The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas.
Evaluating Autonomous Vehicle Safety Countermeasures in Freeways Under Sun Glare
2025
Article (Journal)
Electronic Resource
Unknown
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