用CLIP模型让自动驾驶更像人类司机
CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning
- 用CLIP视觉语言模型构建人机对齐的奖励函数
- 在无信号灯路口决策准确率提升17.3%
- 适合研究自动驾驶与人机共驾的工程师
本文提出CLIP-RLDrive，一种基于强化学习的自动驾驶决策框架，旨在复杂城市交通场景（尤其是无信号灯交叉口）中提升自动驾驶车辆的决策能力。核心挑战在于设计合适的奖励模型，而传统方法难以手动建模复杂的交互关系。为此，本文利用对比语言-图像预训练模型（CLIP）构建基于视觉与文本线索的额外奖励模型，实现自动驾驶行为与人类偏好对齐，显著改善决策合理性。
原文摘要 · Abstract (English)
This paper presents CLIP-RLDrive, a new reinforcement learning (RL)-based framework for improving the decision-making of autonomous vehicles (AVs) in complex urban driving scenarios, particularly in unsignalized intersections. To achieve this goal, the decisions for AVs are aligned with human-like preferences through Contrastive Language-Image Pretraining (CLIP)-based reward shaping. One of the primary difficulties in RL scheme is designing a suitable reward model, which can often be challenging to achieve manually due to the complexity of the interactions and the driving scenarios. To deal with this issue, this paper leverages Vision-Language Models (VLMs), particularly CLIP, to build an additional reward model based on visual and textual cues.
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