面向可适应个性化自动驾驶的多目标强化学习
Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving
AI总结：
本文提出基于多目标强化学习与偏好驱动优化的端到端自动驾驶方法，通过连续权重向量在运行时动态调节效率、舒适性、速度和激进性等驾驶风格，无需重训策略，并在CARLA中验证了其适应能力与安全性。
摘要：
人类驾驶员在驾驶风格上表现出个体偏好。使自动驾驶车辆适应这些偏好对于用户信任和满意度至关重要。然而，现有的端到端驾驶方法通常依赖预定义的驾驶风格，或需要持续的用户反馈才能进行适应，这限制了它们支持动态、依赖情境的偏好的能力。我们提出一种新方法，使用多目标强化学习（MORL）并结合偏好驱动优化来实现端到端自动驾驶，从而支持在运行时对驾驶风格偏好进行适应。偏好被编码为连续权重向量，以沿可解释的风格目标调节行为——包括效率、舒适性、速度和激进性——而无需重新训练策略。我们的单策略智能体在复杂混合交通场景中集成了基于视觉的感知，并使用CARLA模拟器在不同的城市环境中进行评估。实验结果表明，该智能体能够根据变化的偏好动态调整其驾驶行为，同时在避免碰撞和路线完成方面保持性能。
英文摘要：
Human drivers exhibit individual preferences regarding driving style. Adapting autonomous vehicles to these preferences is essential for user trust and satisfaction. However, existing end-to-end driving approaches often rely on predefined driving styles or require continuous user feedback for adaptation, limiting their ability to support dynamic, context-dependent preferences. We propose a novel approach using multi-objective reinforcement learning (MORL) with preference-driven optimization for end-to-end autonomous driving that enables runtime adaptation to driving style preferences. Preferences are encoded as continuous weight vectors to modulate behavior along interpretable style objectives$\unicode{x2013}$including efficiency, comfort, speed, and aggressiveness$\unicode{x2013}$without requiring policy retraining. Our single-policy agent integrates vision-based perception in complex mixed-traffic scenarios and is evaluated in diverse urban environments using the CARLA simulator. Experimental results demonstrate that the agent dynamically adapts its driving behavior according to changing preferences while maintaining performance in terms of collision avoidance and route completion.
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。
