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Personalized lane change decision algorithm using deep reinforcement learning approach
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AI
DOI：10.1007/s10489-022-04172-1.png)
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Abstract
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To develop driving automation technologies for humans, a human-centered methodology should be adopted for safety and satisfactory user experience. Automated lane change decision in dense highway traffic is challenging, especially when considering different driver preferences. This paper proposes a personalized lane change decision algorithm based on deep reinforcement learning. Firstly, driving experiments are carried out on a moving-base simulator. Based on the analysis of the experiment data, three personalization indicators are selected to describe the driver preferences in lane-change decisions. Then, a deep reinforcement learning (RL) approach is applied to design human-like agents for automated lane change decisions to capture the driver preferences, with refined rewards using the three personalization indicators. Finally, the trained RL agents and benchmark agents are tested in a two-lane highway driving scenario. Results show that the proposed algorithm can achieve higher consistency of lane change decision preferences than the comparison algorithm.
Keywords:
Reinforcement learning
Deep Q-Network
Automated driving
Lane change decision
Driver-in-the-loop experiment
Driving style
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