CLC number: TP18;U495
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-08-10
Cited: 0
Clicked: 2361
Citations: Bibtex RefMan EndNote GB/T7714
Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG. Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 131-140.
@article{title="Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning",
author="Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="1",
pages="131-140",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200128"
}
%0 Journal Article
%T Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning
%A Huiqian LI
%A Jin HUANG
%A Zhong CAO
%A Diange YANG
%A Zhihua ZHONG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 1
%P 131-140
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200128
TY - JOUR
T1 - Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning
A1 - Huiqian LI
A1 - Jin HUANG
A1 - Zhong CAO
A1 - Diange YANG
A1 - Zhihua ZHONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 1
SP - 131
EP - 140
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200128
Abstract: Ensuring the safety of pedestrians is essential and challenging when autonomous vehicles are involved. Classical pedestrian avoidance strategies cannot handle uncertainty, and learning-based methods lack performance guarantees. In this paper we propose a hybrid reinforcement learning (HRL) approach for autonomous vehicles to safely interact with pedestrians behaving uncertainly. The method integrates the rule-based strategy and reinforcement learning strategy. The confidence of both strategies is evaluated using the data recorded in the training process. Then we design an activation function to select the final policy with higher confidence. In this way, we can guarantee that the final policy performance is not worse than that of the rule-based policy. To demonstrate the effectiveness of the proposed method, we validate it in simulation using an accelerated testing technique to generate stochastic pedestrians. The results indicate that it increases the success rate for pedestrian avoidance to 98.8%, compared with 94.4% of the baseline method.
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