A_General_Autonomous_Driving_Planner_Adaptive_to_Scenario_Characteristics.pdf (2.43 MB)

A General Autonomous Driving Planner Adaptive to Scenario Characteristics

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posted on 2022-05-17, 17:26 authored by Xinyu JiaoXinyu Jiao, Zhong cao, Junjie Chen, Kun Jiang, Diange Yang
Autonomous vehicle requires a general planner for all possible scenarios. Existing researches design such a planner by a unified scenario description.
However, it may significantly increase the planner complexity even in some simple tasks, e.g., car following, further resulting in unsatisfactory driving performance. This work aims to design a general planner which can 1) drive in all possible scenarios and 2) have lower complexity in some common scenarios.
To this end, this work proposes a pertinent boundary for multi-scenario driving planning. The total approach is named as Pertinent Boundary-based Unified Decision system. Based on the original drivable area, the pertinent boundary can further support motion status and semantics of the traffic elements, which provides the potential of pertinent performance for given scenarios. The pertinent boundary can support unified driving with the drivable area, in the meantime, can be pertinently modified to support the pertinent driving decisions for identified driving scenarios (e.g., car-following, junction left turning). It will further avoid the bump between the connections of the scenarios due to the continuity of space boundary. Thus, the planner is suitable for the fully autonomous driving. The proposed method is validated in different classical driving decision scenarios. Results show that the proposed method can support pertinent driving decisions in identified scenarios, in the meantime, assure generalized cross-scenario planning when no scenario information is available. Such a method shed light on fully autonomous driving by pertinence improvement of multi-scenario decision in the complex real world.


National Natural Science Foundation of China U1864203

National Natural Science Foundation of China 52102396

National Natural Science Foundation of China 52102460


Email Address of Submitting Author

Submitting Author's Institution

Tsinghua University

Submitting Author's Country

  • China

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