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arXiv · 2506.23982

StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving

Ruiyang Hao·Bowen Jing·Haibao Yu·Zaiqing Nie
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Abstract

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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Ruiyang Hao, Bowen Jing, Haibao Yu, Zaiqing Nie. 2025-06-30. StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving. https://arxiv.org/abs/2506.23982

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