Sambhu, Neil (University of South Florida), Katkoori, Srinivas (University of South Florida)

Autonomous-Vehicle Path Following in Simulation

Scheduled for presentation during the Regular Session "Driving behavior models" (ThBT6), Thursday, September 26, 2024, 15:10−15:30, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

Autonomous race driving is a difficult problem, as the driving algorithm needs to find unique maneuvers on the track to achieve the fastest lap time. To date and to the best of our knowledge, there exists no algorithm that supersedes human performance. In this work, we propose an analytical self-driving algorithm for race driving. The self-driving algorithm will use a baseline (i.e., generated a priori by manually driving on the track) for course correction while attempting to achieve the shortest lap time. The proposed algorithm iteratively determines the steer angle, throttle, and brake controls while adhering as close to the baseline, by computing deviations between (1) the predicted location at the next time step and (2) the baseline location closest to predicted. We report results for various fixed speeds to demonstrate the correctness of the algorithm. We then demonstrate more realistic adaptive driving that greedily increases the speed whenever the steering correction is small. The proposed algorithm is implemented and validated in the open-source CARLA Simulator v0.9.14. Experimental results for three different car models demonstrate that the adaptive algorithm is effective in achieving good lap times.