POMDP Motion Planning Algorithm Based on Multi-Modal Driving Intention

Lin Li

doi:10.1109/TIV.2022.3209926

2023-02-01

PRE

Lin Li

赵

赵万忠 (Wanzhong Zhao) *

王

王春燕 (Chunyan Wang)

DOI：10.1109/TIV.2022.3209926

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摘要

En 中文

On highways, the interaction with surrounding vehicles is very crucial for the decision-making and planning of autonomous vehicles. However, the multi-modal driving intentions of surrounding vehicles have brought great challenges. Aiming at the multi-modal driving intention of surrounding vehicles, a multi-modal driving risk field based on dynamic collision region is proposed, and multi-modal driving intention partially observable markov decision process (MDI-POMDP) decision framework is established, which integrating behavior decision and motion planning. Firstly, the multi-modal probability distribution of driving intention is fused to establish a driving risk field. Moreover, combined with the longitudinal safety distance model and lateral driving direction, the concept of dynamic collision area is proposed in the driving risk field. Then, MDI-POMDP is formulated to analyze the influence of the uncertainty on planning, which is caused by the multi-modal driving intention of surrounding vehicles. In the following, with the help of the previous state, a time-dependent deep reinforcement learning (DRL) algorithm recurrent deterministic policy gradient (RDPG) is designed to enhance the current observation, to solve the optimal driving policy under partial observation and generate the optimal trajectory. Furthermore, the simulation results show that the performance of our proposed motion planning algorithm is outstanding, compared with the states-of-the-art methods. And our algorithm has the powerful ability to model the multi-modality of driving intention, to ensure the traffic safety.

Keyword:

Deep reinforcement learning (DRL)

multi-modal driving intention (MDI)

partial observed Markov decision process (POMDP)

recurrent deterministic policy gradient (RDPG)