作者
Tim Puphal, Raphael Wenzel, Benedict Flade, Malte Probst, Julian Eggert
发表日期
2022/11/18
研讨会论文
2022 7th International Conference on Robotics and Automation Engineering (ICRAE)
页码范围
376-383
出版商
IEEE
简介
Self-driving cars face complex driving situations with a large amount of agents when moving in crowded cities. However, some of the agents are actually not influencing the behavior of the self-driving car. Filtering out unimportant agents would inherently simplify the behavior or motion planning task for the system. The planning system can then focus on fewer agents to find optimal behavior solutions for the ego agent. This is helpful especially in terms of computational efficiency. In this paper, therefore, the research topic of importance filtering with driving risk models is introduced. We give an overview of state-of-the-art risk models and present newly adapted risk models for filtering. Their capability to filter out surrounding unimportant agents is compared in a large-scale experiment. As it turns out, the novel trajectory distance balances performance, robustness and efficiency well. Based on the results, we can further …
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T Puphal, R Wenzel, B Flade, M Probst, J Eggert - 2022 7th International Conference on Robotics and …, 2022