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Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers' decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants' final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The driver's takeover time is crucial to ensure a safe takeover transition in conditional automated driving. The study aimed to construct a prediction model of driver's takeover time based on individual characteristics, external environment, and situation awareness variables. A total of 18 takeover events were designed with scenarios, non-driving-related tasks, takeover request time, and traffic flow as variables. High-fidelity driving simulation experiments were carried out, through which the driver's takeover data was obtained. Fifteen basic factors and three dynamic factors were extracted from individual characteristics, external environment, and situation awareness. In this experiment, these 18 factors were selected as input variables, and XGBoost and Shapely were used as prediction methods. A takeover time prediction model (BM + SA model) was then constructed. Moreover, we analyzed the main effect of input variables on takeover time, and the interactive contribution made by the variables. And in this experiment, the 15 basic factors were selected as input variables, and the basic takeover time prediction model (BM model) was constructed. In addition, this study compared the performance of the two models and analyzed the contribution of input variables to takeover time. The results showed that the goodness of fit of the BM + SA model (Adjusted_R 2 ) was 0.7746. The XGBoost model performs better than other models (support vector machine, random forest, CatBoost, and LightBoost models). The relative importance degree of situation awareness variables, individual characteristic variables, and external environment variables to takeover time gradually reduced. Takeover time increased with the scan and gaze durations and decreased with pupil area and self-reported situation awareness scores. There was also an interaction effect between the variables to affect takeover time. Overall, the performance of the BM + SA model was better than that of the BM model. This study can provide support for predicting driver's takeover time and analyzing the mechanism of influence on takeover time. This study can provide support for the development of real-time driver's takeover ability prediction systems and optimization of human-machine interaction design in automated vehicles, as well as for the management department to evaluate and improve the driver's takeover performance in a targeted manner., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Ideally, the evaluation of automated vehicles would involve the careful tracking of individual vehicles and recording of observed crash events. Unfortunately, due to the low frequency of crash events, such data would require many years to acquire, and potentially place the motorized public at risk if defective automated technologies were present. To acquire information on the safety effectiveness of automated vehicles more quickly, this paper uses the collective crash histories of a group of automated vehicles, and applies a duration modeling approach to the accumulated distances between crashes. To demonstrate the applicability of this approach as a method compare automated and conventional vehicles (human drivers), an empirical assessment was undertaken using two comparable sources of data. For conventional vehicles, police and non-police-reportable crashes were collected from the Second Strategic Highway Research Program's naturalistic driving study, and for automated vehicles, data from the California Department of Motor Vehicles Autonomous Vehicle Tester program were used (105 crashes from 59 permit holders driving ∼2.8 million miles were used for the analysis). The results of the empirical study showed that automated driving was safer at the 95% confidence level, with a higher number of miles between crashes, relative to their conventional vehicle counterparts. The findings indicate that the number of miles between crashes would be increased by roughly 27% when switching from conventional vehicles to automated vehicles. Despite limited data which mandated a group-vehicle approach, this study can be considered a reasonable initial approximation of automated vehicle safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Existing studies identified targeted audiences showing increases in Automated Vehicles (AV) acceptance after experiencing automated driving. However, there is still uncertainty regarding the reasons. Although some studies cited safety as the primary reason, there is no objective evidence from safety performance in verifying its impact on AV acceptance. This study contributes to the literature by quantitatively revealing why AV acceptance is changed after experiencing automated driving via a Structural Equation Modeling (SEM) method and objectively validating that safety is the primary factor in determining AV acceptance. Sixty drivers completed driving tasks on a driving simulator under Levels 0, 4, 3, and 2 and survey questions in between. As a result, the safety-related perceptions of AV were identified as reasons for affecting AV acceptance. Particularly, the evaluation of traffic conflicts and conflict severity validates the results from SEM, proving that safety is the primary and significant reason for influencing AV acceptance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In road traffic, mental overload often leads to a failure to notice new and distinctive stimuli. Such phenomenon is known as 'inattentional blindness'. Safe and efficient interaction between automated vehicles (AVs) and pedestrians is expected to rely heavily on external human-machine interfaces (eHMIs), a tool AVs are equipped with to communicate their intentions to pedestrians. This study seeks to explore the phenomenon of 'inattentional blindness' in the context of pedestrian-AV interactions. Specifically, the aim is to understand the effects of a warning eHMI on pedestrians' crossing decisions when they are engaged in a secondary task. In an experiment study with videos of pedestrian crossing scenarios filmed from the perspective of the crossing pedestrian, participants had to decide the latest point at which they would be willing to cross the road in front of an AV with an eHMI vs. an AV without an eHMI. Participants were also asked to predict the future behavior of the AV. 125 female and 9 male participants aged between 18 and 25 completed the experiment and a follow-up questionnaire. It was found that the presence of a warning eHMI on AVs contributes to a clearer understanding of pedestrians' inferences about the intention of AVs and helps deter late and dangerous crossing decisions made by pedestrians. However, the eHMI fail to help pedestrians avoid such decisions when they face a high mental workload induced by secondary task engagement., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The objective of this study was to use computational models to study how unconventional seating positions and orientations in vehicles with Automated Driving System (ADS) may affect occupant response metrics of children with various restraint conditions. A literature review was first conducted to frame a simulation plan, including selections of surrogate ADS-equipped vehicles, potential seating arrangements, impact scenarios, anthropomorphic test device (ATD) models, and child restraint system (CRS) models that are relevant to the selected ATD models. Due to the lack of impact tests with child ATDs and CRS in farside, oblique, and rear impacts, 17 sled tests were conducted with CRS harness-restrained ATDs and vehicle belt-restrained ATDs in frontal, farside, oblique, and rear impact conditions. The sled tests were then used to validate a set of MADYMO (MAthematical DYnamic MOdels) v7.7 models. A total of 550 simulations were then conducted with four child ATDs and various CRS conditions across a range of conventional and unconventional seating locations and orientations under five impact directions. We did not find major safety concerns with ATDs restrained by harnessed CRSs based on the nature of ATD contacts. Compared with frontal and rear impacts, CRSs may rotate laterally in farside and oblique impacts, which could result in higher head and chest injury measures than frontal due to inertial loading to the CRS, and the larger lateral rotation of the CRS may lead to a contact between the CRS and vehicle interior. The major safety concern for vehicle belt-restrained ATDs (with and without booster) is that they have the potential to contact the seat next to them or the instrument panel behind them in a farside or oblique impact. Unconventional seating does not necessarily create additional safety concerns beyond what we know with the conventional seating. However, due to the orientation of the unconventional seats, the occupants on those seats may be involved in a higher percentage of oblique and rear-oblique impacts relative to their seating orientations than conventional seats, which may be considered in the future safety design process. This is the first study using different child ATDs and CRSs to investigate child occupant responses in a wide range of impact directions and seating orientations. Results from the sled tests and simulations provide a better understanding of child occupant responses in those crash conditions, but also identified several limitations of using frontal ATDs in other crash directions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

When a cyclist's path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists' (other road users') behavior will be increasingly important to ensure safety. To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists' behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions. In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists' crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists' behavioral cues obtained from sensors. Both kinematics and cyclists' behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist's yielding behavior. This research shows that adding information about the cyclists' behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Vehicle automation promises to reduce the demands of the driving task, making driving less fatiguing, more convenient, and safer. Nevertheless, Level 3 automated vehicles rely on a human driver to be ready to resume control, requiring the driver to reconstruct situation awareness (SA) and resume the driving task. Understanding the interaction between non-driving-related task (NDRT) use, SA, and takeover capacity is important because an effective takeover is entirely dependent on, and scaffolds from, effectively reconstructed SA. While a number of studies have looked at the behavioural impact of being 'in- or on-the-loop', fewer consider the cognitive impact, particularly the consequences for SA. The present study exposed participants to an extended simulated automated drive involving two critical takeover scenarios (early- and late-drive). We compared automated vehicle (AV) operators who were required to passively monitor the vehicle to those engaging with self-selected NDRTs. Monitoring operators demonstrated lower total- and schema-specific SA count scores following a fatiguing drive compared to those engaging with self-selected NDRTs. NDRT engagement resulted in no significant difference in SA count scores early- and late-drive. Assessment of differences in the type and sensory modality of NDRTs indicated operators make fundamentally different selections about the NDRTs they engage with in an automated driving environment compared to a manual environment. The present study provides further evidence linking SA and AV operator behaviour and underscores the need to understand the role of SA in takeover capacity. Our findings suggest that although SA declines over time regardless of driving task requirements (Monitoring versus NDRT engagement), NDRT use may facilitate better SA construction, with implications for the regulation of NDRT use in AVs as the technology progresses., Competing Interests: Declaration of Competing Interest This research was supported by an Australian Government Research Training Program (RTP) Scholarship. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

This study contributes to understanding the behavioral impacts of infrastructure adaptation to Automated Vehicles (AVs) on non-AV drivers. It attempts to answer the question of how a narrow (9 ft) lane dedicated to AVs would affect the behavior of drivers in terms of safety measures who are driving in the adjacent lane to the right. To this end, a custom designed driving simulator world was designed mimicking the Interstate 15 smart corridor in San Diego. A group of participants were assigned to drive next to the simulated 9 ft narrow lane while a control group were assigned to drive next to a regular 12 ft AV lane. Behavior of drivers was analyzed by measuring the mean lane position, mean speed, and the mental effort. In addition to AV lane width, AV headway, gender, and right lane traffic were taken into consideration in the experimental design to investigate interaction effects. The results showed no significant differences in speed and mental effort of drivers while indicating significant differences in lane positioning. Although the overall effect of AV lane width was not significant, there were some significant interaction effects between lane width and other factors (i.e., driver gender and presence of traffic on the next regular lane to the right). In all the significant interactions, there was no case in which those factors stayed constant while AV lane width changed between the groups indicating that the significant difference might be stemmed from the other factors rather than the lane width. However, the trend observed was that drivers driving next to the 12 ft lane had better lane centering compared to the 9 ft lane. The analysis also showed that while in general female drivers tended to drive further away from the 9 ft lane and performed worse in terms of lane centering, they performed better than male drivers when right lane traffic was present., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

As automated vehicles are deployed across the world, it has become critically important to understand how these vehicles interact with each other, as well as with other conventional vehicles on the road. One such method to achieve a deeper understanding of the safety implications for Automated Vehicles (AVs) is to analyze instances where AVs were involved in crashes. Unfortunately, this poses a steep challenge to crash-scene investigators. It is virtually impossible to fully understand the factors that contributed to an AV involved crash without taking into account the vehicle's perception and decision making. Furthermore, there is a tremendous amount of data that could provide insight into these crashes that is currently unused, as it also requires a deep understanding of the sensors and data management of the vehicle. To alleviate these problems, we propose a data pipeline that takes raw data from all on-board AV sensors such as LiDAR, radar, cameras, IMU's, and GPS's. We process this data into visual results that can be analyzed by crash scene investigators with no underlying knowledge of the vehicle's perception system. To demonstrate the utility of this pipeline, we first analyze the latest information on AV crashes that have occurred in California and then select two crash scenarios that are analyzed in-depth using high-fidelity synthetic data generated from the automated vehicle simulator CARLA. The data visualization procedure is demonstrated on the real-world Kitti dataset by using the YOLO object detector and a monocular depth estimator called AdaBins. Depth from LIDAR is used as ground truth to calibrate and assess the effect of noise and errors in depth estimation. The visualization and data analysis from these scenarios clearly demonstrate the vast improvement in crash investigations that can be obtained from utilizing state-of-the-art sensing and perception systems used on AVs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

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., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

To integrate automated vehicles (AVs) into our transportation network, we should consider how human road users will interact with them. Human aggression toward AVs could be a new risk in mixed traffic and reduce AV adoption. Is it OK to drive aggressively toward AVs? We examined how identical aggressive behavior toward an AV or human driver is appraised differently by observers. In our 2 (scenario type: human driver vs. AV) × 2 (victim identity salience: low vs. high) between-subjects survey, we randomly allocated participants (N = 956) to one of four conditions where they viewed a video clip from an AV or a human driver showing a car suddenly braking continuously ahead of the AV or human driver's car. The salience of victim identity influenced the observers' appraisals of aggressive behavior. When asked to judge the front car's behavior toward this AV or human driver (the victim identity is salient), they reported more acceptability and less risk perception, negative affect, and immoral judgment while judging this behavior toward the AV. When asked to judge the front car's behavior (the victim identity not highlighted), they reported non-different appraisals. This finding implies that AVs might need to hide their identity to blend in visually and behaviorally as regular cars., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Differently sized automated vehicles (AVs) will enter the roads of tomorrow and will interact with other road users. Pedestrians as vulnerable road users heavily rely on the communication with other road users, especially for the interaction with larger vehicles, as miscommunication pose a high risk. Therefore, AVs need to provide communication abilities to safely interact with pedestrians. This study's focus was on the explicit communication which is highly relevant in low-speed and low-distance traffic scenarios to clarify misunderstandings before they result in accidents. External human-machine interfaces (eHMIs) placed on the outside of AVs can be used as a communication tool to explicitly inform the surrounding traffic environment. Although research manifested effects of vehicle size on pedestrians' perceived safety and crossing behavior, little research about the eHMI design for differently sized AVs exists. This experimental online study (N = 155) aimed at investigating the application of a light-based eHMI on two differently sized AVs (car, bus) by focusing on the overall goal of ensuring traffic safety in future traffic. The light-based eHMI showed different communication strategies, i.e., a static eHMI and three dynamic eHMIs. The results revealed that an automated car was perceived as safer and affectively rated as more positive compared to an automated bus. Nevertheless, no significant differences were found between the two AVs in terms of the eHMI communication. A dynamic eHMI was perceived as safer and evaluated affectively as more positive compared to a static eHMI or no eHMI for both AVs. In conclusion, the use of a light-based eHMI had a positive effect on pedestrians' interaction with an automated car and an automated bus and, therefore, could contribute to the overall traffic safety in this study. Implications for the design of eHMIs for differently sized AVs were discussed., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

We present an alternative approach to the forecasting of motor vehicle collision rates. We adopt an oft-used tool in mathematical finance, the Heston Stochastic Volatility model, to forecast the short-term and long-term evolution of motor vehicle collision rates. We incorporate a number of extensions to the Heston model to make it fit for modelling motor vehicle collision rates. We incorporate the temporally-unstable and non-deterministic nature of collision rate fluctuations, and introduce a parameter to account for periods of accelerated safety. We also adjust estimates to account for the seasonality of collision patterns. Using these parameters, we perform a short-term forecast of collision rates and explore a number of plausible scenarios using long-term forecasts. The short-term forecast shows a close affinity with realised rates (over 95% accuracy), and outperforms forecasting models currently used in road safety research (Vasicek, SARIMA, SARIMA-GARCH). The long-term scenarios suggest that modest targets to reduce collision rates (1.83% annually) and targets to reduce the fluctuations of month-to-month collision rates (by half) could have significant benefits for road safety. The median forecast in this scenario suggests a 50% fall in collision rates, with 75% of simulations suggesting that an effective change in collision rates is observed before 2044. The main benefit the model provides is eschewing the necessity for setting unreasonable safety targets that are often missed. Instead, the model presents the effects that modest and achievable targets can have on road safety over the long run, while incorporating random variability. Examining the parameters that underlie expected collision rates will aid policymakers in determining the effectiveness of implemented policies., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Automated Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and acceptance of AVs' driving styles. The present study investigated the effects of the designed driving style of AV (aggressive/defensive) and driver's driving style (aggressive/defensive) on driver's trust, acceptance, and take-over behavior in a fully AV. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve driving scenarios in either an aggressive AV or a defensive AV. Results revealed that driver's trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV's driving style and driver's driving style. General estimating equations were conducted to analyze the relationships between driver's trust, acceptance, and take over frequency. The results showed that the effect of driver's trust in AVs on takeover frequency was mediated by driver's acceptance of AVs. These findings implied that driver's trust and acceptance of AVs could be enhanced when the designed AV's driving style aligned with driver's own driving style, which in turn, reduce undesired take over behavior. However, the "aggressive" AV driving style should be designed carefully considering driver safety., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Automated vehicles are anticipated to have benefits for older adults in maintaining their mobility and autonomy. These anticipated benefits can only be realized if this technology is accepted and thus used by older adults. However, it remains unclear how certain factors affect older adults' acceptance of automated vehicles. This study investigated the extent to which older adults' acceptance of fully automated vehicles are affected by exposure to automated vehicle technology (pre- vs. post-exposure), driving style (manual style relative to automated style), driving conditions (clear, rain, traffic), and age. Thirty-six older adults (M = 73.25, SD = 5.96) completed non-automated (manual) and fully automated driving scenarios under different driving conditions in a high-fidelity driving simulator. The fully automated driving scenarios were designed to be reliably driven by the system in a conservative driving style. Driving conditions included clear daytime, rain, and high-traffic. Pre- and post-exposure to the simulated fully automated driving experience, participants rated their comfort level with fully automated vehicles (FAVs). Additionally, after each driving condition, participants answered a validated questionnaire on their acceptance of the simulated fully automated experience for each respective driving condition. Age and driving style were found to have a significant effect on older adults' acceptance of FAVs, with older age and greater dissimilarity of an individual's manual driving style from the FAV's driving style being associated with lower acceptance. The results suggest that if reliability of fully automated vehicles is ultimately ensured and is demonstrated to the older adults, their acceptance of fully automated vehicles is generally high, particularly if the FAV is operated in a style similar to their own., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Increasing automation calls for evaluating the effectiveness and intelligence of automated vehicles. This paper proposes a framework for quantitatively evaluating the intelligence of automated vehicles. Firstly, we establish the evaluation environment for automated vehicles including test field, test task, and evaluation index. The test tasks include the single vehicle decision-making (turning, lane-changing, overtaking, etc.) and the maneuver execution of multi-vehicle interaction (obstacle avoidance, trajectory optimization, etc.). The intelligence evaluation index is the action amount of driving process considering the safety, efficiency, rationality and comfort. Then, we calculate the actual action amount of the automated vehicle in different scenarios in the test field. Finally, the least action calculated theoretically corresponds to the highest intelligence degree of the automated vehicle, and is employed as a standard to quantify the performance of other tested automated vehicles. The effectiveness of this framework is verified with two naturalistic driving datasets that contain the normal driving scenarios and high-risk scenarios. Specifically, the naturalistic lane-changing data filters 40,416 frames and 179 similar lane-changing trajectories. Compared with the lane-changing behavior of a large number of drivers, experimental results verify that the proposed algorithm can achieve the intelligence degree of drivers in the lane change scenario. Meanwhile, in 253 reconstructed high-risk scenarios, the intelligent risk avoidance ability of the proposed intelligence degree evaluation algorithm can be verified by comparing with the driver behavior and TTC algorithm. These experimental results show that the proposed framework can effectively quantify intelligence and evaluate the performance of automated vehicles under various scenarios., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Automated vehicles (AVs), the wide adoption of which is expected to improve traffic safety significantly, are penetrating our roads. The AVs that are testing on public roads have been bullied by human road users. We are not sure whether the bullying incidents are isolated or will be common in the future. In a cross-national survey (N = 998 drivers in China and South Korea), we developed an eleven-item bullying intention questionnaire. We assumed and confirmed that, overall, participants had a greater intention to bully machine drivers than to bully other human drivers. Compared to the Korean participants, the Chinese participants reported a greater intention to drive aggressively. The correlations of their intention to bully AVs with their attitude toward AVs and with risk-benefit perception of AVs were weak. Male participants (vs. female participants) and younger participants (vs. older participants) reported a greater intention to drive aggressively. Drivers' aggressive behaviors toward AVs might be common in the future, which might increase traffic risk and hinder the implementation of this technology., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Automated vehicles are emerging on the transportation networks as manufacturers test their automated driving system (ADS) capabilities in complex real-world environments in testing operations like California's Autonomous Vehicle Tester Program. A more comprehensive understanding of the ADS safety performances can be established through the California Department of Motor Vehicle disengagement and crash reports. This study comprehensively examines the safety performances (159,840 disengagements, 124 crashes, and 3,669,472 automated vehicle miles traveled by the manufacturers) documented since the inauguration of the testing program. The reported disengagements were categorized as control discrepancy, environmental conditions and other road users, hardware and software discrepancy, perception discrepancy, planning discrepancy, and operator takeover. An applicable subset of disengagements was then used to identify and quantify the 5 W's of these safety-critical events: who (disengagement initiator), when (the maturity of the ADS), where (location of disengagement), and what/why (the facts causing the disengagement). The disengagement initiator, whether the ADS or human operator, is linked with contributing factors, such as the location, disengagement cause, and ADS testing maturity through a random parameter binary logit model that captured unobserved heterogeneity. Results reveal that compared to freeways and interstates, the ADS has a lower likelihood of initiating the disengagement on streets and roads compared to the human operator. Likewise, software and hardware, and planning discrepancies are associated with the ADS initiating the disengagement. As the ADS testing maturity advances in months, the probability of the disengagement being initiated by the ADS marginally increases when compared to human-initiated. Overall, the study contributes by understanding the factors associated with disengagements and exploring their implications for automated systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

To assess and explain finely drivers' a priori acceptance of highly automated cars, this study used the Theory of Planned Behaviour (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Further, the current study sought to extend upon previous research to assess if intentions to use highly automated cars in the future differed according to country (i.e., Australia, France, & Sweden). These three countries were selected to enable comparisons of a priori acceptance between countries of differing levels of exposure to highly automated cars. Participants (N = 1563; 62.1 % male) were recruited in Australia (n = 558), France (n = 625), and Sweden (n = 380) to complete a 20 min online questionnaire. The findings differed according to country of residence. Individuals residing in France reported significantly greater intentions to use highly automated cars when they become publicly available compared to individuals residing in Australia and in Sweden. Of the TPB constructs entered at step 1 in the hierarchical regression, attitudes, subjective norms, and perceived behavioural control (capability and controllability) were significant predictors of intentions to use highly automated cars for participants residing in Australia and France. For participants residing in Sweden, only attitudes and PBC-capability were significant predictors of intentions. Of the UTAUT constructs entered at step 2, performance expectancy and effort expectancy were significant predictors of intentions for participants residing in France and only performance expectancy a significant predictor of intentions for participants residing in both Australia and Sweden. Age and gender did not add to the prediction of intentions when entered at step 3. However, pre-existing knowledge was a significant negative predictor of intentions when entered at step 3 for participants residing in Australia. Overall, the findings found some support for applying the TPB and UTAUT to assess intentions to use highly automated cars in different countries. The findings also highlight differences in a priori acceptance across countries and the factors which predict such acceptance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Automated vehicles (AVs) represent an opportunity to reduce crash frequency by eliminating driver error, as safety studies reveal human error contributes to the majority of crashes. To provide insights into the contributing factors of AV crashes, this study created a unique database from the California Department of Motor Vehicles 124 manufacturer-reported Traffic Collision Reports and was linked with detailed data on roadway and built-environment attributes. A novel text analysis was first conducted to extract useful information from crash report narratives. Of the crashes that could be geocoded (N = 113), results indicate the most frequent AV crash type was rear-end collisions (61.1%; N = 69) and 13.3% (N = 15) were injury crashes. These noteworthy outcomes and a small sample size motivated us to rigorously analyze rear-end and injury crashes in a Full Bayesian empirical setup. Owing to the potential issue of unobserved heterogeneity, hierarchical-Bayes fixed and random parameter logit models are estimated. Results reveal that when the automated driving system is engaged and remains engaged, the likelihood of an AV-involved rear-end crash is substantially higher compared to a conventionally-driven AV or when the driver disengages the automated driving system prior to a crash. Given the AV-involved crashes, the likelihood of an AV-involved rear-end crash was significantly higher in mixed land-use settings compared to other land-use types, and was significantly lower near public/private schools. Correlations of other roadway attributes and environmental factors with AV-involved rear-end and injury crash propensities are discussed. This study aids in understanding the interactions of AVs and human-driven conventional vehicles in complex urban environments., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Automated vehicles (AV) testing on the public roads is ongoing in several states in the US as well as in Europe and Asia. As long as the automated vehicle technology has not achieved full automation (Level 5), human drivers are still expected to take over the steering wheel and throttles when there is an automated vehicle disengagement. However, contributing factors and the mechanism about automated vehicle-initiated disengagement has not been quantitatively and comprehensively explored and investigated due to the lack of field test data. Besides, understanding human drivers' perception and promptness of reaction to the AV disengagement is essential to ensure safety transition between automated and manual driving. By harnessing California's Autonomous Vehicle Disengagement Report Database, which includes the AV disengagement data from field tests in 2016-2017, this paper quantitatively investigated the AV disengagement using multiple statistical modeling approaches that involve statistical modeling and classification tree. Specifically, the paper identifies the contributing factors impacting human drivers' promptness to AV disengagements, and quantitatively investigates the underlying causes to AV disengagements. Results indicate that current AV disengagement on public roads is dominated by causes due to a planning issue. The cause of an AV disengagement is significantly induced by lacking certain numbers of radar and LiDAR sensors installed on the automated vehicles. These thresholds of these sensors needed are revealed. Cause of disengagement and roadway characteristics significantly impact drivers' take-over time when facing an AV disengagement. AV perception or control issue-based disengagement can significantly extend drivers' perception-reaction time to take over the driving. The quantitative knowledge obtained ultimately facilitates revealing the mechanisms of the automated vehicle disengagements to ensure safe AV operations on public roads., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

This study applied the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to assess drivers' intended use of automated vehicles (AVs) after undertaking a simulated driving task. In addition, this study explored the potential for trust to account for additional variance to the psychosocial factors in TPB and TAM. Seventy-four participants (51% female) aged between 25 and 64 years (M = 42.8, SD = 12.9) undertook a 20 min simulated experimental drive in which participants experienced periods of automated driving and manual control. A survey task followed. A hierarchical regression analysis revealed that TPB constructs; attitude toward the behavior, subjective norms, and perceived behavioral control, were significant predictors of intentions to use AV. In addition, there was partial support for the test of TAM, with ease of use (but not usefulness) predicting intended use of AV (SAE Level 3). Trust contributed variance to both models beyond TPB or TAM constructs. The findings provide an important insight into factors that might reflect intended use of vehicles that are primarily automated (longitudinal, lateral, and manoeuvre controls) but require and allow drivers to have periods of manual control., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Many light-duty vehicle crashes occur due to human error and distracted driving. Partially-automated crash avoidance features offer the potential to reduce the frequency and severity of vehicle crashes that occur due to distracted driving and/or human error by assisting in maintaining control of the vehicle or issuing alerts if a potentially dangerous situation is detected. This paper evaluates the benefits and costs of fleet-wide deployment of blind spot monitoring, lane departure warning, and forward collision warning crash avoidance systems within the US light-duty vehicle fleet. The three crash avoidance technologies could collectively prevent or reduce the severity of as many as 1.3 million U.S. crashes a year including 133,000 injury crashes and 10,100 fatal crashes. For this paper we made two estimates of potential benefits in the United States: (1) the upper bound fleet-wide technology diffusion benefits by assuming all relevant crashes are avoided and (2) the lower bound fleet-wide benefits of the three technologies based on observed insurance data. The latter represents a lower bound as technology is improved over time and cost reduced with scale economies and technology improvement. All three technologies could collectively provide a lower bound annual benefit of about $18 billion if equipped on all light-duty vehicles. With 2015 pricing of safety options, the total annual costs to equip all light-duty vehicles with the three technologies would be about $13 billion, resulting in an annual net benefit of about $4 billion or a $20 per vehicle net benefit. By assuming all relevant crashes are avoided, the total upper bound annual net benefit from all three technologies combined is about $202 billion or an $861 per vehicle net benefit, at current technology costs. The technologies we are exploring in this paper represent an early form of vehicle automation and a positive net benefit suggests the fleet-wide adoption of these technologies would be beneficial from an economic and social perspective., (Copyright © 2016 Elsevier Ltd. All rights reserved.)