arXiv:2509.20653cs.ROcs.HC2025-09被引 1

用触觉共控教人开赛车,效果比全程自控或完全不帮更好。

Cyber Racing Coach: A Haptic Shared Control Framework for Teaching Advanced Driving Skills

  • 通过触觉反馈让系统与人协同控制方向盘,渐进减少辅助
  • 实验显示学员表现更优且更稳定,优于自学到或全程辅助组
  • 适合想练高阶驾驶技能的赛车手或自动驾驶训练场景

本研究提出一种基于触觉共控的虚拟赛车教练框架,用于教授人类驾驶员高阶驾驶技能。共控指人类与自动驾驶系统同时操控车辆转向。高阶驾驶技能包括竞速和紧急避障等极限工况下的安全操控能力。以往研究证明共控在性能与安全性上有效,但尚未评估其对复杂任务长期技能习得的影响。现有长期训练研究要么仅用于简单任务,要么使用视觉听觉反馈。本研究构建了基于触觉共控的虚拟赛车教练框架,包含高性能自主驾驶系统及随驾驶员表现逐步减弱辅助的渐退机制。对比无辅助与全辅助两种基准,人体实验结果表明,该框架显著提升驾驶员的赛车技能,表现更优且更一致。

原文摘要 · Abstract (English)

This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the human driver collaborates with an autonomous driving system to control the steering of a vehicle simultaneously. Advanced driving skills are those necessary to safely push the vehicle to its handling limits in high-performance driving such as racing and emergency obstacle avoidance. Previous research has demonstrated the performance and safety benefits of shared control schemes using both subjective and objective evaluations. However, these schemes have not been assessed for their impact on skill acquisition on complex and demanding tasks. Prior research on long-term skill acquisition either applies haptic shared control to simple tasks or employs other feedback methods like visual and auditory aids. To bridge this gap, this study creates a cyber racing coach framework based on the haptic shared control paradigm and evaluates its performance in helping human drivers acquire high-performance driving skills. The framework introduces (1) an autonomous driving system that is capable of cooperating with humans in a highly performant driving scenario; and (2) a haptic shared control mechanism along with a fading scheme to gradually reduce the steering assistance from autonomy based on the human driver's performance during training. Two benchmarks are considered: self-learning (no assistance) and full assistance during training. Results from a human subject study indicate that the proposed framework helps human drivers develop superior racing skills compared to the benchmarks, resulting in better performance and consistency.

触觉反馈共控驾驶技能学习

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。