通过人类反馈强化学习学习个性化驾驶风格
Learning Personalized Driving Styles via Reinforcement Learning from Human Feedback
AI总结：
针对生成式轨迹模型难以捕捉个性化驾驶风格的问题，提出人类反馈驱动微调框架TrajHF，结合多条件去噪器与人类反馈强化学习，在NavSim基准上达到最先进性能，实现个性化自适应轨迹生成。
摘要：
在动态环境中生成类人且自适应的轨迹对于自动驾驶至关重要。尽管生成模型在合成可行轨迹方面展现出潜力，但由于数据集偏差和分布偏移，它们往往无法捕捉个性化驾驶风格的细微差异。为解决这一问题，我们提出了TrajHF，一个用于生成式轨迹模型的人类反馈驱动微调框架，旨在使运动规划与多样化的驾驶风格对齐。TrajHF结合了多条件去噪器和人类反馈强化学习，以超越传统模仿学习，细化多模态轨迹生成。这使其能够更好地与人类驾驶偏好对齐，同时保持安全性和可行性约束。TrajHF在NavSim基准上达到了与最先进方法相当的性能。TrajHF为自动驾驶中个性化和自适应的轨迹生成树立了新的范式。
英文摘要：
Generating human-like and adaptive trajectories is essential for autonomous driving in dynamic environments. While generative models have shown promise in synthesizing feasible trajectories, they often fail to capture the nuanced variability of personalized driving styles due to dataset biases and distributional shifts. To address this, we introduce TrajHF, a human feedback-driven finetuning framework for generative trajectory models, designed to align motion planning with diverse driving styles. TrajHF incorporates multi-conditional denoiser and reinforcement learning with human feedback to refine multi-modal trajectory generation beyond conventional imitation learning. This enables better alignment with human driving preferences while maintaining safety and feasibility constraints. TrajHF achieves performance comparable to the state-of-the-art on NavSim benchmark. TrajHF sets a new paradigm for personalized and adaptable trajectory generation in autonomous driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
