As the advanced driver assistance system (ADAS) functions become more sophisticated, the strategies that properly coordinate interaction and communication among the ADAS functions are required for autonomous driving. This paper proposes a derivative-free optimization based imitation learning method for the decision maker that coordinates the proper ADAS functions. The proposed method is able to make decisions in multi-lane highways timely with the LIDAR data. The simulation-based evaluation verifies that the proposed method presents desired performance.
@article{arxiv.2109.05197,
title = {Adversarial Imitation Learning via Random Search in Lane Change Decision-Making},
author = {Myungjae Shin and Joongheon Kim},
journal= {arXiv preprint arXiv:2109.05197},
year = {2021}
}
Comments
Note that this framework is accepted to be published in proceedings of IJCNN 2019 and IJCAI 2019