Hazard Perception in Visually Impaired Drivers Who Use Bioptic Telescopes.

Purpose: Bioptic telescopic spectacles can allow individuals with central vision impairment to obtain or maintain driving privileges. The purpose of this study was to (1) compare hazard perception ability among bioptic drivers and traditionally licensed controls, (2) assess the impact of bioptic telescopic spectacles on hazard perception in drivers with vision impairment, and (3) analyze the relationships among vision and hazard detection in bioptic drivers. Methods: Visual acuity, contrast sensitivity, and visual field were measured for each participant. All drivers completed the Driving Habits Questionnaire. Hazard perception testing was conducted using commercially available first-person video driving clips. Subjects signaled when they could first identify a traffic hazard requiring a change of speed or direction. Bioptic drivers were tested with and without their bioptic telescopes in alternating blocks. Hazard detection times for each clip were converted to z-scores, converted back to seconds using the average response time across all videos, and then compared among conditions. Results: Twenty-one bioptic drivers and 21 normally sighted controls participated in the study. The hazard response time of bioptic drivers was improved when able to use the telescope (5.4 ± 1.4 seconds vs 6.3 ± 1.8 seconds without telescope); however, it remained significantly longer than for controls (4.0 ± 1.4 seconds). Poorer visual acuity, contrast sensitivity, and superior visual field sensitivity loss were related to longer hazard response times. Conclusions: Drivers with central vision loss had improved hazard response times with the use of bioptic telescopic spectacles, although their responses were still slower than normally sighted control drivers. Translational Relevance: The use of a bioptic telescope by licensed, visually impaired drivers improves their hazard detection speed on a video-based task, lending support to their use on the road.

Lights, Sirens, and Load: Anticipatory emergency medical treatment planning causes cognitive load during emergency response driving among paramedicine students.

Paramedics face various unconventional and secondary task demands while driving ambulances, leading to significant cognitive load, especially during lights-and-sirens responses. Previous research suggests that high cognitive load negatively affects driving performance, increasing the risk of accidents, particularly for inexperienced drivers. The current study investigated the impact of anticipatory treatment planning on cognitive load during emergency driving, as assessed through the use of a driving simulator. We recruited 28 non-paramedic participants to complete a simulated baseline drive with no task and a cognitive load manipulation using the 1-back task. We also recruited 18 paramedicine students who completed a drive while considering two cases they were travelling to: cardiac arrest and infant seizure, representing varying difficulty in required treatment. The results indicated that both cases imposed considerable cognitive load, as indicated by NASA Task Load Index responses, comparable to the 1-back task and significantly higher than driving with no load. These findings suggest that contemplating cases and treatment plans may impact the safety of novice paramedics driving ambulances for emergency response. Further research should explore the influence of experience and the presence of a second individual in the vehicle to generalise to broader emergency response driving contexts.

Accustomed or Regulated: Influencing factors of two-wheeler riders' illegal Lane-Transgressing behavior when overtaking.

Illegal lane-transgressing is a typical aberrant riding behavior of riders of two-wheelers, i.e., motorcycles, bicycles, and e-bikes, which is highly frequent in accident reports. However, there is insufficient attention to this behavior at present. This study aims to explore the socio-psychologic factors that influence the illegal lane-transgressing behavior of two-wheeler riders when overtaking. For this purpose, a questionnaire was first composed. The questionnaire included the behavioral intention of two-wheeler riders towards illegal overtaking behavior and five influencing factors: safety knowledge, descriptive norms, injunctive norms, perceived behavior control, and risk perception. Second, a survey was conducted on different two-wheeler riders in Xi'an. Third, various types of two-wheelers were analyzed jointly and separately by structural equation models and analyses of variance. Results show that e-bike riders were more similar to motorcycle riders in behavioral intentions, with their risk perception weaker than other riders. Descriptive norms and perceived behavior control played the most significant roles in the structural equation model. It was also found that two-wheeler riders with a car license had better traffic safety performance. Based on the above results, it is recommended that attention be paid to illegal lane-transgression in the process of law enforcement and education, and a higher level of safety training should be provided for two-wheeler riders.

Enhancing multi-scenario applicability of freeway variable speed limit control strategies using continual learning.

Variable speed limit (VSL) control benefits freeway operations through dynamic speed limit adjustment strategies for specific operation scenarios, such as traffic jams, secondary crash prevention, etc. To develop optimal strategies, deep reinforcement learning (DRL) has been employed to map the traffic operation status to speed limits with the corresponding control effects. Then, VSL control strategies were obtained based upon memories of these complex mapping relationships. However, under multi-scenario conditions, DRL trained VSL faces the challenge of performance decay, where the control strategy effects drop sharply for early trained "old scenarios". This so-called scenario forgetting problem is attributed to the fact that DRL would forget the learned old scenario mapping memories after new scenario trainings. To tackle this issue, a continual learning approach has been introduced in this study to enhance the multi-scenario applicability of VSL control strategies. Specifically, a gradient projection memory (GPM) based neural network parameter updating method was proposed to keep the mapping memories of old scenarios during new scenario trainings by imposing constraints on the direction of gradient updates for new tasks. The proposed method was evaluated using three typical freeway operation scenarios developed in the simulation platform SUMO. Experimental results showed that the continual learning approach has substantially reduced the performance decay in old scenarios by 17.76% (valued using backward transfer metrics). Furthermore, the multi-scenario VSL control strategies successfully reduced the speed standard deviation and average travel time by 28.77% and 7.25% respectively. Moreover, the generalization of the proposed continual learning based VSL approach were evaluated and discussed.

Audiovisual presentation of variable message signs improves message processing in distracted drivers during partially automated driving.

INTRODUCTION: Highly automated driving is expected to reduce the accident risk occurrence by human errors, but it can also increase driver distraction. Previous evidence shows that auditory signals can help drivers take over in critical situations. However, it is still uncertain whether the potential benefit of verbal auditory signals could be generalized to driving situations where drivers are visually and auditorily distracted. METHOD: Our first objective was to compare the effectiveness of complementary audio messages (audio + visual condition) and visual only (visual condition) variable message signs (VMS) messages. The second objective was to explore the potential use of oral messages with traffic information to help highly-automated vehicle drivers identify critical situations. Eye-tracking data were also registered. Twenty-four volunteers participated in a driving simulator study, completing two tasks: (a) a TV series task, where they had to pay attention to an episode of a TV series while traveling along the route; and (b) a VMS task, where they had to recover the manual control of the car if the VMS message was a 'critical message.' RESULTS: General results showed that, when the audio was available, the participants: (a) had a higher ability to discriminate the VMS messages, (b) were less conservative, (c) responded earlier, and (d) their pattern of fixations was more efficient. A complementary analysis showed that the counterbalance order was a moderating factor for the discrimination ability and the response distance measures. This evidence suggests a potential learning effect, not cancelled by counterbalancing the order of the conditions. CONCLUSION: The processing of traffic messages may improve when provided as oral and visual messages. PRACTICAL APPLICATIONS: These results would be of special interest for engineers designing highly automated cars, considering that the design of automated systems must ensure that the driver's attention is sufficient to take over control.

Assessing the determinants of crash propensity using structural equation modeling: Role of distractions caused by fellow drivers.

INTRODUCTION: Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity. METHOD: Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses. RESULTS: The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity. PRACTICAL APPLICATIONS: The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs.

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

A panoramic driving perception fusion algorithm based on multi-task learning.

With the rapid development of intelligent connected vehicles, there is an increasing demand for hardware facilities and onboard systems of driver assistance systems. Currently, most vehicles are constrained by the hardware resources of onboard systems, which mainly process single-task and single-sensor data. This poses a significant challenge in achieving complex panoramic driving perception technology. While the panoramic driving perception algorithm YOLOP has achieved outstanding performance in multi-task processing, it suffers from poor adaptability of feature map pooling operations and loss of details during downsampling. To address these issues, this paper proposes a panoramic driving perception fusion algorithm based on multi-task learning. The model training involves the introduction of different loss functions and a series of processing steps for lidar point cloud data. Subsequently, the perception information from lidar and vision sensors is fused to achieve synchronized processing of multi-task and multi-sensor data, thereby effectively improving the performance and reliability of the panoramic driving perception system. To evaluate the performance of the proposed algorithm in multi-task processing, the BDD100K dataset is used. The results demonstrate that, compared to the YOLOP model, the multi-task learning network performs better in lane detection, drivable area detection, and vehicle detection tasks. Specifically, the lane detection accuracy improves by 11.6%, the mean Intersection over Union (mIoU) for drivable area detection increases by 2.1%, and the mean Average Precision at 50% IoU (mAP50) for vehicle detection improves by 3.7%.

Effects of multimodal explanations for autonomous driving on driving performance, cognitive load, expertise, confidence, and trust.

Advances in autonomous driving provide an opportunity for AI-assisted driving instruction that directly addresses the critical need for human driving improvement. How should an AI instructor convey information to promote learning? In a pre-post experiment (n = 41), we tested the impact of an AI Coach's explanatory communications modeled after performance driving expert instructions. Participants were divided into four (4) groups to assess two (2) dimensions of the AI coach's explanations: information type ('what' and 'why'-type explanations) and presentation modality (auditory and visual). We compare how different explanatory techniques impact driving performance, cognitive load, confidence, expertise, and trust via observational learning. Through interview, we delineate participant learning processes. Results show AI coaching can effectively teach performance driving skills to novices. We find the type and modality of information influences performance outcomes. Differences in how successfully participants learned are attributed to how information directs attention, mitigates uncertainty, and influences overload experienced by participants. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, results support the need to align communications with human learning and cognitive processes. We provide eight design implications for future autonomous vehicle HMI and AI coach design.

Impact of cognitive impairment on driving behaviour and route choices of older drivers: a real-world driving study.

Maintaining driving independence is important for older adults. However, cognitive decline, a common issue in older populations, can impair older adults' driving abilities and overall safety on the roads. This study explores how cognitive impairment influences driving patterns and driving choices among older adults. We analyzed real-world driving patterns of 246 older adults using GPS dataloggers. Our sample included 230 cognitively normal older adults (CN; Clinical Dementia Rating R [CDR] = 0) and 16 older adults with incident cognitive impairment (ICI; CDR = 0.5). The CN group had an average age of 68.2 years, with 46% females and an average of 16.5 years of education, while the ICI group's average age was 69.2 years, with 36% females and an average of 16.0 years of education. We employed spatial clustering and hashing algorithms to evaluate driving behaviours. Significant differences emerged: The ICI group used fewer distinct routes to their most common destination. These differences can be leveraged to develop driving as a digital biomarker for the early detection and continuous monitoring of cognitive impairment.

The Role of Parenting Behaviors and Their Influence on Adolescent Drunk and Drugged Driving: 2016-2019, USA.

Drugged driving, the act of driving a vehicle under the influence of illicit drugs, by adolescents is a serious public health concern. Many factors contribute to this risk behavior, but much less is known regarding the role of parenting behaviors in this phenomenon. The purpose of this study was to examine specific parenting behaviors and their influence among a nationally representative sample of adolescents. Pooled data from the 2016-2019 National Survey on Drug Use and Health (NSDUH) among 17,520 adolescents ages 16-17 years old were analyzed. Differences were found in specific parenting behaviors and adolescent drugged/drunk driving, with parents not checking homework and not telling their children they are proud of them being the most influential. Findings from the present study may inform drugged driving prevention programs for parents and adolescents and enhance road safety interventions.

Trait anger as a predictor of dangerous driving behaviour amongst people who use methamphetamine.

INTRODUCTION: Despite increased prevalence of methamphetamine in road trauma, it remains unclear how its use translates to an increased risk of traffic-related harm. Exploration of psychosocial factors may thus help identify relevant predictors of dangerous driving behaviour among people who regularly consume methamphetamine. METHODS: Licenced individuals who report predominant and sustained methamphetamine use (at least 1-time/month for 6 months at heaviest use) were recruited from the Australian community and via targeted campaign (Eastern Health). Psychosocial, substance use and driving behaviour data (Dula Dangerous Driving Index, DDDI) were collected via a secure anonymous online forced-entry survey platform (Qualtrics). RESULTS: Seventy-seven individuals (65.5 % male) aged between 20-50 years [mean = 29.7, ± Standard Deviation (SD) 6.1] were included. Most (90 %) respondents met criteria for problematic methamphetamine use [Severity of Dependency Scale (SDS) score ≥ 5], and 75 % were high-risk alcohol consumers [Alcohol Use Disorders Identification Test (AUDIT-C) score ≥ 4 for men and ≥ 3 for women]. On average, age of first methamphetamine use occurred at 23.3 years (±5.2). A best-possible subset's regression selection method with dangerous driving behaviour as the dependent variable determined the model with three predictors (alcohol use, substance dependence severity and trait anger) as most parsimonious. After controlling for substance use, trait anger strongly and positively predicted dangerous driving behaviour as measured by the DDDI ([F(3,74) = 26.06, p < .001, adjusted R2 = 0.50, Cohens f2 = 0.42). DISCUSSION AND CONCLUSIONS: Trait anger is a strong predictor of risky driving among road users who use methamphetamine. Interactions between stable negative-emotional and situational traffic and driving-related factors may increase risk of harm through greater engagement in risk-taking behaviour.

Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data.

Early warning of driving risks can effectively prevent collisions. However, numerous studies that predicted driving risks have suffered from the use of single data sources, insufficiently advanced models, and lack of time window analysis. To address these issues, this paper proposes a self-attention-based bidirectional long short-term memory (Att-Bi-LSTM) network model to predict driving risk based on multi-source data. First, driving simulation tests are conducted. Driver demographic, operation, visual, and physiological data as well as kinematic data are collected. Then, the driving risks are classified into no risk, low risk, medium risk, and high risk. Next, the Att-Bi-LSTM model is constructed, and convolutional neural network (CNN), CNN-LSTM, CatBoost, LightGBM, and XGBoost are employed for comparison. To generate the inputs and outputs of the models, observation, interval, and prediction time windows are introduced. The results show that the Att-Bi-LSTM model using early-fusion method significantly outperforms the five comparison models, with a macro-average F1-score of 0.914. The results of ablation studies indicate that the Bi-LSTM layers and self-attention layer have achieved the expected effect, which is crucial for improving the model's performance. As the interval or prediction time window is extended, the accuracy of the prediction results gradually decreases. However, as the observation time window is extended, the results first improve and then become stable. Compared to using only relative kinematic data, using all data (i.e., multi-source data) is shown to improve the F1-score by 0.061. This study provides an effective method for driving risk prediction and supports the improvement of advanced driver assistance systems.

Targeted nudging for speeding behavior: The influence of interpersonal characteristics on responses to in-vehicle road nudges.

Road carnage is one of the most fatal and expensive global issues today. Many solutions have been implemented to minimize it, but most are costly and unreliable. Therefore, in this study, nudges were used as a reliable and inexpensive tool to affect safe driving behavior which, in turn, may reduce road fatalities. To optimize the use of nudges, we suggested that responses to nudges - in a similar manner to responses to other stimuli - may vary by interpersonal characteristics, so that different nudges may lead to more accurate and reliable reactions in different sub-populations in a predictable manner. To test these assertions, we collected a sample of 200 participants, both men and women, ages 17.5 to 83 years. We measured different interpersonal characteristics that included both demographic information (e.g., age, gender, years with a driver's license) and different personality traits. We then assessed responses to nudges using a simulator that was specially designed for this study, in which participants are asked to adjust their speed as they see fit while they watched a video shot from a driver's perspective of the forward roadway. Over the course of the video, a different nudge was displayed for each subject and their response latency and speeds were recorded for further analysis. We were able to observe several interesting phenomena: responses to a reminder nudge and a negative reinforcement nudge were faster than responses to a social norm nudge. However, the latter showed a longer-term impact. The responses to the social norm interventions were also more variable, demonstrating that high neuroticism is linked to decreased response to social norm nudges, a picture that is repeated in men compared to women. Contrarily, conscientiousness was linked to a faster and more reliable response to the social norm nudge, and the gender effect was eliminated for men with high conscientiousness. Moreover, parenthood was found to increase the response to all nudges and was protective against the effects of high sensation-seeking, which led to more road violations. These findings may be tested using modern technology, which can facilitate the measurements of personal traits and verify the reliability of responses to nudges. Therefore, the current study suggests nudge personalization may be beneficial in improving the use of nudges on the road.

Predicting driver's takeover time based on individual characteristics, external environment, and situation awareness.

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_R2) 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.

Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment.

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human-machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers' responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers' gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

A game-theoretic approach for modelling pedestrian-vehicle conflict resolutions in uncontrolled traffic environments.

The interactions of motorised vehicles with pedestrians have always been a concern in traffic safety. The major threat to pedestrians comes from the high level of interactions imposed in uncontrolled traffic environments, where road users have to compete over the right of way. In the absence of traffic management and control systems in such traffic environments, road users have to negotiate the right of way while avoiding conflict. Furthermore, the high level of movement freedom and agility of pedestrians, as one of the interactive parties, can lead to exposing unpredictable behaviour on the road. Traffic interactions in uncontrolled mixed traffic environments will become more challenging by fully/partially automated driving systems' deployment, where the intentions and decisions of interacting agents must be predicted/detected to avoid conflict and improve traffic safety and efficiency. This study aims to formulate a game-theoretic approach to model pedestrian interactions with passenger cars and light vehicles (two-wheel and three-wheel vehicles) in uncontrolled traffic settings. The proposed models employ the most influencing factors in the road user's decision and choice of strategy to predict their movements and conflict resolution strategies in traffic interactions. The models are applied to two data sets of video recordings collected in a shared space in Hamburg and a mid-block crossing area in Surat, India, including the interactions of pedestrians with passenger cars and light vehicles, respectively. The models are calibrated using the identified conflicts between users and their conflict resolution strategies in the data sets. The proposed models indicate satisfactory performances considering the stochastic behaviour of road users - particularly in the mid-block crossing area in India - and have the potential to be used as a behavioural model for automated driving systems.

The effect of human-machine interface modality, specificity, and timing on driver performance and behavior while using vehicle automation.

The effectiveness of the human-machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers' takeover time, performance, and eye glance behavior. Drivers' behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, or other eye glance metrics (e.g., on road glance duration). Future work should disentangle HMI design further to determine exactly which aspects of design yield between safety critical behavior.

Greater prosociality toward other human drivers than autonomous vehicles: Human drivers' discriminatory behavior in mixed traffic.

The development of autonomous vehicles (AVs) has rapidly evolved in recent years, aiming to gradually replace humans in driving tasks. However, road traffic is a complex environment involving numerous social interactions. As new road users, AVs may encounter different interactive situations from those of human drivers. This study therefore investigates whether human drivers show distinct degrees of prosociality toward AVs or other human drivers and whether AV behavioral patterns exert a relevant influence. Sixty-two drivers participated in the driving simulation experiment and interacted with other human drivers and different kinds of AVs (conservative, human-like, aggressive). The results show that human drivers are more willing to yield to other human drivers than to all kinds of AVs. Their braking reaction time is longer when yielding to AVs and their distance to AVs is shorter when choosing not to yield. AVs of different behavioral patterns do not significantly differ in yielding rate, but the braking reaction time of human-like AVs is longer than conservative AVs and shorter than aggressive AVs. These findings suggest that human drivers show more prosocial behaviors toward other human drivers than toward AVs. And human drivers' yielding behavior changes as the behavioral patterns of AVs changes. Accordingly, this study improves the understanding of how human drivers interact with nonliving road users such as AVs and how the former accept AVs with different driving styles on the road.

Coupling intention and actions of vehicle-pedestrian interaction: A virtual reality experiment study.

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian's intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning.