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1. Crash rates over time among younger and older drivers in the SHRP 2 naturalistic driving study

   https://doi.org/2020.03.001  

   Bruce G. Simons-Morton, Pnina Gershon (June 2020, Journal of safety research)

   null (Ed.)

   To examine crash rates over time among 16–17-year-old drivers compared to older drivers. Methods: Data were from a random sample of 854 of the 3,500 study participants in SHRP 2, a U.S.national, naturalistic driving (instrumented vehicle) study. Crashes/10,000 miles by driver age group, 3-month period, and sex were examined within generalized linear mixed models. Results: Analyses of individual differences between age cohorts indicated higher incidence rates in the 16–17-year old cohort relative to older age groups each of the first four quarters (except the first quarter compared to 18 – 20 year old drivers) with incident rate ratios (IRR) ranging from 1.98 to 18.90, and for the full study period compared with drivers 18–20 (IRR = 1.69, CI = 1.00, 2.86), 21 to 25 (IRR = 2.27, CI = 1.31, 3.91), and 35 to 55 (IRR = 4.00, CI = 2.28, 7.03). Within the 16–17-year old cohort no differences were found in rates among males and females and the decline in rates over the 24-month study period was not significant. Conclusions: The prolonged period of elevated crash rates suggests the need to enhance novice young driver prevention approaches such as Graduated Driver’s Licensing limits, parent restrictions, and postlicensure supervision and monitoring. Practical Applications: Increases are needed in Graduated Driver’s Licensing limits, parent restrictions, and postlicensure supervision and monitoring. 

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2. Investigating Traffic Crashes Involving Autonomous Vehicles

   Torres, Jesus (May 2021, IISE Annual Conference Proceedings)

   A. Ghate, K. Krishnaiyer (Ed.)

   Deaths due to road traffic accidents are one of the leading causes of death in the United States. Furthermore, the economic impact of road traffic accidents accounts for about 3% of the United States' annual gross domestic product (GDP). In the past decade, extensive research has focused on autonomous vehicles (AVs). This technology is said to help prevent traffic accidents while promoting road traffic safety. This study aims to investigate the safety performance of AVs and identify the significant risk factors associated with the AV collisions. The study considers more than 200 crashes involving AVs and includes vehicle factors, environmental factors, collision type and crash severity. Multinomial logistic regression was conducted with collision type. The results showed no statistically significant risk factors to crash severity. However, movement preceding to collision contributes significantly to collision type. When both vehicles are moving, there's a higher likelihood of an angled collision, 47% to be exact. The other scenario which demonstrates a high probability of an angled collision is when the AV vehicle is not moving while the other is moving. The highest probability for a rear-end collision to occur is when the AV vehicle is not moving while the other is moving. This scenario makes up 55% of the entire rear-end collisions. As for the second-highest proportion, both vehicles moving, it consists of 42%. The research shall help reduce AV involved collisions and increase driving safety. 

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3. The Future Driver: Exploring the Safety Challenges of Level 4 Automated Vehicles During Manual Control

   https://doi.org/10.1177/03611981251333713  

   Andriola, Cesar; Chitturi, Madhav V; Noyce, David A (August 2025, Transportation Research Record: Journal of the Transportation Research Board)

   Level 4 automated vehicles (AVs) with the operational design domain (ODD) expanding over time are expected to be the future. Although Level 4 AVs do not require driver takeover, human driving will be necessary outside the ODD. While there is a significant amount of research on takeover/disengagement, no prior studies have explored the safety challenges of manual operation of Level 4 AVs. Crash sequence analysis was employed to compare crashes of the AV (during manual control) (AVM) and general driving population, using U.S. data from California Department of Motor Vehicles crash reports and the Crash Report Sampling System (CRSS) dataset, respectively. Clusters of AVM and CRSS crashes were aggregated into nine groups based on crash context. The results suggest that certain crash groups are more challenging for AVM than for CRSS. AVM crashes are vastly less severe than CRSS crashes for all but one crash group that involved right turns. Nearly half of the AVM crashes involving left and right turns were rear-end crashes, while the majority of similar CRSS crashes were side-swipe or angle. The majority of rear-end AVM crashes occur at intersections, while the converse is true for similar CRSS crashes. Intriguingly, in all the AVM rear-end crashes, the lead vehicle was an AV, suggesting hesitation on the part of the safety driver. For AVM, while lane-changing crashes were less frequent, crashes involving parked vehicles were more frequent than for CRSS. The findings indicate the importance of understanding how driver behavior changes with Level 4 AVs, and how driver training might play an important role in the safety of AVs. 

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4. Legal Framework for Rear-End Crashes in Mixed-Traffic Platooning: A Matrix Game Approach

   https://doi.org/10.3390/futuretransp3020025  

   Chen, Xu; Di, Xuan (June 2023, Future Transportation)

   Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon. 

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5. Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation

   https://doi.org/10.3390/ai5040090  

   Owor, Neema Jakisa; Adu-Gyamfi, Yaw; Zhang, Linlin; Sun, Carlos (December 2024, AI)

   Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety. 

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