Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

A multimodal physiological dataset for driving behaviour analysis.

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.

Rapid Return to Braking After Anterior and Posterior Approach Total Hip Arthroplasty.

BACKGROUND: Little is known about the effect of surgical approach on return to braking after total hip arthroplasty (THA), and few studies have investigated braking after THA with modern surgical techniques and rehabilitation protocols. METHODS: In a prospective comparative design, we enrolled 65 patients who received right-sided primary THA at our institution from April 2018 through March 2020, 34 with a direct anterior approach (DAA) and 31 with a posterior approach (PA). Braking tests measuring brake reaction time (BRT) and brake pedal depression (BPD) were administered to patients preoperatively and at 1, 2, and 4 weeks postoperatively using a realistic driving simulator. BRT and BPD were compared between groups and preoperatively versus postoperatively using mixed-effects models. RESULTS: Preoperative BRT averaged 638 msec in the DAA group and 604 msec in the PA group (P = 0.31). At 1 week postoperatively, the DAA group had significantly prolonged BRT compared with preoperatively (694 msec, P = 0.02). No significant difference was observed in the PA group (633 msec, P = 0.31). Both groups had returned to baseline by 2 weeks, and both had significantly faster BRT at 4 weeks compared with preoperatively (583 msec for DAA, P = 0.01; 537 msec for PA, P < 0.001). BPD was similar between groups, and there were no significant differences between preoperative and postoperative BPD at any time point. CONCLUSIONS: With modern surgical techniques, BRT after right-sided THA returns to baseline levels approximately 2 weeks after surgery. There seems to be a quicker return to preoperative BRT observed in patients with a PA.

A method to enhance drivers' hazard perception at night based on "knowledge-attitude-practice" theory.

During nighttime driving, the inherent challenges of low-illuminance conditions often lead to an increased crash rate and higher fatalities by impairing drivers' ability to recognize imminent hazards. While the severity of this issue is widely recognized, a significant research void exists with regard to strategies to enhance hazard perception under such circumstances. To address this lacuna, our study examined the potential of an intervention grounded in the knowledge-attitude-practice (KAP) framework to bolster nighttime hazard detection among drivers. We engaged a cohort of sixty drivers split randomly into an intervention group (undergoing specialized training) and a control group and employed a holistic assessment that combined eye movement analytics, physiological response monitoring, and driving performance evaluations during simulated scenarios pre- and post-intervention. The data showed that the KAP-centric intervention honed drivers' visual search techniques during nighttime driving, allowing them to confront potential threats with reduced physiological tension and ensuring more adept vehicle handling. These compelling findings support the integration of this methodology in driver training curricula and present an innovative strategy to enhance road safety during nighttime journeys.

Risk field modeling of urban tunnel based on APF.

OBJECTIVE: The purpose of this paper is to explore the changing laws of driving safety in the complex and changing driving environment in urban tunnels, to analyze the evolution of driving risk fields caused by changes in adjacent vehicles, driving behavior characteristics and road environment, and to reveal the formation mechanism of tunnel driving danger zones. METHODS: The kinetic field, behavioral field and potential field models are constructed according to the APF theory. The driving safety risks arising from the surrounding vehicles, driving behavior characteristics and changes in the tunnel environment are analyzed in the process of driving from the open section to the exit of the tunnel. RESULTS: The magnitude of the risk field force is inversely proportional to the spacing of the vehicles and the distance between the tunnel sidewalls, and is proportional to the relative speed between the vehicles and the slope of the longitudinal slope. Under the same conditions, the vehicle at the entrance and exit of the tunnel is subjected to a greater force of travel risk than inside the tunnel, and the effect of speed on the force of the risk field is greater than the distance. CONCLUSIONS: The established model better describes the trend of driving risk during the driving of vehicles in urban tunnels, and the research findings can provide theoretical support for the design and traffic management of urban tunnels.

Energy field-based lane changing behavior interaction model and risk evaluation in the weaving section of expressway.

OBJECTIVE: With the development of intelligent driving assistance systems, the evaluation of driving behavior risk has shifted from traditional single-vehicle studies to multi-vehicle studies. This study aimed to investigate the interaction mechanism between vehicles and to study the microscopic laws of traffic flow operation. METHODS: Firstly, the concept of "driving interaction field" was proposed. The virtual interaction quality and distance were used to define the driving interaction field. The interaction angle distinguished the vehicle interaction between different lanes. Then, the risk mechanism in the interaction process was analyzed by driving risk index. Corresponding thresholds of 50% and 85% quantile values were determined. Finally, the process of the lane-changing simulation experiments was divided into three phases (preparation, execution and adjustment). RESULTS: The driving risk index of the execution phase was larger than the other phases. Meanwhile, the comparison with the classical driving risk indexes revealed that the proposed index was more accurate and intuitive in describing the interaction risks. CONCLUSIONS: The driving interaction model proposed in this study quantified the overall environmental pressure on the vehicle. It overcomes the previous limitation of kinetic interaction parameters. The research provides a new idea for the ITS and autonomous driving systems, contributing to the enhancement of traffic safety and efficiency.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Behind the wheel: Probing into personality, skills, and driving behavior's role in bus rapid transit crashes.

OBJECTIVE: Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers. METHODS: The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method. RESULT: The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving. CONCLUSION: The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.

A systematic review of the use of in-vehicle telematics in monitoring driving behaviours.

BACKGROUND: Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed. METHODOLOGY: Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of "good" in the quality assessment. RESULTS: We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review. CONCLUSION: We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.

A multimodal driver monitoring benchmark dataset for driver modeling in assisted driving automation.

In driver monitoring various data types are collected from drivers and used for interpreting, modeling, and predicting driver behavior, and designing interactions. Aim of this contribution is to introduce manD 1.0, a multimodal dataset that can be used as a benchmark for driver monitoring in the context of automated driving. manD is the short form of human dimension in automated driving. manD 1.0 refers to a dataset that contains data from multiple driver monitoring sensors collected from 50 participants, gender-balanced, aged between 21 to 65 years. They drove through five different driving scenarios in a static driving simulator under controlled laboratory conditions. The automation level (SAE International, Standard J3016) ranged from SAE L0 (no automation, manual) to SAE L3 (conditional automation, temporal). To capture data reflecting various mental and physical states of the subjects, the scenarios encompassed a range of distinct driving events and conditions. manD 1.0 includes environmental data such as traffic and weather conditions, vehicle data like the SAE level and driving parameters, and driver state that covers physiology, body movements, activities, gaze, and facial information, all synchronized. This dataset supports applications like data-driven modeling, prediction of driver reactions, crafting of interaction strategies, and research into motion sickness.

Exploring the associations of demographics and scale measures with cognitive driving behavior among older drivers in China.

Age-related changes and frailty are reasons for the high proportion of older drivers in certain types of crashes, such as giving right of way at intersections and turning left. The identified crash causes include the driver's demographics, driving style, cognitive function, and mental workload. This study aimed to explore the associations of demographics and scale measures with cognitive driving behavior. Thirty-nine drivers, consisting of twenty younger drivers (18-60 years old) and nineteen older drivers (above 60 years old), participated in driving simulation experiments after completing scale tests. The selected scale measures included the demographic questionnaire, Multidimensional Driving Style Inventory (MDSI-C), Mini-Mental State Examination (MMSE), Trail Making Test Part A (TMT-A) and Part B (TMT-B), and the National Aeronautics and Space Administration Task Load Index (NASA-TLX) for obtaining subjective information from drivers. Driving scenarios were developed based on the driving characteristics of older adults to investigate age-related driving ability. The driving behavior parameters included reaction time, lateral stability, and driving speed, corresponding to reaction, perception, and execution. Three stepwise regression models showed that NASA-TLX, the interaction between age and driving experience, and the interaction between age and TMT-A significantly explained 53.3 % of reaction time variance; TMT-A, risk driving style, anxiety driving style, and gender significantly explained 53.5 % of lateral stability variance; TMT-A, NASA-TLX, and MMSE significantly explained 60.6 % of driving speed variance. Subsequently, the impact of four age-related predictor variables on driving behavior was further discussed. It is worth noting that a rich driving experience may compensate for driving performance. However cognitive impairment impairs this compensation. Driving behavior is influenced by a combination of various factors. Age, as a physiological indicator, is not sufficient to be a strong predictive factor for lateral stability and driving speed. The results provide a reference for traffic safety management departments to streamline driving suitability test procedures and propose targeted training methods for older drivers.

Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach.

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the "Behavior of Young Novice Drivers Scale" (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

Assessing the collision risk of mixed lane-changing traffic in the urban inter-tunnel weaving section using extreme value theory.

Urban inter-tunnel weaving (UIW) sections are characterized by short lengths and frequent lane-changing behaviors in the area, commonly used for fast through traffic. These features increase the likelihood of collisions, however, collision risk assessment in this area has been inadequate. The aim of this study was to evaluate the potential collision risk of urban inter-tunnel weaving (UIW) sections in mixed lane-changing traffic conditions in morning rush hours, utilizing surrogate safety measures. The investigation involved the collection of trajectory data via an unmanned aerial vehicle (UAV). Time to collision (TTC) and extended time to collision (ETTC) were chosen as surrogate safety indicators. The estimation of collision risk was conducted using Extreme Value Theory (EVT) by means ofsurrogate safety indicators. It was found that the threshold of TTC and ETTC in this area was 1.25 s. Furthermore, a comprehensive evaluation of collision risks associated with various vehicle types was performed, revealing an inverse relationship between thecollisions riskof vehicles in mixed traffic and their size. It was worth noting that while heavy vehicles exhibit a lower collision risk, they resulted in the highest energy loss and inflicted greater harm in the event of a collision. By an examination of the distribution features pertaining to conflict types during the operation of heavy vehicles, it showed that the highest likelihood of conflict with heavy vehicles occurred when adjacent lanes are involved. Consequently, the implementation of assisted driving technology for heavy vehicles was imperative in order to mitigate the risk associated with side collisions.

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.

Enhancing intersection safety in autonomous traffic: A grid-based approach with risk quantification.

Existing studies on autonomous intersection management (AIM) primarily focus on traffic efficiency, often overlooking the overall intersection safety, where conflict separation is simplified and traffic conflicts are inadequately assessed. In this paper, we introduce a calculation method for the grid-based Post Encroachment Time (PET) and the total kinetic energy change before and after collisions. The improved grid-based PET metric provides a more accurate estimation of collision probability, and the total kinetic energy change serves as a precise measure of collision severity. Consequently, we establish the Grid-Based Conflict Index (GBCI) to systematically quantify collision risks between vehicles at an autonomous intersection. Then, we propose a traffic-safety-based AIM model aimed at minimizing the weighted sum of total delay and conflict risk at the intersection. This entails the optimization of entry time and trajectory for each vehicle within the intersection, achieving traffic control that prioritizes overall intersection safety. Our results demonstrate that GBCI effectively assesses conflict risks within the intersection, and the proposed AIM model significantly reduces conflict risks between vehicles and enhances traffic safety while ensuring intersection efficiency.

Development and classification of autonomous vehicle's ambiguous driving scenario.

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

On the impact of on-road partially-automated driving on drivers' cognitive workload and attention allocation.

The use of partially-automated or SAE level-2 vehicles is expected to change the role of the human driver from operator to supervisor, which may have an effect on the driver's workload and visual attention. In this study, 30 Ontario drivers operated a vehicle in manual and partially-automated mode. Cognitive workload was measured by means of the Detection Response Task, and visual attention was measured by means of coding glances on and off the forward roadway. No difference in cognitive workload was found between driving modes. However, drivers spent less time glancing at the forward roadway, and more time glancing at the vehicle's touchscreen. These data add to our knowledge of how vehicle automation affects cognitive workload and attention allocation, and show potential safety risks associated with the adoption of partially-automated driving.

Lateral safety evaluation of autonomous trucks in overtaking maneuvers and adverse weather: Exploratory comparison of different lateral control modes.

As the detrimental impact of the commonly recommended centered driving mode for autonomous trucks on road longevity is gaining attention, more lateral control modes are being proposed to enhance road sustainability. However, there is currently a lack of research on the lateral safety analysis of autonomous trucks with different lateral control modes, especially in complex driving scenarios (such as overtaking) and adverse weather conditions. Therefore, this study developed a safety assessment framework to comparatively analyze the risk probability differences in lateral accidents during overtaking maneuvers by autonomous trucks with different lateral control modes under adverse weather conditions. Based on aerodynamics and vehicle dynamics simulations to capture the multifactorial influences on truck lateral deviation, the results are used for model validation and training. In the reliability approach, Support Vector Machine Regression (SVR) is introduced to establish the SVR response surface model with optimal predictive performance, and combined with Monte Carlo simulations for safety assessment, quantifying safety indices. The results indicate that trucks being overtaken during overtaking maneuvers are more prone to lateral accidents under crosswind influences. The overall impact of lateral control modes on the lateral safety trends is minor. Compared to other lateral control modes, following the centered zero-drift mode is generally safer. However, in conditions of low wind speeds (below 20 km/h) or on highly slippery road surfaces (road friction coefficient below 0.1), autonomous trucks following a uniform distribution mode can better maintain a low-risk level. This study provides crucial insights for future considerations integrating road longevity and truck safety in a collaborative manner, and the proposed methodology has broad applications.

Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.

Crash risk estimation of Heavy Commercial vehicles on horizontal curves in mountainous terrain using proactive safety method.

Heavy commercial vehicles (HCVs) face elevated crash risks in mountainous terrains due to the challenging topography and intricate geometry, posing a significant challenge for transportation agencies in mitigating these risks. While safety studies in such terrains traditionally rely on historical crash data, the inherent issues associated with crash data have led to a shift towards proactive safety studies using surrogate safety measures (SSM) in recent years. However, the scarcity of accurate microscopic data related to HCV drivers has limited the application of proactive safety studies in mountainous terrains. This study addresses this gap by employing an SSM known as anticipated collision time (ACT) to explore the impact of horizontal curves on the crash risk of HCVs in mountainous terrain. To perform the crash risk analysis, a collection of videos was gathered from horizontal curves in the mountainous terrain along the Guwahati-Shillong bypass in the Northeastern region of India. Subsequently, trajectories were extracted from these videos using semi-automated image processing software. Traffic conflicts were identified using ACT, and the crash risk was estimated through the Peak-Over Threshold (POT) approach of the Extreme Value Theory (EVT). The findings indicate that Run-Off-Road (ROR) traffic events happen more frequently on or near the horizontal curves falling in mountainous terrain. However, the frequency of severe ROR traffic events is lower, indicating the lower propensity for such collisions on the selected curves. The threshold for the safety margin of ROR traffic events involving HCVs was 2 s. The study revealed that stationary models exhibit an overestimation of crash frequency (0, 6) compared to the observed crash frequency (0, 2). Consequently, non-stationary crash risk models were developed, incorporating road geometry and the braking and yaw rates of HCVs as covariates. The results demonstrate that the estimated confidence bounds (1, 2) align with the observed crash frequency (0, 2), emphasizing the applicability of POT models for safety analysis in mountainous terrains in India. The study identified curve radius, length of the approach tangent, and the distance between the center points of horizontal and vertical curves as influential factors affecting the Run-Off-Road (ROR) crash risk of HCVs. Notably, sharp curves with radii less than 200 m or more are associated with a significantly higher crash risk. Additionally, an increased distance between the midpoints of horizontal and vertical curves beyond 1 m was found to escalate the ROR crash risk of HCVs. To mitigate these risks, it is recommended to reduce the length of the approach tangent to prevent high-speed travel on sharp curves. Furthermore, proper signage should be strategically placed to warn drivers and avert potential hazards.