An Integrated Decision-Making Framework for Highway Autonomous Driving Using Combined Learning and Rule-Based Algorithm

In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time h...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 71; H. 4; S. 3621 - 3632
Hauptverfasser: Xu, Can, Zhao, Wanzhong, Liu, Jinqiang, Wang, Chunyan, Lv, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Autonomous vehicles
Decision making
Driving
Headways
highway driving
Machine learning
Roads
Task analysis
Training
Trajectory
Windows
ISSN:0018-9545, 1939-9359
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Zusammenfassung:In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3150343