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Learning-based approaches, such as reinforcement learning (RL) and imitation learning (IL), have indicated superiority over rule-based approaches in complex urban autonomous driving environments, showing great potential to make intelligent decisions.
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Z. Huang, J. Wu, and C. Lv, “Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
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