StyleDrive：面向驾驶风格感知的端到端自动驾驶基准测试
StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving
AI总结：
针对端到端自动驾驶（E2EAD）领域个性化研究被忽视的问题，本研究构建首个大规模真实世界个性化E2EAD数据集，提出混合标注流水线并建立标准化基准，实证显示融入驾驶偏好可显著提升模型与人类行为的一致性。
摘要：
个性化在传统自动驾驶流水线中已得到广泛研究，但在端到端自动驾驶（E2EAD）领域却被严重忽视，尽管它对提升用户信任、安全感知和实际落地至关重要。主要瓶颈在于缺乏能系统捕捉驾驶偏好的大规模真实世界数据集，这严重限制了个性化E2EAD模型的开发与评估。本研究首次引入专为个性化E2EAD精心构建的大规模真实世界数据集，整合了全面的场景拓扑结构与丰富的动态上下文——后者源自智能体动态信息及通过微调视觉语言模型（VLM）推断的语义信息。我们提出一种混合标注流水线，结合行为分析、基于规则和分布的启发式方法，以及由VLM推理引导的主观语义建模，并通过人在回路验证进行最终优化。基于该数据集，我们首次推出标准化基准，用于系统评估个性化E2EAD模型。对最先进架构的实证评估表明，融入个性化驾驶偏好能显著提升模型行为与人类示范的一致性。
英文摘要：
Personalization, while extensively studied in conventional autonomous driving pipelines, has been largely overlooked in the context of end-to-end autonomous driving (E2EAD), despite its critical role in fostering user trust, safety perception, and real-world adoption. A primary bottleneck is the absence of large-scale real-world datasets that systematically capture driving preferences, severely limiting the development and evaluation of personalized E2EAD models. In this work, we introduce the first large-scale real-world dataset explicitly curated for personalized E2EAD, integrating comprehensive scene topology with rich dynamic context derived from agent dynamics and semantics inferred via a fine-tuned vision-language model (VLM). We propose a hybrid annotation pipeline that combines behavioral analysis, rule-and-distribution-based heuristics, and subjective semantic modeling guided by VLM reasoning, with final refinement through human-in-the-loop verification. Building upon this dataset, we introduce the first standardized benchmark for systematically evaluating personalized E2EAD models. Empirical evaluations on state-of-the-art architectures demonstrate that incorporating personalized driving preferences significantly improves behavioral alignment with human demonstrations.
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