<ul><li>This paper presents a novel pipeline for generating human-aligned reward labels in the context of offline reinforcement learning for autonomous emergency braking in occluded pedestrian crossing scenarios.</li><li>The proposed pipeline addresses the challenge of absent reward signals in real-world datasets by generating labels that reflect human judgment and safety considerations.</li><li>An adaptive safety component is incorporated, allowing the autonomous vehicle to prioritize safety over efficiency in potential collision scenarios by analyzing semantic segmentation maps.</li><li>The results demonstrate the effectiveness of the method in producing reliable and human-aligned reward signals, facilitating the training of autonomous driving systems in alignment with human values.</li></ul>

Offline Reinforcement Learning using Human-Aligned Reward Labeling for Autonomous Emergency Braking in Occluded Pedestrian Crossing

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