes) moral dilemmas as the drivers of a semi-autonomous car. They embodied four different avatars (counterbalanced - personalized motor control, personalized no motor control, generic motor control, generic no motor control). Overall, participants took a utilitarian approach by performing harmful actions only to maximize outcomes. We found increased physiological arousal (SCRs and heart rate) for personalized avatars compared to generic avatars, and increased SCRs in motor control conditions compared to no motor control. Participants had slower reaction times when they had motor control over their avatars, possibly hinting at more elaborate decision-making processes. Presence was also higher in motor control compared to no motor control conditions. Embodiment ratings were higher for personalized avatars, and generally, personalization and motor control were perceptually positive features. These findings highlight the utility of personalized avatars and open up a range of future research possibilities that could benefit from the affordances of this technology and simulate, more closely than ever, real-life action.

Real-time combined safety-mobility assessment using self-driving vehicles collected data

The study presents a real-time safety and mobility assessment approach using data generated by autonomous vehicles (AVs). The proposed safety assessment method uses Bayesian hierarchical spatial random parameter extreme value model (BHSRP), which can handle the limited availability and uneven distribution of conflict data and accounts for unobserved spatial heterogeneity. The approach estimates two real-time safety metrics: the risk of crash (RC) and return level (RL), using Time-To-Collision (TTC) as conflict indicator. Additionally, a Risk Exposure (RE) index was developed to reflect the risk of an individual vehicle to travel through a corridor. In parallel, the mobility of corridor were assessed based on the highway Capacity manual methodology using real-time traffic data (Highway Capacity Manual, 2010). The study used a 440-hour AVs' dataset of a corridor in Palo Alto, California. After normalizing for each LOS representation in the dataset, LOS E was identified as the most hazardous operating condition with the highest average crash risk. However, the time spent under different operating condition would affect the safety of individual vehicles traveling through a road facility (i.e., vehicle's exposure time). Accounting for exposure time, the vehicle has the highest chance of encountering an extremely risky driving condition at intersections and segments under LOS D and E, respectively.

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles

OBJECTIVE

This study develops a computational model to predict drivers' response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs).

BACKGROUND

Previous research has modeled drivers' takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking.

METHOD

A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists.

RESULTS

Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting.

CONCLUSION

The current model can successfully predict drivers' response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs.

APPLICATION

This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance.

Safety performance evaluation of freeway merging areas under autonomous vehicles environment using a co-simulation platform

Merging areas serve as the potential bottlenecks for continuous traffic flow on freeways. Traffic incidents in freeway merging areas are closely related to decision-making errors of human drivers, for which the autonomous vehicles (AVs) technologies are expected to help enhance the safety performance. However, evaluating the safety impact of AVs is challenging in practice due to the lack of real-world driving and incident data. Despite the increasing number of simulation-based AV studies, most relied on single traffic/vehicle driving simulators, which exhibit limitations such as inaccurate description of AV behavior using pre-defined driving models, limited testing modules, and a lack of high-fidelity traffic scenarios. To this end, this study addresses these challenges by customizing different types of car-following models for AVs on freeway and developing a software-in-the-loop co-simulation platform for safety performance evaluation. Specifically, the environmental perception module is integrated in PreScan, the decision-making and control model for AVs is designed by Matlab, and the traffic flow environment is established by Vissim. Such a co-simulation platform is supposed to be able to reproduce the mixed traffic with AVs to a large extent. By taking a real freeway merging scenario as an example, comprehensive experiments were conducted by introducing a single AV and multiple AVs on the mainline of freeway, respectively. The single AV experiment investigated the performance of different car-following models microscopically in the case of merging conflict. The safety and comfort of AVs were examined in terms of TTC and jerk, respectively. The multiple AVs experiment examined the safety impact of AVs on mixed traffic of freeway merging areas macroscopically using the developed risk assessment model. The results show that AVs could bring significant benefits to freeway safety, as traffic conflicts and risks are substantially reduced with incremental market penetration rates.

Resilient interactions between cyclists and drivers, and what does this mean for automated vehicles?

The road transport system is a complex sociotechnical system that relies on a number of formal and informal rules of the road to ensure safety and resilience. Interactions between vulnerable road users and drivers often includes informal communication channels that are tightly linked to social norms, user expectations and the environmental context. Automated vehicles have a challenge in being able to communicate and respond to these informal rules of the road, therefore additional technologies are required to better support vulnerable road users. This paper presents the informal rules that cyclists and drivers employ within a cyclist overtake manoeuvre, through qualitative data collected from focus groups and interviews with road users. These informal rules are classified into the key elements of resilience (monitor, detect, anticipate, respond and learn) to understand how they guide the resilient interactions between road users. Using a human factors approach, the Perceptual Cycle Model shows how information is communicated between different road users and created by the situational context. This is then used to inform how automation will alter the communication between cyclists and drivers, and what additional feedback mechanisms will be needed to support the systems resilience. Technologies that can support these feedback mechanisms are proposed as avenues for future development.

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n  =  471) were fatal crashes, while the remaining 40% (n  =  321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

Modeling and analyzing self-resistance of connected automated vehicular platoons under different cyberattack injection modes

The high-level integration and interaction between the information flow at the cyber layer and the physical subjects at the vehicular layer enables the connected automated vehicles (CAVs) to achieve rapid, cooperative and shared travel. However, the cyber layer is challenged by malicious attacks and the shortage of communication resources, which makes the vehicular layer suffer from system nonlinearity, disturbance randomness and behavior uncertainty, thus interfering with the stable operation of the platoon. So far, scholars usually adopt the method of assuming or improving the car-following model to explore the platoon behavior and the defense mechanism in cyberattacks, but they have not considered whether the model itself has disturbance and impact on cyberattack defenses. In other words, it is still being determined whether the car-following model designed can be fully applicable to such cyberattacks. To provide a theoretical basis for vehicular layer modeling, it is necessary to comprehend the self-resistance of different car-following models faced on various cyberattacks. First, we review the car-following models adopted on the vehicular layer in cyberattacks, involving traffic engineering, physical statistics, and platoon dynamics. Based on the review, we divide the malicious attacks faced by the cyber layer into explicit attacks and implicit attacks. Second, we develop a cooperative generalized force model (CGFM), which combines and unifies the r-predecessors following communication topology. The proposed models, labeled the vulnerable cooperative intelligent driver model (VCIDM), the vulnerable cooperative optimal velocity model (VCOVM), and the vulnerable cooperative platoon dynamics model (VCPDM), incorporate the CGFM model and assorted cyberattack injection modes to explain the cyberattack effects on the platoon self-resistance capability. Upon the described models, we provide six indicators in three dimensions from the basic traffic element, including drivers, vehicles, and environment. These indicators illustrate driver tolerance, vehicle adaptability, and environmental resistance when a platoon faces attacks such as bogus information, replay/delay, and communication interruption. We arrange and reorganize the car-following models and the cyberattack injection modes to complete the research on the self-resistance capability of the platoon, which has positive research value and practical significance for enhancing the endogenous security at the vehicular layer and improving the intrusion tolerability at the cyber layer.

Assessing the collective safety of automated vehicle groups: A duration modeling approach of accumulated distances between crashes

Ideally, the evaluation of automated vehicles would involve the careful tracking of individual vehicles and recording of observed crash events. Unfortunately, due to the low frequency of crash events, such data would require many years to acquire, and potentially place the motorized public at risk if defective automated technologies were present. To acquire information on the safety effectiveness of automated vehicles more quickly, this paper uses the collective crash histories of a group of automated vehicles, and applies a duration modeling approach to the accumulated distances between crashes. To demonstrate the applicability of this approach as a method compare automated and conventional vehicles (human drivers), an empirical assessment was undertaken using two comparable sources of data. For conventional vehicles, police and non-police-reportable crashes were collected from the Second Strategic Highway Research Program's naturalistic driving study, and for automated vehicles, data from the California Department of Motor Vehicles Autonomous Vehicle Tester program were used (105 crashes from 59 permit holders driving ∼2.8 million miles were used for the analysis). The results of the empirical study showed that automated driving was safer at the 95% confidence level, with a higher number of miles between crashes, relative to their conventional vehicle counterparts. The findings indicate that the number of miles between crashes would be increased by roughly 27% when switching from conventional vehicles to automated vehicles. Despite limited data which mandated a group-vehicle approach, this study can be considered a reasonable initial approximation of automated vehicle safety.

Enhancing autonomous vehicle hyperawareness in busy traffic environments: A machine learning approach

As autonomous vehicles (AVs) advance from theory into practice, their safety and operational impacts are being more closely studied. This study aims to contribute to the ever-evolving algorithms used by AVs during travel in busy urban districts, as well as explore the potential utilization of AV sensor data to identify safety hazards to surrounding road users in real time. Accordingly, the study incorporates AV data collected from multiple cities in the United States to detect and categorize traffic conflicts that involve the source AVs, as well as conflicts that involve other surrounding road users. Then, a machine learning conflict prediction model is trained with Isolation Forest - Convolutional Neural Network - Long Short-Term Memory (IF-CNN-LSTM) layers. The model receives data in real time in the form of road user trajectories and headings to make an informed prediction of the potential frequency and severity of conflicts three seconds into the future. In addition, the transferability of the trained model to new data and locations is explored to understand the potential compromise in accuracy compared to the effort and cost of retraining. The results show that the proposed model is capable of predicting the possibility of conflict occurrence and conflict severity with high accuracy (sensitivity = 83.5 % and fallout = 11 %). The reported sensitivity of AV conflict prediction ranged between 89 % and 95 %, depending on conflict type, which outperforms most of the existing conflict prediction models. The model is also capable of predicting hazardous conflicts of surrounding road users in real time, with sensitivity values ranging between 82 % and 87 %, affirming the promising capabilities of onboard vehicle sensors in undertaking real-time safety applications. The model also retains good performance when transferred to different data, with the potential to retain nearly 97 % of the source model's performance if sufficient tuning data exists.

Designing for passengers' information needs on fellow travelers: A comparison of day and night rides in shared automated vehicles

Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence - especially at night - will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. Prior research points out that having information about fellow travelers could alleviate the concerns of passengers and we designed two user interface variants to investigate the role of this information in an exploratory within-subjects user study (N=24). Participants experienced four automated day and night rides with varying personal information about co-passengers in a simulated environment. The results of the mixed-method study indicate that having information about other passengers (e.g., photo, gender, and name) positively affects user experience at night. In contrast, it is less necessary during the day. Considering participants' simultaneously raised privacy concerns, balancing security and privacy demands poses a substantial challenge for resilient system design.

Safety-oriented automated vehicle longitudinal control considering both stability and damping behavior

Extensive research has examined the potential benefits of Automated Vehicles (AVs) for increasing traffic capacity and improving safety. However, previous studies on AV longitudinal control have focused primarily on control stability and instability or tradeoffs between safety and stability, neglecting the importance of vehicle damping characteristics. This study aims to demonstrate the significance of explicitly considering safety in addition to stability in AV longitudinal control through damping behavior analysis. Specifically, it proposes a safety-oriented AV longitudinal control and provides recommendations on the control parameters. For the proposed AV control, an Adaptive Cruise Control (ACC) model is integrated with damping behavior analysis to model AV safety under continuous traffic perturbations. Numerical simulations are conducted to quantify the relationship between mobility and safety for AVs considering both damping behavior and control stability. Different ACC control parameters are evaluated in terms of damping and stability properties, and their safety impacts are assessed based on various surrogate safety measures such as Deceleration Rate to Avoid Crash (DRAC), Crash Potential Index (CPI) and Time-Integrated Time-to-collision (TIT). The results indicate that an underdamped state (ACC damping ratio < 1) is less safe than the critically damped state (ACC damping ratio = 1) and the overdamped state (ACC damping ratio > 1). Furthermore, given the same AV car-following time lag, ACC with a damping ratio between 1 and 1.2 provides better safety performance. Increasing the AV car-following time lag can improve both safety and stability when the remaining ACC control parameters are kept the same. In this case, the optimal safety-oriented ACC regions also increase. The findings of this study provide important insights into designing safe and stable AV longitudinal control algorithms.

Using voice recognition to measure trust during interactions with automated vehicles

Trust in an automated vehicle system (AVs) can impact the experience and safety of drivers and passengers. This work investigates the effects of speech to measure drivers' trust in the AVs. Seventy-five participants were randomly assigned to high-trust (the AVs with 100% correctness, 0 crash, and 4 system messages with visual-auditory TORs) and low-trust group (the AVs with a correctness of 60%, a crash rate of 40%, 2 system messages with visual-only TORs). Voice interaction tasks were used to collect speech information during the driving process. The results revealed that our settings successfully induced trust and distrust states. The corresponding extracted speech feature data of the two trust groups were used for back-propagation neural network training and evaluated for its ability to accurately predict the trust classification. The highest classification accuracy of trust was 90.80%. This study proposes a method for accurately measuring trust in automated vehicles using voice recognition.

Key factors capturing the willingness to use automated vehicles for travel in China

With the continuous advancement of technology, automated vehicle technology is progressively maturing. It is crucial to comprehend the factors influencing individuals' intention to utilize automated vehicles. This study examined user willingness to adopt automated vehicles. By incorporating age and educational background as random parameters, an ordered Probit model with random parameters was constructed to analyze the influential factors affecting respondents' adoption of automated vehicles. We devised and conducted an online questionnaire survey, yielding 2105 valid questionnaires. The findings reveal significant positive correlations between positive social trust, perceived ease of use, perceived usefulness, low levels of perceived risk, and the acceptance of automated vehicles. Additionally, our study identifies extraversion and openness as strong mediators in shaping individuals' intentions to use automated vehicles. Furthermore, prior experience with assisted driving negatively impacts people's inclination toward embracing automated vehicles. Our research also provides insights for promoting the adoption of automated vehicles: favorable media coverage and a reasonable division of responsibilities can enhance individuals' intentions to adopt this technology.

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Pedestrians are a vulnerable road user group, and their crashes are generally spread across the network rather than in a concentrated location. As such, understanding and modelling pedestrian crash risk at a corridor level becomes paramount. Studies on pedestrian crash risks, particularly with the traffic conflict data, are limited to single or multiple but scattered intersections. A lack of proper modelling techniques and the difficulties in capturing pedestrian interaction at the network or corridor level are two main challenges in this regard. With autonomous vehicles trialled on public roads generating massive (and unprecedented) datasets, utilising such rich information for corridor-wide safety analysis is somewhat limited where it appears to be most relevant. This study proposes an extreme value theory modelling framework to estimate corridor-wide pedestrian crash risk using autonomous vehicle sensor/probe data. Two types of models were developed in the Bayesian framework, including the block maxima sampling-based model corresponding to Generalised Extreme Value distribution and the peak over threshold sampling-based model corresponding to Generalised Pareto distribution. The proposed framework was applied to autonomous vehicle data from Argoverse-a Ford Motors subsidiary. This autonomous vehicle fleet of Agro AI (owner of Argoverse dataset) is equipped with two 64 beams synchronised LiDAR sensors, a cluster of seven high-resolution cameras, and a pair of stereo-vison high-resolution cameras to capture surrounding road users' information within a range of 200 meters. A subset of the Argoverse dataset, focussing on an arterial corridor in Miami, USA, was used to extract pedestrian and vehicle trajectories. From these trajectories, vehicle-pedestrian conflicts were identified and measured using post encroachment time. The non-stationarity of extremes was captured by vehicle volume, pedestrian volume, average vehicle speed, and average pedestrian speed in the extreme value model. Both block maxima and peak over threshold sampling-based models were found to provide a reasonable estimate of historical pedestrian crash frequencies. Notably, the block maxima sampling-based model was more accurate than the peak over threshold sampling-based model based on mean crash estimates and confidence intervals. This study demonstrates the potential of using autonomous vehicle sensor data for network-level safety, enabling an efficient identification of pedestrian crash risk zones in a transport network.

Evidence of automated vehicle safety's influence on people's acceptance of the automated driving technology

Existing studies identified targeted audiences showing increases in Automated Vehicles (AV) acceptance after experiencing automated driving. However, there is still uncertainty regarding the reasons. Although some studies cited safety as the primary reason, there is no objective evidence from safety performance in verifying its impact on AV acceptance. This study contributes to the literature by quantitatively revealing why AV acceptance is changed after experiencing automated driving via a Structural Equation Modeling (SEM) method and objectively validating that safety is the primary factor in determining AV acceptance. Sixty drivers completed driving tasks on a driving simulator under Levels 0, 4, 3, and 2 and survey questions in between. As a result, the safety-related perceptions of AV were identified as reasons for affecting AV acceptance. Particularly, the evaluation of traffic conflicts and conflict severity validates the results from SEM, proving that safety is the primary and significant reason for influencing AV acceptance.

What do surrogate safety metrics measure? Understanding driving safety as a continuum

Road safety is an important public health issue; technology, policy, and educational interventions to prevent crashes are of significant interest to researchers and policymakers. In particular, there is significant ongoing research to proactively evaluate the safety of new technologies, including autonomous vehicles, before enough crashes occur to directly measure their impact. We analyze the distributional form of five diverse datasets that approximate motor vehicle safety incident severity, including one dataset of hard braking events that characterizes the severity of non-crash incidents. Our empirical analysis finds that all five datasets closely fit a lognormal distribution (Kolmogorov-Smirnov distance < 0.013; significance of loglikelihood ratio with other distributions < 0.000029). We demonstrate a linkage between two well-known but largely qualitative safety frameworks and the severity distributions observed in the data. We create a formal model of the Swiss Cheese Model (SCM) and show through analysis and simulations that this formalization leads to a lognormal distribution of the severity continuum of safety-critical incidents. This finding is not only consistent with the empirical data we examine, but represents a quantitative restatement of Heinrich's Triangle, another heretofore largely qualitative framework that hypothesizes that safety events of increasing severity have decreasing frequency. Our results support the use of more frequent, low-severity events to rapidly assess safety in the absence of less frequent, high-severity events for any system consistent with our formalization of SCM. This includes any complex system designed for robustness to single-point failures, including autonomous vehicles.

Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers' Driving Styles

OBJECTIVE

This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction.

BACKGROUND

Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers.

METHOD

Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires.

RESULTS

Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting.

CONCLUSION

Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs.

APPLICATION

This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

Moral reasoning behind the veil of ignorance: An investigation into perspective-taking accessibility in the context of autonomous vehicles

Perspective-taking (PT) accessibility has been recognized as an important factor in affecting moral reasoning, also playing a non-trivial role in moral investigation towards autonomous vehicles (AVs). A new proposal to deepen this effect leverages the principles of the veil of ignorance (VOI), as a moral reasoning device aimed to control self-interested decisions by limiting the access to specific perspectives and to potentially biased information. Throughout two studies, we deepen the role of VOI reasoning in the moral perception of AVs, disclosing personal and contingent information progressively throughout the experiment. With the use of the moral trilemma paradigm, two different VOI conditions were operationalized, inspired by the Original Position theory by John Rawls and the Equiprobability Model by John Harsanyi. Evidence suggests a significant role of VOI reasoning in affecting moral reasoning, which seems not independent from the order in which information is revealed. Coherently, a detrimental effect of self-involvement on utilitarian behaviours was detected. These results highlight the importance of considering PT accessibility and self-involvement when investigating moral attitudes towards AVs, since it can help the intelligibility of general concerns and hesitations towards this new technology.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven - 40% 1st generation AVs- 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

Runtime unknown unsafe scenarios identification for SOTIF of autonomous vehicles

Safety is a critical concern for autonomous vehicles (AVs). Current testing approaches face challenges in simultaneously meeting the requirements of being valid, safe, and fast. To address these challenges, the silent testing approach that tests functions or systems in the background without interfering with driving is motivated. Building upon our previous research, this study first extends the method to specifically address the validation of AV perception, utilizing a lane marking detection algorithm (LMDA) as a case study. Second, field experiments were conducted to investigate the method's effectiveness in validating AV systems. For both studies, an architecture for describing the working principle is presented. The efficacy of the method in evaluating the LMDA is demonstrated through the use of adversarial images generated from a dataset. Furthermore, various scenarios involving pedestrians crossing a road under different levels of criticality were constructed to gain practical insights into the method's applicability for AV system validation. The results show that corner cases of the LMDA are successfully identified by the given evaluation metrics. Furthermore, the experiments highlight the benefits of employing multiple virtual instances with different initial states, enabling the expansion of the test space and the discovery of unknown unsafe scenarios, particularly those prone to false-positive objects. The practical implementation and systematic discussion of the method offer a significant contribution to AV safety validation.

Critical voxel learning with vision transformer and derivation of logical AV safety assessment scenarios

Safety assessment is an active research subject for autonomous vehicles (AVs) that have emerged as a new mode of mobility. In particular, scenario-based safety assessments have garnered significant attention. AVs can be tested on how they safely avoid hypothetical situations leading to accidents. However, scenarios written by humans based on their expert knowledge and experience may only partially reflect real-world situations. Instead, we are keen on a different technique of extracting statistically significant and more detailed scenarios from sensor data captured during the critical moments when AVs become vulnerable to potential accidents. Specifically, we first render the three-dimensional space around an AV with fixed-sized voxels. Then, we modeled the aggregate kinetics of the objects in each voxel detected by 3D-LiDAR sensors mounted on real test AVs. The Vision Transformer we used to model the kinetics helped us quickly pinpoint critical voxels containing objects that threatened the AV's safety. We traced the trajectory of the critical voxels on a visual attention map to describe in detail how AVs become vulnerable to accidents according to the logical scenario format defined by the PEGASUS Project. We tested our novel method with 250 h of 3D-LiDAR recordings capturing critical moments. We devised an inference model that detected critical situations with an F1-score of 98.26%. For each type of scenario, our model consistently identified the critical objects and their tendency to influence AVs. Given the evaluation results, we can ensure that our data-driven approach yields an AV safety assessment scenario with high representativeness, coverage, expansion, and computational feasibility.

Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

Knowledge as a key determinant of public support for autonomous vehicles

Autonomous vehicles (AVs) have the potential to revolutionize transportation safety and mobility, but many people are still concerned about the safety of AVs and hesitate to use them. Here we survey 4112 individuals to explore the relationship between knowledge and public support for AVs. We find that AV support has a positive relationship with scientific literacy (objective knowledge about science) and perceived understanding of AV (self-assessed knowledge). Respondents who are supportive of AVs tended to have more objective AV knowledge (objective knowledge about AVs). Moreover, the results of further experiments show that increasing people's self-assessed knowledge or gaining additional objective AV knowledge may contribute to increasing their AV support. These findings therefore improve the understanding of the relationship between public knowledge levels and AV support, enabling policy-makers to develop better strategies for raising AV support, specifically, by considering the role of knowledge, which in turn may influence public behavioural intentions and lead to higher levels of AV acceptance.

Lateral Evasive Maneuver with Shared Control Algorithm: A Simulator Study

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle's lane. The study's goal is to assess the algorithm's performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver's sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles.

Existence of connected and autonomous vehicles in mixed traffic: Impacts on safety and environment

OBJECTIVES

With the growing market penetration of connected and autonomous vehicles (CAVs), the interaction between conventional human-driven vehicles (HDVs) and CAVs will be inevitable. However, the effects of CAVs in mixed traffic streams have not been extensively studied in China. This study aims to quantify the changes in driving characteristics of an HDV while following a CAV compared to following another HDV and investigate the corresponding impact on traffic safety and the environment caused by these changes.

METHODS

Firstly, two scenarios were built on a driving simulation platform. In scenario 1, the driver follows a vehicle programmed to execute the speed profile of the HDV obtained from the Shanghai Naturalistic Driving Study (SH-NDS) project. In scenario 2, the driver follows a vehicle whose speed profile is calibrated according to the Cooperative Adaptive Cruise Control (CACC) follow-along theory. Secondly, the speed, acceleration, and headway of 30 individuals in each following scenario were analyzed. Speed and acceleration volatility (standard deviation, deviation rate) and time-to-collision (TTC) were selected as indexes to assess the safety impact. The emission and fuel consumption models were used to determine the environmental impact after being localized by the parameters.

RESULTS

HDVs following CAVs exhibit less driving volatility in speed and acceleration, show remarkable improvements in TTC, consume less fuel, and produce fewer emissions on average.

CONCLUSIONS

By introducing CAVs into the road traffic system, traffic operation safety and environmental quality will be improved, with a more stable flow status, lower collision risk, and less air pollution.

Identifying interaction types and functionality for automated vehicle virtual assistants: An exploratory study using speech acts cluster analysis

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to communicate situation information to effectively support the transfer of control and responsibility of the driving task. This study explores 'interaction types' used for this complex human-machine transaction, by analysing how situation information is conveyed and reciprocated during a transfer of control scenario. Two human drivers alternated control in a bespoke, dual controlled driving simulator with the transfer of control being entirely reliant on verbal communication. Handover dialogues were coded based on speech-act classifications, and a cluster analysis was conducted. Four interaction types were identified for both virtual assistants (i.e., agent handing over control) - Supervisor, Information Desk, Interrogator and Converser, and drivers (i.e., agent taking control) - Coordinator, Perceiver, Inquirer and Silent Receiver. Each interaction type provides a framework of characteristics that can be used to define driver requirements and implemented in the design of future virtual assistants to support the driver in maintaining and rebuilding timely situation awareness, whilst ensuring a positive user experience. This study also provides additional insight into the role of dialogue turns and takeover time and provides recommendations for future virtual assistant designs in AVs.

Sleep quality and comfort in fully automated vehicles: A comparison of two seat configurations

As autonomous driving technology advances, the possibility of using vehicles as sleeping environments becomes increasingly relevant. To investigate the feasibility of this concept, a sleep study was conducted with twelve participants who were given a 4-h opportunity window to sleep in both reclined and flat seat configurations. The evaluation involved both objective measures, including polysomnographic (PSG) data analysis, and subjective measures through questionnaires, assessing sleep quality and comfort. While the sleep quantity results were comparable between the two sleeping positions, the reclined position showed a slight advantage in sleep quantity (TST and WASO). Interestingly, a trend highlighting a possible difference was found between the seat positions regarding non-rapid eye movement stage 3 (NREM 3). NREM 3 tended to be in a higher proportion of total sleep time in the flat seat position. Sleep onset latency (SOL) also showed a trend of a shorter latency by participants in the flat position. Additionally, most participants reported a preference for the flat position over the reclined position. These findings suggest that a flat seat configuration could offer a more comfortable and restful sleep environment for passengers in autonomous vehicles.

A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning

Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and model environmental vehicles with predefined trajectories, which ignore the time-sequential interactions between the ego vehicle and environmental vehicles. In this paper, we propose a dynamic test scenario generation method to evaluate autonomous vehicles by modeling environmental vehicles as agents with human behavior and simulating the interaction process between the autonomous vehicle and environmental vehicles. Considering the multimodal features of traffic scenarios, we cluster the real-word traffic environments, and integrate the scenario class labels into the conditional generative adversarial imitation learning (CGAIL) model to generate different types of traffic scenarios. The proposed method is validated in a typical lane-change scenario that involves frequent interactions between ego vehicle and environmental vehicles. Results show that the proposed method further test autonomous vehicles' ability to cope with dynamic scenarios, and can be used to infer the weaknesses of the tested vehicles.

Get Ready for Take-Overs: Using Head-Up Display for Drivers to Engage in Non-Driving-Related Tasks in Automated Vehicles

OBJECTIVE

The study aims to investigate the potential of using HUD (head-up display) as an approach for drivers to engage in non-driving-related tasks (NDRTs) during automated driving, and examine the impacts on driver state and take-over performance in comparison to the traditional mobile phone.

BACKGROUND

Advances in automated vehicle technology have the potential to relieve drivers from driving tasks so that they can engage in NDRTs freely. However, drivers will still need to take-over control under certain circumstances.

METHOD

A driving simulation experiment was conducted using an Advanced Driving Simulator and real-world driving videos. Forty-six participants completed three drives in three display conditions, respectively (HUD, mobile phone and baseline without NDRT). The HUD was integrated with the vehicle in displaying NDRTs while the mobile phone was not. Drivers' visual (e.g. gaze, blink) and physiological (e.g. ECG, EDA) data were collected to measure driver state. Two take-over reaction times (hand and foot) were used to measure take-over performance.

RESULTS

The HUD significantly shortened the take-over reaction times compared to the mobile phone condition. Compared to the baseline condition, drivers in the HUD condition also experienced lower cognitive workload and physiological arousal. Drivers' take-over reaction times were significantly correlated with their visual and electrodermal activities during automated driving prior to the take-over request.

CONCLUSION

HUDs can improve driver performance and lower workload when used as an NDRT interface.

APPLICATION

The study sheds light on a promising approach for drivers to engage in NDRTs in future AVs.

A case study of unavoidable accidents of autonomous vehicles

Objective: Autonomous driving technology eliminates human errors, and thus it is a promising solution for reducing road traffic fatalities and injuries. While future autonomous driving technology may be able to reduce the number of collision accidents, it will not be able to avoid all collision accidents. This study is aimed to demonstrate why some accidents will still be unavoidable even with advanced perceiving and controlling capabilities.Methods: Because fully autonomous vehicles are currently in the laboratory stage, we used the prospective method to study the unavoidable accident of autonomous vehicles. Suitable traffic accident cases were screened from the China In-Depth Accident Study (CIDAS). Videos of the accidents were analyzed and the accidents were reconstructed using PC-Crash software. We assumed that target vehicle possesses near-perfect autonomous driving capabilities. Unavoidable accidents were determined based on vehicle dynamics and traffic constraints. The time from perceiving hazard to collision was calculated for each accident.Results: Among the 112 accidents screened, 15 cases of unavoidable accidents were identified. Three typical cases are presented in detail in this study. Based on the reasons why the target vehicles cannot avoid the collisions, we classified the unavoidable accidents into time-limit type and space-limit type. Time-limit means that vehicle cannot stop or steer out of danger in time, and space-limit means that the traffic environment does not have sufficient space for vehicle to avoid collision.Conclusions: Collision accidents will still occur even with perfect autonomous driving technology. We used the prospective method to investigate scenarios and characteristics of unavoidable accidents of autonomous vehicles. The time-limit type and the space-limit type were identified as two categories of unavoidable accidents. For the time-limit unavoidable accidents, the time from perceiving hazard to collision is typically not longer than 1.5s. The characteristics of unavoidable accidents and the estimated pre-crash warning time can provide some reference for establishing future occupant protection strategies. This study also showed the limitations of active safety and the necessity of passive safety.

Subjective risk and associated electrodermal activity of a self-driving car passenger in an urban shared space

Shared spaces are urban areas without physical separation between motorised and non-motorised users. Previous research has suggested that it is difficult for users to appropriate these spaces and that the advent of self-driving cars could further complicate interactions. It is therefore important to study the perception of these spaces from the users' perspectives to determine which conditions may promote their acceptance of the vehicles. This study investigates the perceived collision risk of a self-driving car's passenger when pedestrians cross the vehicle's path. The experiment was conducted with a driving simulator. Seven factors were manipulated to vary the dynamics of the crossing situations in order to analyse their influence on the passenger's perception of collision risk. Two measures of perceived risk were obtained. A continuous subjective assessment, reflecting an explicit risk evaluation, was reported in real time by participants. On the other hand, their skin conductance responses, which reflects implicit information processing, were recorded. The relationship between the factors and the risk perception indicators was studied using Bayesian networks. The best Bayesian networks demonstrate that subjective collision risk assessments are primarily influenced by the factors that determine the relative positions of the vehicle and the pedestrian as well as the distance between them when they are in close proximity. The analysis further reveals that variations in skin conductance response indicators are more likely to be explained by variations in subjective assessments than by variations in the manipulated factors. These findings could benefit the development of self-driving navigation among pedestrians by improving understanding of the factors that influence passengers' feelings.

CAV-enabled data analytics for enhancing adaptive signal control safety environment

Given the connected and autonomous vehicle (CAV) generated trajectories as a "floating sensor" data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision.

Stated preference analysis of autonomous vehicle among bicyclists and pedestrians in Pittsburgh using Bayesian Networks

Presently, technology innovations are disrupting the status quo and changing the way people travel. In an effort to enhance safety, ease driving tasks, and attract car buyers, automobile manufacturers are offering new vehicle automation technologies. As these vehicle technologies become more automated, navigation around and interactions with pedestrians and bicyclists in complex travel environments becomes more challenging. With people being less predictable and less identifiable than other machines, these technologies can pose safety concerns for all users. In light of this, there is a need to further study the interaction between cyclists, pedestrians, and automated vehicles. In 2019, Bike Pittsburgh (BikePGH) conducted a survey of autonomous vehicles (AVs) in Pittsburgh, Pennsylvania to understand the perception of bicyclists and pedestrians when sharing the road with AVs. This study used the data collected by BikePGH to understand various factors associated with bicyclists' and pedestrians' perception of safety when sharing the road with AVs. Bayesian Networks (BNs) were used to learn the probabilistic interrelationship among AVs' aspects. BN results revealed that familiarity with the technology behind AVs, feeling safe while sharing the road with AVs, and using Pittsburgh's public streets as a proving ground for AVs were associated with higher likelihood of AVs' safety potential to reduce traffic injuries and fatalities. On the other hand, feeling safe while sharing the road with human-driven cars was associated with lower likelihood of AVs' safety potential to reduce traffic injuries and fatalities. Furthermore, the BN model predicted that the experience of sharing the road with AVs while riding a bicycle or walking, familiarity with the technology behind AVs, and using Pittsburgh's public streets as a proving ground for AVs were associated with higher likelihood of feeling safe sharing the road with AVs. The joint analysis of the variable showed the highest predicted probabilities of 95% and 86%, respectively for AVs' potential to reduce traffic injuries and fatalities and for feeling safe sharing the road with AVs. The practical application of this study is presented along with recommendations to operators, city engineers, and planner.

Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis

In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers' physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.

Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model

The severity of vehicle-pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle-pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB's Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle-pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle-pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.

A novel autonomous vehicle interface for older adults with cognitive impairment

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs' usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

The 'invisible gorilla' during pedestrian-AV interaction: Effects of secondary tasks on pedestrians' reaction to eHMIs

In road traffic, mental overload often leads to a failure to notice new and distinctive stimuli. Such phenomenon is known as 'inattentional blindness'. Safe and efficient interaction between automated vehicles (AVs) and pedestrians is expected to rely heavily on external human-machine interfaces (eHMIs), a tool AVs are equipped with to communicate their intentions to pedestrians. This study seeks to explore the phenomenon of 'inattentional blindness' in the context of pedestrian-AV interactions. Specifically, the aim is to understand the effects of a warning eHMI on pedestrians' crossing decisions when they are engaged in a secondary task. In an experiment study with videos of pedestrian crossing scenarios filmed from the perspective of the crossing pedestrian, participants had to decide the latest point at which they would be willing to cross the road in front of an AV with an eHMI vs. an AV without an eHMI. Participants were also asked to predict the future behavior of the AV. 125 female and 9 male participants aged between 18 and 25 completed the experiment and a follow-up questionnaire. It was found that the presence of a warning eHMI on AVs contributes to a clearer understanding of pedestrians' inferences about the intention of AVs and helps deter late and dangerous crossing decisions made by pedestrians. However, the eHMI fail to help pedestrians avoid such decisions when they face a high mental workload induced by secondary task engagement.

Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks

5G wireless networks are paying increasing attention to Vehicle to Everything (V2X) communications as the number of autonomous vehicles rises. In V2X applications, a number of demanding criteria such as latency, stability, and resource availability have emerged. Due to limited licensed radio resources in 5G cellular networks, Cellular V2X (C-V2X) faces challenges in serving a large number of cars and managing their network access. A reason is the unbalanced load of serving Base Stations (BSs) that makes it difficult to manage the resources of the BSs optimally regarding the frequency reuse in cells and its subsequent co-channel interference. It is while the routing protocols could help redirect the load of loaded BSs to neighboring ones. In this article, we propose a resource-aware routing protocol to mitigate this challenge. In this regard, a hybrid C-V2X/ Dedicated Short Range Communication (DSRC) vehicular network is considered. We employ cluster-based routing that enables many cars to interface with the network via some Cluster Heads (CH) using DSRC resources while the CHs send their traffic across C-V2X links to the BSs. Traditional cluster-based routings do not attend the resource availability in BSs that are supporting the clusters. Thus, our study describes an enhanced clustering method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that re-clusters the vehicles based on the resource availability of BSs. Simulation results show that the proposed re-clustering method improves the spectrum efficiency by at least 79%, packet delivery ratio by at least 5%, and load balance of BSs by at least 90% compared to the baseline.

Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery

The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options. Older individuals and those with concerns about package handling exhibit a lower preference for automated modes, while individuals with higher education and technology affinity exhibit greater acceptance. These findings provide valuable insights for logistics companies and retailers looking to introduce automation technologies in their last-mile delivery operations, emphasizing the need to tailor marketing and communication strategies to meet customer preferences. Additionally, providing information about appropriate package handling by automated technologies may alleviate concerns and increase the acceptance of these modes among all customer groups.

Effect of hip flexion angle on lower limb injuries of occupants in autonomous vehicle crashes

This study aims to determine the influence of the hip flexion angle on the injury trends of lower limbs. An impact model was established using a hybrid human body model and an accurate vehicle model. Simulations were performed in two boundary environments of 25 and 40% overlap impacts under different hip flexion angles. The analysis of the dynamic responses indicated that the hip flexion angle significantly affected the injury trends. The maximum femur index of different overlaps was all found at the minimum hip angle, except for the left femur at 25% overlap rate. Meanwhile, the maximum acetabular stress was all found at the minimum hip angle (approximately 0.09-0.20 GPa). This study provides mechanistic insights into the lower limb injuries associated with complex human postures.

GNSS performance enhancement using measurement estimation in harsh environment

Global navigation satellite systems (GNSSs) are commonly used to measure the position and time globally. A GNSS is convenient owing to its ability to measure accurate position relatively without using assistive tools for navigation by comparing with other sensors. Based on these benefits, the applicable area is expanding to commercial and social uses (e.g., vehicle navigation, smart grids, and smartphone apps). In the future, various services and technologies (e.g., the use of autonomous vehicles, unmanned delivery, and industrial field robots), which make Internet of Things (IOT) more active, will be used in our society. Conversely, the performance of GNSS can degrade in harsh environments, such as urban areas, owing to the property of GNSS, which calculates position and time via satellite signal reception. However, buildings in a city can block navigation satellite signals and generate multi-path errors. The blocked signals exacerbate the dilution of precision (DOP), which indicates the accuracy of the navigation solution and increases the navigation solution error. This study proposes methods to improve navigation performance by leveraging various techniques (e.g., range differences, receiver clock error hold, and virtual satellites). The methods were validated in harsh environments where visible satellites were reduced. In the simulation, each proposed method improved the navigation performance by creating an environment similar to a normal situation, despite the receiver entering a harsh environment. The results confirmed that the navigation performance deteriorated compared to the normal situation where the number of visible satellites decreased. However, the navigation performance was recovered gradually by applying the proposed techniques. Using the proposed methods, navigation performance can be maintained continuously even in situations where satellite signals are blocked.

Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS

For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain-computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers' mental activities in low-risk episode and high-risk episode were compared, the influences on passengers' mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers' driving risk cognition.

Regulation of multi-area power system load frequency in presence of V2G scheme

The integration of renewable sources (RSs) and the widespread deployment of electric vehicles (EVs) has transitioned from a luxury to a necessity in modern power systems. This results from the sharp increase in electric power demand and public awareness of switching to green energy. However, in addition to load fluctuations and changes in system parameters, these RSs and EVs negatively impact the load frequency (LF). This work presents a LF control for a modern multi-area power system incorporating photovoltaic (PV) and EV chargers. The proposed controller primarily utilizes EV chargers within modern power systems. This approach offers the advantage of using the already present components instead of introducing new ones. The proposed controller comprises the ecological optimization approach (ECO) and the integral controller (I). Both of these components are designed for autonomous vehicle-to-grid (V2G) devices. The proposed control technique is applied to a three-area power system, where the V2G scheme is located in Area-1. Variations in the load, PV power generated, and system parameters are considered to evaluate the effectiveness of the proposed (I+ECO+V2G) controller for controlling the LF. To assess the performance of the proposed I+ECO+V2G system, a comparative analysis is conducted to compare its performance with both the I+ECO system and the standard I-controller. The simulation findings demonstrate that implementing the I+ECO and the proposed I+ECO+V2G strategies results in enhanced system stability and decreased LF fluctuations compared to the conventional I-control approach. Furthermore, while comparing the I+ECO control technique to the suggested control strategy I+ECO+V2G, it was seen that the latter reaches steady state values more quickly. The results validate the robustness and effectiveness of the proposed controller in mitigating the impacts of load disturbances, uncertainties, and nonlinearities within the system. These simulations were performed using MATLAB/SIMULINK. To validate the outcomes of the simulation results, an experimental setup consisting of a real-time dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card was used. The experimental findings have substantiated the accuracy of the simulation findings about the superiority of the I+ECO+V2G methodology compared to both the I+ECO and I-control methodologies concerning system performance and LF control.

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.

Comparison of the effectiveness of rotating seats in autonomous vehicles and airbags in traditional vehicles to minimize head injuries in frontal crash

We analyzed and compared the effectiveness of the rotating seat of autonomous vehicles and that of an airbag in traditional vehicles as head protection measures in a frontal crash. Driver frontal crash models of traditional and autonomous vehicles and a head finite element model were established. Four evaluation indexes were used for comparison. The airbag proved more effective than seat rotation in terms of injury criteria and brain tissue injury risk under frontal crash conditions with and without brake involvement. A segmented protection measure based on crash acceleration values is proposed to improve crash safety in future autonomous vehicles.