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CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning
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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.
Forward citations
Cited by 2 Pith papers
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HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving
The HCRMP planner feeds LLM semantic hints into state representation and critic weighting instead of letting the LLM decide actions, reporting better CARLA driving metrics.
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Decoupled Functional Evaluation of Autonomous Driving Models via Feature Map Quality Scoring
A CLIP-based network predicts a feature-map score defined as 80% NDS ratio plus 20% similarity to SOTA features; using it as an auxiliary loss gives a 3.89% average NDS gain on BEVFormer.
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