让自动驾驶更懂人性：基于驾驶习惯的变道推荐系统
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
- 引入驾驶员遵从度建模，动态调整变道建议
- 在CARLA环境中实测，提升单车行驶效率
- 适合智能交通与人机协同研究者参考
本文提出一种考虑人类驾驶员对建议动作部分服从行为的强化学习方法，旨在半自动驾驶环境下优化单车变道推荐，以提升行驶效率。该问题被建模为马尔可夫决策过程，并通过引入遵从度感知的深度Q网络进行求解。模型在真实场景下的CARLA驾驶环境进行了评估，验证了其有效性。
原文摘要 · Abstract (English)
In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment to enhance a single vehicle's travel efficiency. The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network, which takes into account the partial compliance of human drivers with the recommended actions. This approach is evaluated within CARLA's driving environment under realistic scenarios.
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