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TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment
arXiv - CS - Robotics Pub Date : 2020-09-23 , DOI: arxiv-2009.11345 Runxin He, Jinyun Zhou, Shu Jiang, Yu Wang, Jiaming Tao, Shiyu Song, Jiangtao Hu, Jinghao Miao, Qi Luo
arXiv - CS - Robotics Pub Date : 2020-09-23 , DOI: arxiv-2009.11345 Runxin He, Jinyun Zhou, Shu Jiang, Yu Wang, Jiaming Tao, Shiyu Song, Jiangtao Hu, Jinghao Miao, Qi Luo
This paper presents an optimization-based collision avoidance trajectory
generation method for autonomous driving in free-space environments, with
enhanced robust-ness, driving comfort and efficiency. Starting from the hybrid
optimization-based framework, we introduces two warm start methods, temporal
and dual variable warm starts, to improve the efficiency. We also reformulates
the problem to improve the robustness and efficiency. We name this new
algorithm TDR-OBCA. With these changes, compared with original hybrid
optimization we achieve a 96.67% failure rate decrease with respect to initial
conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in
planner efficiency as obstacles number scales. We validate our results in
hundreds of simulation scenarios and hundreds of hours of public road tests in
both U.S. and China. Our source code is
availableathttps://github.com/ApolloAuto/apollo.
更新日期：2020-09-25
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