Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning
Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning
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DOI：
10.1109/smc42975.2020.9283222
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Zhuoxi Liu;Z. Wang;Bo Yang;Kimihiko Nakano
中科院分区：
文献类型：
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作者：
Zhuoxi Liu;Z. Wang;Bo Yang;Kimihiko Nakano
在这篇文章中，作者提出了一种新的方法来学习个性化的自主车道变换启动策略，通过人机交互的全自动驾驶汽车。采用强化学习技术来学习如何从交通环境、自动驾驶车辆的动作和车载用户的反馈中启动车道变更，而不是从人类驾驶演示中学习。建议的离线算法奖励的行动选择策略时，用户给予积极的反馈和惩罚时，负反馈。此外，多维驾驶场景被认为是代表一个更现实的车道变化的权衡。结果表明，该方法得到的换道启动模型能够再现个性化的换道策略，且定制模型的性能（平均准确率为86.1%）明显优于非定制模型（平均准确率为75.7%）.这种方法允许在完全自动驾驶期间持续改进用户的定制，即使没有人类驾驶经验，这将显著提高用户对自动驾驶车辆高水平自主性的接受程度。
In this article, the authors present a novel method to learn the personalized tactic of discretionary lane-change initiation for fully autonomous vehicles through human-computer interactions. Instead of learning from human-driving demonstrations, a reinforcement learning technique is employed to learn how to initiate lane changes from traffic context, the action of a self-driving vehicle, and in-vehicle user’s feedback. The proposed offline algorithm rewards the action-selection strategy when the user gives positive feedback and penalizes it when negative feedback. Also, a multi-dimensional driving scenario is considered to represent a more realistic lane-change trade-off. The results show that the lane-change initiation model obtained by this method can reproduce the personal lane-change tactic, and the performance of the customized models (average accuracy 86.1%) is much better than that of the non-customized models (average accuracy 75.7%). This method allows continuous improvement of customization for users during fully autonomous driving even without human-driving experience, which will significantly enhance the user acceptance of high-level autonomy of self-driving vehicles.