Automated Vehicles and Pedestrian Safety: Exploring the Promise and Limits of Pedestrian Detection

A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X)

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

A real-time vehicle safety system by concurrent object detection and head pose estimation via stereo vision

A considerable number of vehicular accidents occur in low-millage zones like school streets, neighborhoods, and parking lots, among others. Therefore, the proposed work aims to provide a novel ADAS system to warn about dangerous scenarios by analyzing the driver's attention and the corresponding distances between the vehicle and the detected object on the road. This approach is made possible by concurrent Head Pose Estimation (HPE) and Object/Pedestrian Detection. Both approaches have shown independently their viable application in the automotive industry to decrease the number of vehicle collisions. The proposed system takes advantage of stereo vision characteristics for HPE by enabling the computation of the Euler Angles with a low average error for classifying the driver's attention on the road using neural networks. For Object Detection, stereo vision is used to detect the distance between the vehicle and the approaching object; this is made with a state-of-the-art algorithm known as YOLO-R and a fast template matching technique known as SoRA that provides lower processing times. The result is an ADAS system designed to ensure adequate braking time, considering the driver's attention on the road and the distances to objects.

Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs.

Control of Haemorrhage in Orthopaedic Trauma

This paper aims to outline current practices and examine promising new advancements in the modern management of haemorrhage in orthopaedic trauma. Many prehospital and perioperative haemorrhage control strategies and techniques have been available to clinicians for multiple decades, yet our understanding and utilisation of these practices continues to be refined and optimised. There is a particular focus in this article on issues related to resuscitation and coagulation in trauma. We examine the complex mechanisms that lead to coagulopathy in trauma patients as well as the transformative effect tranexamic acid has had in limiting blood loss. We also explore some emerging technologies such as endovascular interventions and clot-stabilising dressings and devices that are likely to have a significant impact going forward.

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.

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.

Pediatric emergency department visits for pedestrian injuries in relation to the enactment of Complete Streets policy

Introduction

This study aimed to evaluate the rate of pediatric emergency department (ED) visits for pedestrian injuries in relation to the enactment of the Complete Streets policy.

Methods

The National Complete Streets policies were codified by county and associated with each hospital's catchment area and date of enactment. Pedestrian injury-related ED visits were identified across 40 children's hospitals within the Pediatric Health Information System (PHIS) from 2004 to 2014. We calculated the proportion of the PHIS hospitals' catchment areas covered by any county policy. We used a generalized linear model to assess the impact of the proportion of the policy coverage on the rate of pedestrian injury-related ED visits.

Results

The proportion of the population covered by Complete Streets policies increased by 23.9%, and pedestrian injury rates at PHIS hospitals decreased by 29.8% during the study period. After controlling for years, pediatric ED visits for pedestrian injuries did not change with increases in the PHIS catchment population with enacted Complete Streets policies.

Conclusion

After accounting for time trends, Complete Streets policy enactment was not related to observed changes in ED visits for pedestrian injuries at PHIS hospitals.

External Human-Machine Interfaces for Automated Vehicles in Shared Spaces: A Review of the Human-Computer Interaction Literature

Given the rise of automated vehicles from an engineering and technical perspective, there has been increased research interest concerning the Human and Computer Interactions (HCI) between vulnerable road users (VRUs, such as cyclists and pedestrians) and automated vehicles. As with all HCI challenges, clear communication and a common understanding-in this application of shared road usage-is critical in order to reduce conflicts and crashes between the VRUs and automated vehicles. In an effort to solve this communication challenge, various external human-machine interface (eHMI) solutions have been developed and tested across the world. This paper presents a timely critical review of the literature on the communication between automated vehicles and VRUs in shared spaces. Recent developments will be explored and studies analyzing their effectiveness will be presented, including the innovative use of Virtual Reality (VR) for user assessments. This paper provides insight into several gaps in the eHMI literature and directions for future research, including the need to further research eHMI effects on cyclists, investigate the negative effects of eHMIs, and address the technical challenges of eHMI implementation. Furthermore, it has been underlined that there is a lack of research into the use of eHMIs in shared spaces, where the communication and interaction needs differ from conventional roads.

Safety evaluation method in multi-logical scenarios for automated vehicles based on naturalistic driving trajectory

Automated driving technology has constantly been maturing; however, how to ensure automated vehicle (AV) safety has not yet been effectively solved, functional safety assessment remains an important part of the development of automated driving technology. To compensate for the lack of multidimensional evaluation indicators, this paper proposes a safety evaluation method in multi-logical scenarios (SEMMS) for AVs' functional safety based on naturalistic driving trajectory (NDT) in order to evaluate the comprehensive performance of the tested AV in a diversity of scenarios simultaneously. The potential field method is used to describe the quantified danger level of an AV in a single concrete scenario that considers the dangerous situation of the scenario and AV test results. Combined with the internal probability distribution of the logical scenario parameter space obtained by NDT, the safety performance of an AV in logical scenario is calculated by integrating the two indexes. With the information entropy and relative frequency of different logical scenarios, the relative weights of logical scenarios are obtained, and the safety performance evaluation results of the tested AV in the multi-logical scenarios can be determined based on the weighting danger level in different logical scenarios. During the actual application of the method, the HighD database was used as the input source of NDT, and a black-box automated driving algorithm was subjected to traversal tests in three logical scenarios. The test results of the automated driving algorithm were evaluated using the SEMMS, and the results show that the SEMMS could well evaluate the performance of the tested automated driving algorithm in multiple kinds of logical scenarios simultaneously, indicating that it is an effective solution to the problem of automated driving algorithm safety evaluation.

Senior Americans' perceptions, attitudes, and safety concerns toward Autonomous Vehicles (AVs)

INTRODUCTION

Autonomous vehicles (AVs) are considered a promising solution to improve seniors' safety and mobility. However, to transition to fully automated transportation, especially among seniors, it is vital to assess their perception and attitude toward AVs. This paper investigates seniors' perceptions and attitudes to a wide range of AV options from the perspective of pedestrians and users in general, as well as during and after the COVID-19 pandemic. Underlying this objective is to examine older pedestrians' safety perceptions and behaviors at crosswalks in the presence of AVs.

METHOD

A national survey collected data from a sample of 1,000 senior Americans. Using Principal Component Analysis (PCA) and Cluster Analysis, three clusters of seniors were identified with different demographic characteristics, perceptions, and attitudes toward AVs.

RESULTS

PCA findings revealed that "risky pedestrian crossing behavior," "cautious pedestrian crossing behavior in the presence of AVs," "positive perception and attitude toward shared AVs," and "demographic characteristics" were the main components explaining most of the variation within the data, respectively. The PCA factor scores were used in the cluster analysis, which resulted in the identification of three distinctive groups of seniors. Cluster one included individuals with lower demographic scores and a negative perception and attitude toward AVs from the perspective of users and pedestrians. Clusters two and three included individuals with higher demographic scores. Cluster two included individuals with a positive perception toward shared AVs from the user perspective, but a negative attitude toward pedestrian-AV interaction. Cluster three included those with a negative perception toward shared AVs but a somewhat positive attitude toward pedestrian-AV interaction. The findings of this study provide valuable insights to transportation authorities, AV manufacturers, and researchers regarding older American's perception and attitude toward AVs as well as their willingness to pay and use Advanced Vehicle Technologies.

A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences

INTRODUCTION

Automated vehicle (AV) technology is a promising technology for improving the efficiency of traffic operations and reducing emissions. This technology has the potential to eliminate human error and significantly improve highway safety. However, little is known about AV safety issues due to limited crash data and relatively fewer AVs on the roadways. This study provides a comparative analysis between AVs and conventional vehicles on the factors leading to different types of collisions.

METHOD

A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. Four years (2017-2020) of AV and conventional vehicle crash data on California roads were used. The AV crash dataset was acquired from the California Department of Motor Vehicles, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. A buffer of 50 feet was used to associate each AV crash and conventional vehicle crash; a total of 127 AV crashes and 865 conventional vehicle crashes were used for analysis.

RESULTS

Our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. Further, AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles. The variables associated with the increased likelihood of rear-end collisions for AVs include signalized intersections and lanes with less than 45 mph speed limit.

CONCLUSIONS

Although AVs are found to improve safety on the road in most types of collisions by limiting human error leading to vehicle crashes, the current state of the technology shows that safety aspects still need improvement.

Personal ethical settings for driverless cars and the utility paradox: An ethical analysis of public attitudes in UK and Japan

Driverless cars are predicted to dramatically reduce collisions and casualties on the roads. However, there has been controversy about how they should be programmed to respond in the event of an unavoidable collision. Should they aim to save the most lives, prioritise the lives of pedestrians, or occupants of the vehicle? Some have argued that driverless cars should all be programmed to minimise total casualties. While this would appear to have wide international public support, previous work has also suggested regional variation and public reluctance to purchase driverless cars with such a mandated ethical setting. The possibility that algorithms designed to minimise collision fatalities would lead to reduced consumer uptake of driverless cars and thereby to higher overall road deaths, represents a potential "utility paradox". To investigate this paradox further, we examined the views of the general public about driverless cars in two online surveys in the UK and Japan, examining the influence of choice of a "personal ethical setting" as well as of framing on hypothetical purchase decisions. The personal ethical setting would allow respondents to choose between a programme which would save the most lives, save occupants or save pedestrians. We found striking differences between UK and Japanese respondents. While a majority of UK respondents wished to buy driverless cars that prioritise the most lives or their family members' lives, Japanese survey participants preferred to save pedestrians. We observed reduced willingness to purchase driverless cars with a mandated ethical setting (compared to offering choice) in both countries. It appears that the public values relevant to programming of driverless cars differ between UK and Japan. The highest uptake of driverless cars in both countries can be achieved by providing a personal ethical setting. Since uptake of driverless cars (rather than specific algorithm used) is potentially the biggest factor in reducing in traffic related accidents, providing some choice of ethical settings may be optimal for driverless cars according to a range of plausible ethical theories.

Road-safety-II: Opportunities and barriers for an enhanced road safety vision

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of "successful" interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's "safety pyramid". Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches.

How effective are pedestrian crash prevention systems in improving pedestrian safety? Harnessing large-scale experimental data

Over the past few years, the number of fatalities and severe injuries of vulnerable road users, particularly pedestrians, has risen substantially. Clearly, the safe mobility of pedestrians is critical in our transportation system. Technology can help reduce vehicle-pedestrian crashes, fatalities, and injuries. Emerging technologies such as pedestrian crash prevention (PCP) systems utilized in on-road vehicles have the potential to mitigate pedestrian crash severity or prevent crashes. However, the reliability and effectiveness of these technologies have remained uncertain. This study contributes toward understanding the effectiveness of PCP systems utilized in on-road vehicles with a low level of automation by investigating two crossing and one longitudinal scenarios. The Insurance Institute for Highway Safety field test data from 2018 to 2021 is harnessed, where several on-road vehicles and their PCP systems are evaluated in terms of safety. The large-scale experimental dataset is comprised of 3095 tests of 91 vehicles with different sizes, makes, and models. The empirical results indicate that in hazardous pedestrian-vehicle conflict situations, the performance of PCP systems has been improved during recent years. The test data shows that some pedestrians were undetected in some tests, but on average, in 70% of the tests, the PCP systems avoided pedestrian crashes. However, for the occurred crashes, PCP systems, on average, were able to mitigate impact speeds of >50%. In real-life situations, this could translate to substantial reductions in injury and fatality risk. Through rigorous analysis, the associations of key factors in the studied scenarios and the performance of PCP systems are explored and discussed in this paper. The modeling results show that increasing the maximum deceleration rate of the PCP system and lower weight of vehicles can significantly improve the performance of the PCP system by decreasing the speed at impact with pedestrians. The average maximum deceleration utilized in PCP systems has been increased over time from 7.48 m/s² in 2018 to 9.36 m/s² in 2021. This can be one of the reasons behind the improvement of PCP systems during recent years.

Analyzing Pedestrian Behavior at Unsignalized Crosswalks from the Drivers’ Perspective: A Qualitative Study

This study investigated drivers’ perceptions of pedestrian crossing behavior at unsignalized crosswalks, which was less fruitful in quantitative and qualitative traffic research. Subjective and snow-ball sampling were used to conduct semi-structured in-depth interviews based on drivers’ daily driving experience from qualitative research. A theoretical model of pedestrian behavior at unsignalized crosswalks was constructed using the grounded theory and the theoretical saturation test. The model involved 4 three-level codes and 13 two-level codes (main category) used to obtain seven subcategories. The results show that drivers believe that pedestrian characteristics, driver characteristics, and age factors are the three factors that affect pedestrian crossing safety. Targeted improvement measures are put forward to guide the design of pedestrian crossing facilities, pedestrian management and guidance, and future research on conflicts between autonomous vehicles and pedestrians.

A Review of Vehicle-to-Vulnerable Road User Collisions on Limited-Access Highways to Support the Development of Automated Vehicle Safety Assessments

This study aims to provide evidence to support the development of automated vehicle (AV) safety assessments that consider the possible presence of non-motorized vulnerable road-users (VRUs) on limited-access highways. Although limited-access highways are designed to accommodate high-speed motor vehicles, collisions involving VRUs on such roadways are frequently reported. A narrative review is conducted, covering the epidemiology of VRUs crashes on limited-access highways to identify typical crash patterns considering collisions severity and the underlying reasons for the VRUs to use the highway. The review results show that occupants alighting from a disabled or crashed vehicle, people seeking help or helping others, highway maintenance zones, police stops, and people crossing a highway should be given priority to ensure VRU safety on limited-access highways. The results are summarized in figures with schematic models to generate test scenarios for AV safety assessment. Additionally, the results are discussed using two examples of traffic situations relevant to the potential AV-VRU crashes on highways and the current performance of autonomous emergency braking and autonomous emergency steering systems. These findings have important implications for producing scenarios in which AV may not produce crashes lest it performs worse than human drivers in the proposed scenarios.

Mining patterns of autonomous vehicle crashes involving vulnerable road users to understand the associated factors

Autonomous or automated vehicles (AVs) have the potential to improve traffic safety by eliminating majority of human errors. As the interest in AV deployment increases, there is an increasing need to assess and understand the expected implications of AVs on traffic safety. Until recently, most of the literature has been based on either survey questionnaires, simulation analysis, virtual reality, or simulation to assess the safety benefits of AVs. Although few studies have used AV crash data, vulnerable road users (VRUs) have not been a topic of interest. Therefore, this study uses crash narratives from four-year (2017-2020) of AV crash data collected from California to explore the direct and indirect involvement of VRUs. The study applied text network and compared the text classification performance of four classifiers - Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), and Neural Network (NN) and associated performance metrics to attain the objective. It was found that out of 252 crashes, VRUs were, directly and indirectly, involved in 23 and 12 crashes, respectively. Among VRUs, bicyclists and scooterists are more likely to be involved in the AV crashes directly, and bicyclists are likely to be at fault, while pedestrians appear more in the indirectly involvements. Further, crashes that involve VRUs indirectly are likely to occur when the AVs are in autonomous mode and are slightly involved minor damages on the rear bumper than the ones that directly involve VRUs. Additionally, feature importance from the best performing classifiers (RF and NN) revealed that crosswalks, intersections, traffic signals, movements of AVs (turning, slowing down, stopping) are the key predictors of the VRUs-AV related crashes. These findings can be helpful to AV operators and city planners.

Differential benefit of sensor system field-of-view and range in pedestrian automated emergency braking systems

OBJECTIVE

Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a combination of radar and cameras to detect pedestrians and automatically apply braking to prevent or mitigate an impending collision. However, these current sensor systems might have a restricted field-of-view (FOV) which may not detect all pedestrians. Advanced sensors like LiDAR can have a wider FOV that may substantially help improve detection. The objective of this study was to determine the influence of FOV and range on the effectiveness of P-AEB systems to determine the potential benefit of advanced sensors.

METHODS

This study utilized vehicle-pedestrian crashes from the Pedestrian Crash Data Study (PCDS) to calculate pre-crash pedestrian and vehicle trajectories. A computational model was then applied to simulate the crash with a hypothetical P-AEB system. The model was designed to be able to vary the system's field-of-view (FOV), range, time-to-collision of activation, and system latency. In this study we estimated how the FOV and range of advanced sensors could affect P-AEB system effectiveness at avoiding crashes and reducing impact speed. Sensor range was varied from 25 - 100 m and sensor FOV was varied from ±10° to ±90°.

RESULTS

Sensors simulated with a range of 50 m or greater performed only approximately 1% better than with a 25 m range. Field-of-view had a larger effect on estimated system avoidance capabilities with a ± 10° FOV sensor estimated to avoid 46-47% of collisions compared to 91-92% for a ± 90° FOV sensor. The system was able to avoid a greater percentage of cases in which the vehicle was traveling straight at sensor FOVs of ±30° and below. Among the unavoided crashes with a sensor FOV of ±90°, the average impact velocity using a 100 m range sensor was 7.4 m/s which was 3.1 m/s lower than a 25 m range sensor.

CONCLUSIONS

Sensor ranges above 25 m were not found to significantly affect estimated crash avoidance potential, but had a small effect on impact mitigation. Sensor FOV had a larger effect on crash avoidance up to a FOV of ±60° with little additional benefit at larger FOVs.

Autonomous Vehicles and Vulnerable Road-Users-Important Considerations and Requirements Based on Crash Data from Two Countries

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

Artificial Moral Responsibility: How We Can and Cannot Hold Machines Responsible

Our ability to locate moral responsibility is often thought to be a necessary condition for conducting morally permissible medical practice, engaging in a just war, and other high-stakes endeavors. Yet, with increasing reliance upon artificially intelligent systems, we may be facing a widening responsibility gap, which, some argue, cannot be bridged by traditional concepts of responsibility. How then, if at all, can we make use of crucial emerging technologies? According to Colin Allen and Wendell Wallach, the advent of so-called 'artificial moral agents' (AMAs) is inevitable. Still, this notion may seem to push back the problem, leaving those who have an interest in developing autonomous technology with a dilemma. We may need to scale-back our efforts at deploying AMAs (or at least maintain human oversight); otherwise, we must rapidly and drastically update our moral and legal norms in a way that ensures responsibility for potentially avoidable harms. This paper invokes contemporary accounts of responsibility in order to show how artificially intelligent systems might be held responsible. Although many theorists are concerned enough to develop artificial conceptions of agency or to exploit our present inability to regulate valuable innovations, the proposal here highlights the importance of-and outlines a plausible foundation for-a workable notion of artificial moral responsibility.

Using retro-reflective cloth to enhance drivers' judgment of pedestrian walking direction at night-time

PURPOSE

Fatal pedestrian collisions are over-represented at night and poor conspicuity is believed to be a leading causative factor. Retro-reflective clothing enhances pedestrian conspicuity, particularly when placed in a biological motion or "biomotion" configuration. In this study, we explored how various retro-reflective clothing configurations affected the ability to judge the direction of a pedestrian walking across the road, which has important implications for collision avoidance.

METHODS

Participants included 21 young drivers (mean age 21.6 ± 2.0 years) with normal vision. A closed-road circuit was used to assess the accuracy of drivers' judgement of the direction of walking of a pedestrian at night-time wearing one of five different clothing configurations: four with retro-reflective materials placed in different locations (Biomotion, Legs + Torso, Torso Only, Legs Only), and a control wearing only black clothing (Street). Participants were seated in a stationary vehicle with low beam headlamps, 135 m from a pedestrian, who walked across the road from both sides, in different directions (towards the car, straight across the road, or away from the car). Outcome measures included drivers' response accuracy and confidence ratings for judging pedestrian walking direction.

RESULTS

Accuracy in judging pedestrian walking direction differed significantly across the clothing configurations (p < 0.001). Response accuracy was significantly higher for the Biomotion configuration (80% correct), compared to the other retro-reflective (Legs + Torso 64%; Torso Only 53%; Legs Only 50%) and Street configurations (33%). Similar trends were noted for confidence ratings across the clothing conditions, yet the relationship between confidence ratings and response accuracy within each clothing configurations was poor.

CONCLUSIONS

The use of retro-reflective clothing in a biomotion configuration facilitated the highest accuracy and confidence in drivers' judgment of pedestrian walking direction, compared to other configurations. These findings highlight the importance of using biomotion clothing for pedestrians at night, to not only facilitate drivers' earlier recognition of pedestrians, but also increase their accuracy in determining the walking direction of pedestrians as they cross the road. Practical applications: The use of clothing incorporating retro-reflective material in a biomotion configuration for pedestrians crossing roads at night provides enhanced cues for drivers regarding the presence and walking direction of pedestrians.

Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research

Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study is designed to identify the AV safety quantification studies, evaluate the quantification approaches used in the literature, and uncover the gaps and challenges in AV safety evaluation. We employed a scoping review methodology to identify the approaches used in the literature to quantify AV safety. After screening and reviewing the literature, six approaches were identified: target crash population, traffic simulation, driving simulator, road test data analysis, system failure risk assessment, and safety effectiveness estimation. We ran two evaluations on the identified approaches. First, we investigated each approach in terms of its input (required data, assumptions, etc.), output (safety evaluation metrics), and application (to estimate AVs' safety implications at the vehicle, transportation system, and society levels). Second, we qualitatively compared them in terms of three criteria: availability of input data, suitability for evaluating different automation levels, and reliability of estimations. This review identifies four challenges in AV safety evaluation: (a) shortcomings in AV safety evaluation approaches, (b) uncertainties in AV implementations and their impacts on AV safety, (c) potential riskier behavior of AV passengers as well as other road users, and (d) emerging safety issues related to AV implementations. This review is expected to help researchers and rulemakers to choose the most appropriate quantification method based on their goals and study limitations. Future research is required to address the identified challenges in AV safety evaluation.

Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia

Investigating the connections between pedestrian crashes and various urban variables is critical to ameliorate the prediction of pedestrian fatalities, formulate advisories for the stakeholders, and provide an evidence base for policy change to mitigate the occurrence and intensity of pedestrian fatalities. In this paper, we aim to explore the geographically varying association between the pedestrian fatalities and other associated factors of an urban environment in Jeddah city, which is a car-dependent city in Saudi Arabia. At first, Global Moran’s I and Local Indicators of Spatial Association (LISA) were applied to visualize the clustering of pedestrian fatalities in the various districts of Jeddah. Subsequently, we developed Poisson regression models based on their geographically weighted indicators. Both the global and geographically weighted regression models attempt to assess the association between the pedestrian fatalities and the geographically relevant land use and transport infrastructure factors. The results indicate that geographically weighted Poisson regression (GWPR) performed better than the global Poisson counterparts. It is also revealed that the existing transportation infrastructure in Jeddah was significantly associated with the higher pedestrian fatalities. The results have shown that the proposed model in this study can inform transport policies in Jeddah in prioritizing more safety measures for the pedestrians, including expanding pedestrians’ infrastructure, and cautious monitoring of pedestrian footpaths. It can facilitate the analysis and improvement of road safety for pedestrians in car-dependent cities.

Safe and Ecological Speed Control for Heavy-Duty Vehicles on Long–Steep Downhill and Sharp-Curved Roads

To contribute to the development of sustainable transport that is safe, eco-friendly, and efficient, this research proposed a safe and ecological speed control system for heavy-duty vehicles on long–steep downhill and sharp-curved roads under a partially connected vehicles environment consisting of connected heavy-duty vehicles (CHDVs) and conventional human-driven vehicles. This system prioritizes braking and lateral motion safety before improving fuel efficiency and ensuring traffic mobility at optimal status, and optimizes the speed trajectories of CHDVs to control the entire traffic. Speed optimization is modelled as an optimal control problem and solved by the iterative Pontryagin’s maximum principle algorithm. The simulation-based evaluation shows that the proposed system effectively reduces the peak temperature of the brake drums, the lateral slip angle of the vehicle wheels, and the lateral load transfer rate of the vehicle body; all these measurements of effectiveness are limited to safe ranges. A detailed investigation reveals that the proposed system reduces fuel consumption by up to 15.49% and inhibits the adverse effects on throughput. All benefits increase with the market penetration rate (MPR) of CHDVs and the traffic congestion level and reach significant levels under low MPRs of CHDVs. This indicates that the proposed system has good robustness for the impedance from conventional vehicles and could be implemented in the near future.

The perils of perfect performance; considering the effects of introducing autonomous vehicles on rates of car vs cyclist conflict

How humans will adapt and respond to the introduction of autonomous vehicles (AVs) is uncertain. This study used an agent-based model to explore how AVs, human-operated vehicles, and cyclists might interact based on the introduction of flawlessly performing AVs. Under two separate experimental conditions, results of experiment 1 showed that, despite no conflicts occurring between cyclists and AVs, modelled conflicts among human-operated cars and cyclists increased with the introduction of AVs due to cyclists' adjusted expectations of the behaviour and capability of human-operated and autonomous cars. Similarly, when human-operated cars were replaced with AVs over time in experiment 2, cyclist conflict rates did not follow a linear reduction consistent with the replacement rate but decreased more slowly in the early stages of replacement before 50% substitution. It is concluded that, although flawlessly performing AVs might reduce total conflicts, the introduction of AVs into a transport system where humans adjust to the behaviour and risk presented by AVs could create new sources of error that offset some of AVs assumed safety benefits. Practitioner summary: Ergonomics is an applied science that studies interactions between humans and other elements of a system, including non-human agents. Agent-Based Modelling (ABM) provides an approach for exploring dynamic and emergent interactions between agents. In this article, we demonstrate ABM through an analysis of how cyclists and pedestrians might interact with Autonomous Vehicles (AVs) in future road transport systems. Abbreviations: ABM: agent-based model; AV: autonomous vehicle; ODD; overview, design concepts and details; RW: rescorla-wagner.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

Potential Impact of Autonomous Vehicles on Movement Behavior: A Scoping Review

CONTEXT

This scoping review examines the literature as it relates to autonomous vehicles and impact on movement behavior (i.e., physical activity, sedentary behavior, and sleep) or mode choice (e.g., public transit), beliefs about movement behavior or mode choice, or impact on environments that may influence movement behavior or mode choice.

EVIDENCE ACQUISITION

A search was conducted in June 2018 and updated in August 2019 of numerous databases (e.g., SPORTDiscuss, PubMed, and Scopus) and hand searching using terms such as autonomous cars and walking. Documents were included if they were databased studies, published in English, and related to the research question. They were then coded by 6 reviewers for characteristics of the document, design, sample, autonomous vehicles, movement behavior, and findings. The coding and analysis were conducted between August 2018 and September 2019.

EVIDENCE SYNTHESIS

Of 1,262 possible studies, 192 remained after a title and abstract scan, and 70 were included after a full-article scan. Most of the studies were conducted in Europe (42%) or North America (40%), involved simulation modeling (50%) or cross-sectional (34%) designs, and were published mostly in transportation (83%) journals or reports. Of the 252 findings, 61% related to movement behavior or mode choice. Though the findings were equivocal in some cases, impacts included decreased demand for active transportation, increased demand for autonomous vehicles, increased sitting and sleeping, and reduced walking.

CONCLUSIONS

Though no experimental or longitudinal studies have been published to date, the available research suggests that autonomous vehicles will impact aspects of mode choice and the built environment of people residing in much of the developed world, resulting in reduced walking and more sitting.

Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions

Interaction between drivers and pedestrians enables pedestrians to cross the street without conflicts. When highly automated vehicles (HAVs) become prevalent, interaction will change. Although HAVs manage to identify pedestrians, they may not be able to assess pedestrians’ intentions. This study discusses two different ambitions: Prioritizing pedestrian safety and prioritizing efficient traffic flow; and how these two affect the possibilities to avoid fatal crashes between pedestrians and passenger cars. HAVs’ hypothetical possibilities to avoid different crash scenarios are evaluated based on 40 in-depth investigated fatal pedestrian crashes, which occurred with manually-driven cars in Finland in 2014–2016. When HAVs prioritize pedestrian safety, they decrease speed near pedestrians as a precaution which affects traffic flow due to frequent decelerations. When HAVs prioritize efficient traffic flow, they only decelerate, when pedestrians are in a collision course. The study shows that neither of these approaches can be applied in all traffic environments, and all of the studied crashes would not likely be avoidable with HAVs even when prioritizing pedestrian safety. The high expectations of HAVs’ safety benefits may not be realized, and in addition to safety and traffic flow, there are many other objectives in traffic which need to be considered.

The epidemiology of pedestrian fatalities and substance use in Georgia, United States, 2007-2016

Though U.S. motor vehicle crashes as a whole have decreased over the past few years, fatalities among vulnerable road users have increased. Pedestrian deaths rose nationally by 27% between 2007 and 2016 accounting for 16% of all motor vehicle fatalities. This increase continues to burden transportation specialists, public health professionals, and community stakeholders. Potential risk factors include characteristics of the built environment, distractions, and pedestrians' use of alcohol and drugs. Pedestrian deaths in Georgia, United States, increased 40% between 2014 and 2016 while drug overdose deaths have increased by 18% during the same period. Concurrent increases in mortality due to pedestrian fatalities and drug overdoses make Georgia a natural environment in which to describe the proximity of drugs among pedestrian fatalities, a topic largely overlooked by the literature. This study explores the epidemiology of pedestrian fatalities in Georgia over a 10-year period with an emphasis on reported substance use among cases. The study employed 10-year data from the Fatality Analysis Reporting System (FARS) administered by the National Highway Traffic Safety Administration. Descriptive methods were used to explore drug screens by person, place, and time. We also examined trends in total drug screens over the examination period. Between 2007 and 2016, 1781 pedestrian crashes were reported to FARS; the fatality rate for this period was 94.5%. Of these, most were male with Blacks and Whites equally represented. Ages 15-64 accounted for 81.1% of cases with most occurring in the Atlanta Metropolitan area. When adjusted for population, one finds higher rates in more rural areas of the state. Data revealed that testing for the presence of drugs occurred among half of reported cases. Of those testing positive, five drug categories emerged; stimulants (45.8%), cannabinoids (21.5%), narcotics (including opioids) (14.1%), depressants (12.1%), and "Other Drugs" (6.3%). Positive drug screens across all drug classifications increased by 178.1% between 2007 and 2016. These findings suggest the need for state-wide policies designed to promote more consistent screening among pedestrians involved in motor vehicle crashes as well as diligence in understanding the role played by drugs among this population. Additional investigation should be conducted to tease out the presence of category-specific drugs among pedestrians. Understanding the epidemiology of pedestrian fatalities in the state, especially in relation to substance use, serves as a first step toward implementing localized preventive efforts.

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms’ decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs’ perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV’s algorithms and of policies and regulations to fully realise AVs’ benefits for smart and sustainable cities.