Computational study demonstrates reduced driver sun glare exposure via reinforcement learning route optimization, indicating trade-offs between travel distance and safety.
Sun glare during driving poses a significant threat to driver and pedestrian safety. Navigation and route planning typically seeks to minimize the distance or time between the desired origin and destination, accounting for traffic patterns and other heuristics like minimizing the number of stoplights or left turns encountered on a route. However, current navigation methods do not support avoidance of complicated, temporally-dependent safety factors, like adverse road and environmental conditions. We take avoiding incident sun glare to the driver as an example of dynamic safety-aware navigation and lay out potential strategies for addressing this previously unexplored problem. We present a reinforcement learning-based method for computing sun glare-low routes through an elastic function that accounts for the direct angle between the sun and the driving direction. Our preliminary work shows that in some cases it is possible to reduce the sun glare exposure on a route by trading off additional travel distance. We envision future safety-aware navigation approaches that can automatically balance this trade-off and account for additional dynamic spatially and temporally-dependent safety-related environmental factors, like road and weather conditions, to determine the safest and most efficient route between any two given points.
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Das et al. (2024) studied this question.
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