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

2025-09-24

AI Summary

Existing shared control research predominantly focuses on task completion, lacking systematic evaluation of long-term acquisition of complex driving skills. Method: This paper proposes a performance-adaptive haptic shared control framework that integrates human–machine cooperative steering with progressive assistance attenuation, enabling effective transfer of advanced driving skills—such as racing cornering and emergency obstacle avoidance—in highly dynamic scenarios. Contribution/Results: To our knowledge, this is the first empirical validation of haptic shared control’s efficacy in fostering long-term skill internalization within complex, limit-condition driving tasks. Experimental results demonstrate significant improvements over both unassisted self-learning and constant-assistance baselines: +23.6% in control precision, −31.4% in trajectory standard deviation (enhanced consistency), and higher task success rates. Critically, acquired skills robustly transfer to unassisted operation, establishing a novel paradigm for high-performance human–machine collaborative driving skill training.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Teaching advanced driving skills using haptic shared control framework
• Evaluating skill acquisition impact on complex high-performance driving tasks
• Developing autonomous cooperation and fading assistance for racing scenarios

Innovation

• Haptic shared control for teaching driving skills
• Autonomous system cooperating in high-performance scenarios
• Fading assistance based on driver performance