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Intelligent defensive driving for autonomous vehicles: Framework, strategy and verification.

Ting Zhang1, Zixuan Wang1, Hong Wang1

  • 1School of Vehicle and Mobility, Tsinghua University, Beijing 100084, PR China.

Accident; Analysis and Prevention
|December 17, 2025
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Summary

This study introduces intelligent defensive driving for autonomous vehicles (AVs) to handle rare, high-risk scenarios. By integrating human driving expertise, AVs can better anticipate dangers and improve safety.

Keywords:
Autonomous vehiclesIntelligent defensive drivingLong-tail issuesPotential risksSafe decision-making

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Area of Science:

  • Intelligent Transportation Systems
  • Robotics
  • Artificial Intelligence

Background:

  • Conventional autonomous vehicle (AV) decision-making struggles with infrequent, high-uncertainty scenarios, causing safety issues.
  • Human defensive driving expertise offers a valuable model for enhancing AV safety in complex situations.

Purpose of the Study:

  • To develop an integrated scheme for intelligent defensive driving in AVs.
  • To leverage human defensive driving experience to improve AV safety and systematically address long-tail issues.

Main Methods:

  • Constructed a framework for intelligent defensive driving, including scenario classification and hierarchical design.
  • Developed an online monitoring mechanism using experience-triggered conditions.
  • Integrated formalized defensive driving experience into a safety decision-making strategy.
  • Verified performance using "perception insufficiency" scenarios.

Main Results:

  • The defensive driving trigger mechanism successfully anticipated risks in various scenarios.
  • Improvements observed: 7.49m in braking distance, 1.56s in time-to-collision, and 2.84s in braking time.
  • Significantly reduced emergency accident probability while maintaining system robustness and real-time performance.

Conclusions:

  • The proposed intelligent defensive driving scheme effectively enhances AV safety, particularly in challenging scenarios.
  • Integrating human expertise into AV decision-making is crucial for addressing long-tail issues and improving overall reliability.