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Crash mitigation controller for unavoidable T-bone collisions using reinforcement learning.

Xiaohui Hou1, Junzhi Zhang1, Chengkun He1

  • 1State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China; School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.

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|April 15, 2022
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Summary
This summary is machine-generated.

This study introduces an advanced crash mitigation controller for unavoidable T-bone collisions, enhancing vehicle maneuverability and reducing crash severity. The innovative system utilizes an improved reinforcement learning algorithm for optimal performance in emergency scenarios.

Keywords:
Crash mitigation controllerDrift operation mechanismNonlinear dynamicsReinforcement learningT-bone collision

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • T-bone collisions are severe emergency scenarios exceeding conventional control system capabilities.
  • Minimizing crash severity and maintaining vehicle control in such events remain significant challenges.

Purpose of the Study:

  • To develop an innovative crash mitigation controller for unavoidable T-bone collisions.
  • To enhance vehicle maneuverability and reduce crash severity in extreme scenarios.
  • To improve the performance of autonomous driving systems in challenging conditions.

Main Methods:

  • Implementation of an improved reinforcement learning algorithm (TD3) with an expert-behavior policy and drift-operation mechanism.
  • Development of vehicle and tire models accounting for nonlinear and coupled dynamics for enhanced control accuracy.
  • Testing the controller's effectiveness across diverse emergency scenarios.

Main Results:

  • The proposed controller achieves optimal crash mitigation effects, outperforming conventional systems and other reinforcement learning algorithms.
  • Demonstrated expansion of the vehicle-maneuverability envelope during T-bone collision scenarios.
  • Improved control accuracy due to nonlinear and coupled dynamics modeling.

Conclusions:

  • The developed controller offers a significant advancement in mitigating T-bone collision severity.
  • This technology is expected to enhance the operational capabilities of autonomous driving systems under extreme conditions.
  • The integration of expert knowledge and advanced reinforcement learning provides a robust solution for complex crash scenarios.