驾驶千面：一个闭环个性化端到端自动驾驶的基准
Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
AI总结：
本文提出Person2Drive平台，通过个性化数据集、评价指标和框架，解决自动驾驶个性化难题，实现细粒度分析与有效个性化。
摘要：
人类驾驶行为本质上具有多样性，但大多数端到端自动驾驶（E2E-AD）系统学习单一平均驾驶风格，忽视个体差异。实现个性化E2E-AD面临三个层面的挑战：现实数据集有限且缺乏个体标注、缺乏量化评估个人驾驶风格的指标、缺乏能从用户轨迹中学习风格化表示的算法。为解决这些缺口，我们提出Person2Drive，一个全面的个性化E2E-AD平台和基准。它包括一个开源灵活的数据收集系统，可模拟现实场景生成可扩展的个性化驾驶数据集；基于风格向量的评估指标，利用最大均值差异和KL散度量化个体驾驶行为；以及一个个性化E2E-AD框架，包含风格奖励模型，能高效适应E2E模型以实现安全个性化驾驶。大量实验表明，Person2Drive在端到端自动驾驶中实现了细粒度分析、可重复评估和有效个性化。我们的数据集和代码将在接受后发布。
英文摘要：
Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecting individual differences. Achieving personalized E2E-AD faces challenges across three levels: limited real-world datasets with individual-level annotations, a lack of quantitative metrics for evaluating personal driving styles, and the absence of algorithms that can learn stylized representations from users' trajectories. To address these gaps, we propose Person2Drive, a comprehensive personalized E2E-AD platform and benchmark. It includes an open-source, flexible data collection system that simulates realistic scenarios to generate scalable, diverse personalized driving datasets; style vector-based evaluation metrics with Maximum Mean Discrepancy and KL divergence to comprehensively quantify individual driving behaviors; and a personalized E2E-AD framework with a style reward model that efficiently adapts E2E models for safe and individualized driving. Crucially, our framework enables plug-and-play personalization by fine-tuning only the trajectory prediction head, preserving the pretrained base model and ensuring safety. Extensive experiments demonstrate that Person2Drive enables fine-grained analysis and effective personalization, while preserving driving performance and success rate even in challenging scenarios.
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。
