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

• URL: http://arxiv.org/abs/2506.23982v2
• Date: Sun, 03 Aug 2025 09:28:43 GMT
• Title: StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving
• Authors: Ruiyang Hao, Bowen Jing, Haibao Yu, Zaiqing Nie,
• Abstract summary: Personalization has been largely overlooked in the context of end-to-end autonomous driving (E2EAD)<n>We introduce the first large-scale real-world dataset explicitly curated for personalized E2EAD.<n>We introduce the first standardized benchmark for systematically evaluating personalized E2EAD models.
• Score: 7.525510086747996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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