Optimizing the Lateral Wandering of Automated Vehicles to Improve Roadway Safety and Pavement Life
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Because most automated vehicles (AVs) are programmed to follow a set path and maintain a lateral position in thecenter of the lane, over time significant pavement rutting will occur. This study directly measured AV lateralwandering patterns. It was found that the wandering patterns of both AVs and human-driven vehicles could bemodeled with a normal distribution but have significantly different standard deviations, with AV lateral wanderingbeing at least 3 times smaller than the wandering of human-driven vehicles. Modeling with the TexasMechanistic-Empirical Flexible Pavement Design System (TxME) found that the AVs with smaller lateralwandering would shorten pavement fatigue life by 22 percent and increase pavement rut depth by 30 percent,which leads to a much higher risk of hydroplaning. Researchers also calculated the maximum tolerable rut depthsat different hydroplaning speeds. AVs have a much smaller tolerable rut depth than human-driven vehicles due togreater water film thickness in the rutted wheel paths. To reduce the negative impact of AVs on roadway safetyand pavement life, this research recommends an optimal AV wandering pattern, a uniform distribution, whichresults in prolonged pavement life and decreased hydroplaning potential.