A safety assessment of mixed fleets with Connected and Autonomous Vehicles using the Surrogate Safety Assessment Module

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.

Examining road safety impacts of Green Light Optimal Speed Advisory (GLOSA) system

Mobility and environmental benefits of Green Light Optimal Speed Advisory (GLOSA) systems have been reported by many previous research studies, however, there is insufficient knowledge on the safety implications of such an application. For safe deployment of GLOSA system, it is most critical to identify and address potential safety issues in the design process. It can be argued that implementation of GLOSA system can improve safety by reducing traffic conflicts associated with the interrupted traffic flow at signalised intersections. However, more research findings are needed from field and simulation based studies to evaluate the impacts on safety under a variety of real-world scenarios. As part of the LEVITATE (Societal Level Impacts of Connected and Automated Vehicles) project under European Union's Horizon 2020 Programme, the main objective of this study is to examine the safety impacts of GLOSA under mixed traffic compositions with varying market penetration rates (MPR) of connected and automated vehicles (CAVs). A calibrated and validated microsimulation model (developed in Aimsun) of the greater Manchester area was used for this study where three signalised intersections in a corridor were identified for implementing GLOSA system. An improved algorithm was developed by identifying the potential issues/limitations in some of the GLOSA algorithms found in literature. Behaviours of CAVs were modelled based on the findings of a comprehensive literature review. Safety analysis was performed through processing the simulated vehicular trajectories in the surrogate safety assessment model (SSAM) by the Federal Highway Administration (FHWA). The surrogate safety assessment results showed small improvement in safety with the GLOSA implementation at multiple intersections in the test network only at low MPR (20%) scenarios of CAVs, as compared to the respective without GLOSA scenarios. No or rather slightly lower improvement in safety was observed with GLOSA implementation under mixed fleet scenarios with 40 % or higher 1st Generation or 2nd Generation CAVs, as compared to the respective scenarios without GLOSA. The implementation of GLOSA system was also found to have some impact on the traffic conflict types (although not consistent across all MPR scenarios), where rear-end conflicts were found to decrease while a slight increase was observed in lane-change conflicts.

The role of traffic conflicts in roundabout safety evaluation: A review

The roundabout is one type of at-grade intersection commonly seen in many countries. The evaluation of roundabout safety is usually counted on conflict analysis of the roundabout traffic due to random and limited records of real accidents. This paper surveyed published papers and reports that investigate the role of traffic conflicts in roundabout safety evaluation. It summarized the definitions and observation methods of roundabout conflicts and classified the attributing factors of roundabout conflicts and the countermeasures to control the conflicts. This study found that although unique traffic flow movements at roundabouts create special patterns of roundabout conflicts, the methods of roundabout conflict analysis used in most existing studies were inherited from the studies of highway or cross-intersection conflicts, including conflict definitions, conflict measurements, and thresholds of conflict severity. Special or improper designs of roundabout configurations or basic geometry elements could arouse roundabout conflicts. The most common vehicle-to-vehicle conflicts were entering-circulating conflicts, sideswipe conflicts, and exiting-circulating conflicts. The conflicts among vehicles and vulnerable road users (VRUs) easily evolved into serious collisions, but these conflicts did not get deserved attention in previous studies. Drivers' familiarity with roundabouts also affected road users' safety. Traffic signs and pavement markings were commonly used to control roundabout conflicts, while traffic signals were more effective methods for the roundabouts with uneven distribution of approaching traffic or high traffic volume. Based on the analysis of existing studies, this paper pointed out seven future directions of further research in term of conflict measurement, data collection, infrastructure and access management, geometry, drivers and VRUs, signal control, and vehicle control.

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.

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.

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.

Road traffic safety assessment in self-driving vehicles based on time-to-collision with motion orientation

Traffic conflict analysis based on Surrogate Safety Measures (SSMs) helps to estimate the risk level of an ego-vehicle interacting with other road users. Nonetheless, risk assessment for autonomous vehicles (AVs) is still incipient, given that most of the AVs are currently prototypes and current SSMs do not directly apply to autonomous driving styles. Therefore, to assess and quantify the potential risk arising from AV interactions with other road users, this study introduces the TTC_mo (Time-to-Collision with motion orientation), a metric that considers the yaw angle of conflicting objects. In fact, the yaw angle represents the orientation of the other road users and objects detected by the AV sensors, enabling a better identification of potential risk events from changes in the motion orientation and position through the geometric analysis of the boundaries for each detected object. Using the 3D object detection data annotations available from the publicly available AV datasets nuScenes and Lyft5 and the TTC_mo metric, we find that at least 8% of the interactions with objects detected around the AV present some risk level. This is meaningful, since it is possible to reduce the proportion of data analyzed by up to 60% when replacing regular TTC by our improved TTC computation.

Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways

Traffic accidents are one main cause of human fatalities in modern society. With the fast development of connected and autonomous vehicles (CAVs), there comes both challenges and opportunities in improving traffic safety on the roads. While on-road tests are limited due to their high cost and hardware requirements, simulation has been widely used to study traffic safety. To make the simulation as realistic as possible, real-world crash data such as crash reports could be leveraged in the creation of the simulation. In addition, to enable such simulations to capture the complexity of traffic, especially when both CAVs and human-driven vehicles co-exist on the road, careful consideration needs to be given to the depiction of human behaviors and control algorithms of CAVs and their interactions. In this paper, the authors reviewed literature that is closely related to crash analysis based on crash reports and to simulation of mixed traffic when CAVs and human-driven vehicles co-exist, for studying traffic safety. Three main aspects are examined based on our literature review: data source, simulation methods, and human factors. It was found that there is an abundance of research in the respective areas, namely, crash report analysis, crash simulation studies (including vehicle simulation, traffic simulation, and driving simulation), and human factors. However, there is a lack of integration between them. Future research is recommended to integrate and leverage different state-of-the-art transportation-related technologies to contribute to road safety by developing an all-in-one-step crash analysis system.

Collision-avoidance lane change control method for enhancing safety for connected vehicle platoon in mixed traffic environment

In a mixed traffic environment, the connected vehicle platoon cannot communicate and collaborate with the surrounding vehicles. In this case, there is a high risk of collision in large vehicle platoon's lane change scenario where the non-connected surrounding vehicle occupies the target lane-changing space of the platoon. This study proposes a collision-avoidance lane change control method for a connected bus platoon to elude the non-connected vehicle in the target lane for completing lane change in the mixed traffic environment safely. A platoon vehicle sensor system with low-cost and low data processing complexity is designed, which equips with multiple sensors in longitudinal and lateral directions. Under control of the proposed platoon controller on the basis of vehicle-to-vehicle (V2V) communication, the platoon following vehicles are fully autonomous in both longitudinal and lateral directions. The safe lane change decision-maker is designed based on the Finite State Machine (FSM). The decision-maker fuses multiple sensor data and determines the lane change operation of the following vehicles. To verify the effectiveness of the proposed method, a three-vehicle platoon is carried out the lane change experiments in a high-fidelity mixed traffic scenario built by the PreScan-Simulink joint simulation platform. Exposure-to-Risk Index (ERI) of the platoon vehicles is adopted to evaluate the collision risk of the platoon during lane changing process. Three typical case scenarios are tested, including unimpeded lane change, passive waiting lane change, and active accelerating lane change. The simulation results show that all platoon vehicles have an excellent success rate in lane change without collision with the non-connected surrounding vehicle in these scenarios. The proposed method exhibits compelling benefits on improving the safety of platoon vehicles in the mixed traffic environment.

From conflicts to crashes: Simulating macroscopic connected and automated driving vehicle safety

Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

Application of surrogate safety measures in higher levels of automated vehicles simulation studies: A review of the state of the practice

OBJECTIVE

Surrogate safety measures (SSMs) are developed and applied as alternatives or complements of safety analyses mainly due to important road crash data availability and reliability limitations. Automated vehicles (AVs) have recently emerged as a prominent solution to mitigate transport externalities and increase road traffic safety. Due to the novelty of the technology and the lack of real-world data, traffic simulation combined with SSMs is the most common approach to quantify their impact. This study aims to provide an overview of the state of the practice and, more specifically, examine the applicability of applied SSMs on higher levels of AVs (HAVs).

METHODS

The methodological approach consists of a comprehensive literature search, which aims to provide an overview of the applied SSMs, followed by a critical assessment of the findings.

RESULTS

In total, 17 studies and 11 different SSMs were identified and reviewed. Findings suggest that available SSMs are suitable measures to appropriately estimate the relative safety performance of HAVs and indicate their potential implications due to their expected rule-based driving behavior. However, in some cases, it was noticed that they could not efficiently capture the technological capabilities of HAVs, e.g., shorter headways and faster reaction times, which may lead to false alarms.

CONCLUSIONS

Despite the available evidence, there are still significant gaps and certain limitations, as no comparisons between different measures exist, or the validity of the applied measures could not be assessed based on historical road crash data. This work aims to help researchers and practitioners choose the most appropriate SSMs to evaluate HAVs' safety performance. Finally, several research gaps are identified, and recommendations for potential future research directions are presented.

Simulation-Based Analysis of "What-If" Scenarios with Connected and Automated Vehicles Navigating Roundabouts

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the "what-if" traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs' capabilities to increase roundabouts' throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.

Operation analysis of freeway mixed traffic flow based on catch-up coordination platoon

As one of the innovative technologies of intelligent transportation systems (ITS), Connected and Autonomous Vehicles (CAVs) have been deployed gradually. Given that there will be a long transition period before reaching a fully CAVs environment, it is crucial to assess the potential impacts of CAVs on mixed traffic flow. Considering platoon formation process, this study develops a platoon cooperation strategy based on "catch-up" mechanism, and then analyzes the impact on fundamental diagram, traffic oscillation, and traffic safety within mixed traffic. Simulation results show that with an increasing market penetration rate (MPR) of CAVs, road capacity shows an increasing trend. Compared with base scenario, a clear increase in road capacity is also observed under platoon scenario. With an increasing MPR, traffic oscillation is shown to reduce largely. Furthermore, the proposed platoon strategy could dampen frequent shockwaves and shorten the propagation range of waves. Regarding traffic safety, multiple surrogate safety measures (SSMs) are used to evaluate the traffic risk: including Criticality Index Function (CIF), Potential Index for Collision with Urgent Deceleration (PICUD), and Deceleration Rate to Avoid a Crash (DRAC). With increasing MPR, collision risk identified by CIF and DRAC shows an increase tendency, while that identified by PICUD has no apparent trend. Furthermore, the platoon strategy is shown to increase the severity of traffic conflicts significantly. Overall, this study provides novel insights into CAVs deployment through the analysis of platoon strategy.

Towards common ethical and safe 'behaviour' standards for automated vehicles

Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the 'safe driving behaviour of machines' are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers' driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe 'behaviour' of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the 'behaviour' of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users' perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety - SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors.

Developing dynamic speed limit strategies for mixed traffic flow to reduce collision risks at freeway bottlenecks

Connected and automated vehicles (CAVs) have the great potential to improve traffic flow because of their particular characteristics of connectivity and automation. This study aims to develop CAV control strategies based on car-following speed balance, which are defined as dynamic speed limit (DSL) strategies, and examine their performances on reducing freeway collision risks via microscopic simulations. The core idea of DSL strategy is to command the CAVs to slow down actively before reaching the bottlenecks, and form moving barriers to guide the following human driven vehicles to passively decelerate. Three DSL strategies are first developed for CAVs based on vehicle dynamics principles, and the influences of various position distribution patterns of CAVs on three strategies are compared in the one-lane scenario. Then, the DSL strategy with the best performance is selected based on simulation experiments, and a conventional variable speed limit control is used to compare the performance of our proposed methods. Finally, the DSL strategy based on Min control is tested in the two-lane, three-lane and four-lane scenarios to verify the effectiveness. Simulation results indicate that: (1) three DSL strategies based on CAVs can significantlyreduce collision risks when CAVs reach a certain proportion; (2) the uniform distribution of CAVs can maximize the effect of the moving barriers; (3) DSL strategy based on Min control is negatively affected by lane-changing behaviors, but still works well in the multi-lane scenario.

Multi-vehicle interaction safety of connected automated vehicles in merging area: A real-time risk assessment approach

The risky lane-changing manoeuvre of vehicles often occurs at expressway entrances, which would result in a high crash risk in the freeway system and significantly impact its safety. The highly anticipated environment of connected and autonomous vehicles (CAVs) is expected to reduce the associated crash risk of lane changing by offering various types of driving support, which utilise surrounding traffic information. The modelling crash risk under the environment of CAV driving during mandatory lane changing in merging areas faces new challenges due to the novelty of CAVs and subsequent shortage of data. To explore such risk situation of multi-vehicle interaction at expressway entrances, this study proposed a supervised learning algorithm and a Bayesian hierarchical model to assess risk levels and predict the probability of risk occurrence at different risk levels of interactive vehicles in real time of mandatory driving behaviour during the merging process. The learning algorithm, based on XGBoost, was exploited to classify risk levels. The Bayesian hierarchical model was used to analyse the probability of real-time risk comprising vehicle physical state layer, multi-vehicle interaction layer and risk probability layer. The probabilistic model parameters were calibrated using Markov Chain Monte Carlo (MCMC) Gibbs sampling method. The K-fold cross validation method was used to validate the proposed model of risk level. The probabilistic model validity was tested through posterior prediction of P-value. The quantitative risk estimation of CAVs through a few merging cases was conducted. Results show that the identification accuracy of slight, low, moderate and high risk is 94.24%, 85.82%, 84.16% and 79.69%, respectively. The P-value of Durbin-Watson's posterior, normal hypothesis, test distribution symmetry and kurtosis are all close to 0.5. Therefore, the method of real-time risk assessment is convergent and has good fitting. This research can promote cautious driving behaviours and provide reference for driver's decision making in the long term under the environment of CAVs.

Operational design domain of automated vehicles for crossing maneuvers at two-way stop-controlled intersections

The departure sight triangle provides the view for the vehicle waiting to cross at the two-way stop-controlled intersection. The factors influencing the sight triangle for human drivers are considered in the 2018 AASHTO Green Book, but the Green Book lacks quantitative estimations for automated vehicles (AVs). Therefore, to guarantee the AV's operational safety, this study investigated the impact of intersection angle, speed, and crossing distance on the AV's intersection crossing maneuver. Using physics theorems and cosine law, formulae for the detecting angle (DA) and distance (DD), the two main components of the departure sight triangle, were developed for the acute- and obtuse-angle sides of the intersection for an AV approaching on the minor road; the minimum required DA and DD, with a given crossing distance, are thus proposed for the AV's operational design domain (ODD). Calculations indicate that the DD is mainly affected by the major road design speed and crossing distance, and that the DD increases very quickly as the speed and crossing distance increase. The intersection angle was found to have great impact on the DA on both the acute and obtuse sides, but its influence is negative on the acute side and positive on the obtuse side. On the acute side, the ODD detecting angle range is set as [83.4, 132.7], [80.7, 131.6], and [78.4, 130.7] degrees for major roads with 2, 4, and 6 lanes, respectively. On the obtuse side, the ODD is set as [57.4, 160.6], [70.6, 207.9], and [82.2, 249.1] m for the same respective roads. After comparing the DA and DD results, and depending on the intersection design attributes, it is concluded that most engineering attention should be paid to the DA on the acute side and DD on the obtuse side.

Safety improvements by intelligent connected vehicle technologies: A meta-analysis considering market penetration rates

As the era of intelligent connected vehicles (ICVs) is approaching, a number of studies have investigated the potential benefits of ICVs, including the safety effects. Although previous studies agree that ICVs would significantly improve traffic safety, its quantified safety effects at different stages are still being debated. This study aims to estimate the ICVs' safety effects by market penetration rate (MPR) adopting a meta-analysis approach. The results from the meta-analysis indicate that the number of conflicts is exponentially reduced as the MPR goes up. For example, compared to the environment without ICVs, 4.2% and 17.4% of conflicts would decrease at the MPR of 10% and 50%, respectively. The effects are more obvious at higher MPR-43.4% of conflicts are expected to decrease at the MPR of 90%. From the case study in the United States based on the meta-analysis, it is expected that the MPR would reach 17-20% in the near future (2025) and 40-48% in 2035. The anticipated reduction in the number of fatal crashes would be 5% and 13%, in 2025 and 2035, respectively. The findings from this study will be useful for both public and private sectors to establish strategic plans to promote ICVs and identify their benefits at different MPRs.

A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling

Surrogate Safety Measures (SSM) are important for safety performance evaluation, since crashes are rare events and historical crash data does not capture near crashes that are also critical for improving safety. This paper focuses on SSM and their applications, particularly in Connected and Automated Vehicles (CAV) safety modeling. It aims to provide a comprehensive and systematic review of significant SSM studies, identify limitations and opportunities for future SSM and CAV research, and assist researchers and practitioners with choosing the most appropriate SSM for safety studies. The behaviors of CAV can be very different from those of Human-Driven Vehicles (HDV). Even among CAV with different automation/connectivity levels, their behaviors are likely to differ. Also, the behaviors of HDV can change in response to the existence of CAV in mixed autonomy traffic. Simulation by far is the most viable solution to model CAV safety. However, it is questionable whether conventional SSM can be applied to modeling CAV safety based on simulation results due to the lack of sophisticated simulation tools that can accurately model CAV behaviors and SSM that can take CAV's powerful sensing and path prediction and planning capabilities into crash risk modeling, although some researchers suggested that proper simulation model calibration can be helpful to address these issues. A number of critical questions related to SSM for CAV safety research are also identified and discussed, including SSM for CAV trajectory optimization, SSM for individual vehicles and vehicle platoon, and CAV as a new data source for developing SSM.

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.

Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles

The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV's market penetration increases, the interactions between conventional vehicles and AVs are inevitable but by no means clear. This study aims to create new knowledge by quantifying the behavioral changes caused when conventional human-driven vehicles follow AVs and investigating the impact of these changes (if any) on safety and the environment. This study analyzes data obtained from a field experiment by Texas A&M University to evaluate the effects of AVs on the behavior of a following human-driver. The dataset is comprised of nine drivers that attempted to follow 5 speed-profiles, with two scenarios per profile. In scenario one, a human-driven vehicle follows an AV that implements a human driver speed profile (base). In scenario two, the human-driven vehicle follows an AV that executes an AV speed profile. In order to evaluate safety, these scenarios are compared using time-to-collision (TTC) and several other driving volatility measures. Likewise, fuel consumption and emissions are used to investigate environmental impacts. Overall, the results show that AVs in mixed traffic streams can induce behavioral changes in conventional vehicle drivers, with some beneficial effects on safety and the environment. On average, a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk. The analysis showed a remarkable improvement in TTC as a result of the notably better speed adjustments of the following vehicle (i.e., lower differences in speeds between the lead and following vehicles) in the second scenario. Furthermore, human-driven vehicles were found to consume less fuel and produce fewer emissions on average when following an AV.

Connected vehicle-based road safety information system (CROSS): Framework and evaluation

Valuable high-resolution data representing the maneuvering of both individual subject vehicles and adjacent vehicles are available in the era of the connected vehicle systems, which is also referred to as cooperative intelligent transportation systems (C-ITS). C-ITS can share useful traffic information between connected vehicles (CV) and between vehicles and infrastructure in support of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communications. An excellent feature of a C-ITS pre-deployment project in Korean freeways that CVs are equipped with an in-vehicle forward collision warning system. This technical support provides a useful opportunity to evaluate crash risks more objectively and scientifically based on the analysis of vehicle interactions, which motivates our study. The purpose of this study is to develop a road safety information system based on the analysis of CV data. The proposed system estimates individual vehicle crash risks based on the crash potential index (CPI) and further utilizes them to develop a methodology for assessing road safety risks on freeways. High CPIs were observed in toll plaza area, recurrent congestion sections, and on and off-ramp areas. An encouraging result showed that the relationship between the estimated CPI and the actual crash frequencies was statistically meaningful. In addition, the impact of the CV market penetration rate (MPR) on the feasibility of the proposed road risk monitoring method was explored by microscopic traffic simulation experiments using VISSIM. A safety evaluation equivalent to 100 % MPR was obtainable with 30 % MPR. The outcomes of this study are expected to be utilized as fundamental to support the development of novel road risk monitoring systems in C-ITS environments.

Drivers’ Age and Automated Vehicle Explanations

Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust.

A comparative study of state-of-the-art driving strategies for autonomous vehicles

The autonomous vehicle is regarded as a promising technology with the potential to reshape mobility and solve many traffic issues, such as accessibility, efficiency, convenience, and especially safety. Many previous studies on driving strategies mainly focused on the low-level detailed driving behaviors or specific traffic scenarios but lacked the high-level driving strategy studies. Though researchers showed increasing interest in driving strategies, there still has no comprehensive answer on how to proactively implement safe driving. After analyzing several representative driving strategies, we propose three characteristic dimensions that are important to measure driving strategies: preferred objective, risk appetite, and collaborative manner. According to these three characteristic dimensions, we categorize existing driving strategies of autonomous vehicles into four kinds: defensive driving strategies, competitive driving strategies, negotiated driving strategies, and cooperative driving strategies. This paper provides a timely comparative review of these four strategies and highlights the possible directions for improving the high-level driving strategy design.

Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

An Integrated Microsimulation Approach for Safety Performance Assessment of the Wyoming Connected Vehicle Pilot Deployment Program

The 402-mile of Interstate 80 in Wyoming was selected by the U.S. Department of Transportation to develop, test, and deploy a suite of Connected Vehicle (CV) applications (WYDOT CV Pilot). It is expected that after full deployment of CV technology, the pilot will improve safety and mobility under adverse weather conditions by creating new ways to communicate road and travel information to both drivers and fleet managers. In this regard, this research employed an integrated microsimulation modeling approach to assess the safety performance of the WYDOT CV Pilot. A 23-mile representative I-80 corridor was selected for developing the microsimulation models. Traffic flow and driving behavior data under winter snowy weather condition were collected to calibrate the baseline microsimulation model. A driving simulator experiment was conducted to quantitatively investigate the impacts of CV technology on driving behavior; accordingly, the driving behavior data under CV environment were employed to properly update the calibrated CV microsimulation models. The safety effectiveness of the WYDOT CV Pilot were assessed for various demand levels and CV penetration rates. It was concluded that WYDOT CV applications increased drivers' situation awareness under adverse weather conditions, and thus reduced the crash risk. The reductions in conflicts displayed a decreasing trend with the increase of CV penetration rates, but the reduction was not significant when CV penetration was lower than 10 percent. The maximum reduction in conflicts was 85 percent when all trucks were equipped with CV technology.

Identification of safety benefits by inter-vehicle crash risk analysis using connected vehicle systems data on Korean freeways

Worldwide efforts have been made to deploy connected vehicle (CV) technologies in practice. The Korean government has also conducted various projects to fully exploit the benefits of CVs. This study attempted to estimate the safety benefits achievable of CVs based on crash risk analyses, which is a part of a pre-deployment project for CVs on freeways. A nice feature of the CVs in this project is that they are equipped with in-vehicle forward collision warning systems that are capable of providing both the speed of a preceding vehicle and the spacing between the preceding vehicle and a subject vehicle. This technical support enables us to systematically analyze vehicle interactions in terms of traffic safety. The crash potential index (CPI), which is able to analyze vehicle interactions in terms of crash risks, was adopted to quantify the crash potential of CVs when the forward hazardous situation warning (FHSW) information was either provided or not provided. The results of this study show that the average speed decreased by 10.2 % and the time-to-collision (TTC) increased by 5.3 % when warning information was provided. In addition, the achievable reduction in the CPI was approximately 20.7 % due to the provision of warning information. An illustrative demonstration of identifying freeway hazardous spots was also presented as a further application of the CPI analysis. The outcomes of this study will be useful for the establishment of relevant policies to promote CV technologies.

A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks

In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles

Connected and Automated Vehicle (CAV) technology, although in the development stage, is quickly expanding throughout the vehicle market. However, full market penetration will most likely require considerable planning as key stakeholders, manufacturers, consumers and governing agencies work together to determine optimal deployment strategies. Specifically, road safety is a critical challenge to the widespread deployment and adoption of this disruptive technology. During the transition period fleets will be composed of a combination of CAVs and conventional vehicles, and therefore it is imperative to investigate the repercussions of CAVs on traffic safety at different penetration rates. Since crash severity and frequency in conjunction reflect traffic safety, this study attempts to investigate the effect of CAVs on both crash severity and frequency through a microsimulation modelling exercise. VISSM microsimulation platform is used to simulate a case study of the M1 Geelong Ring Road network (Princes Freeway) in Victoria, Australia. Network performance is evaluated using performance metrics (Total System Travel Time, Delay) and kinematic variables (Speed, acceleration, jerk rate). Surrogate safety measures (time to collision, post encroachment time, etc.) are examined to inspect the safety in the network. The results indicate that the introduction of CAVs does not achieve the expected decrease in crash severity and rates involving manual vehicles, despite the improvement in network performance, given the demand and the set of parameters used in our operational CAV algorithm are intact. Additionally, the study identifies that the safety benefits of CAVs are not proportional to CAV penetration, and full-scale benefits of CAVs can only be achieved at 100 % CAV penetration. Further, considering network efficiency as a performance metric and total crash rate involving conventional vehicles as a safety metric, a Pareto frontier is extracted, for varying CAV operational behaviour. The results presented in this study provide insights into the impacts of CAVs on traffic safety valuable for insurance companies and other industry participants, enabling safety-related services and more enterprising business models.