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<p>This study proposes a synergistic optimization framework that integrates trajectory planning and path tracking control to mitigate motion sickness in autonomous vehicles.The framework employs an Anti-Motion-Sickness Trajectory (AMT) generator that uses time-adaptive quintic polynomials to reduce lateral acceleration by dynamically adjusting the lane-change duration. A dual-loop controller combines Model Predictive Control (MPC) for trajectory tracking with Proportional-Integral-Derivative (PID) feedback to compensate for acceleration oscillations in the motion sickness-sensitive frequency band through steering command smoothing. CarSim-Simulink co-simulation validates the framework under baseline conditions (60&#xa0;km/h, road adhesion coefficient <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\mu\)</EquationSource> </InlineEquation> = 0.85), achieving an 82.6% reduction in Motion Sickness Dose Value (MSDV) compared to conventional approaches. Multi-condition experiments at three vehicle speeds (36, 60, and 72&#xa0;km/h) and two road adhesion coefficients (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\mu\)</EquationSource> </InlineEquation>= 0.3 and 0.85) demonstrate optimal performance at 60&#xa0;km/h with a 20.0% MSDV improvement, stable effectiveness within typical highway speed ranges of 40 to 70&#xa0;km/h and normal road surfaces with adhesion coefficients above 0.5, and acceptable performance under extreme scenarios. Results confirm the framework effectively balances ride comfort and tracking accuracy through synergistic optimization, providing a systematic anti-motion-sickness solution for autonomous vehicles across diverse operating conditions.</p>

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Synergistic Trajectory Planning and Tracking Control for Motion Sickness Mitigation in Autonomous Vehicles

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Zhijun Fu,
  • Tao Yu,
  • Dengfeng Zhao,
  • Jinquan Ding,
  • Yumeng Yao,
  • Bao Ma,
  • Subhash Rakheja

摘要

This study proposes a synergistic optimization framework that integrates trajectory planning and path tracking control to mitigate motion sickness in autonomous vehicles.The framework employs an Anti-Motion-Sickness Trajectory (AMT) generator that uses time-adaptive quintic polynomials to reduce lateral acceleration by dynamically adjusting the lane-change duration. A dual-loop controller combines Model Predictive Control (MPC) for trajectory tracking with Proportional-Integral-Derivative (PID) feedback to compensate for acceleration oscillations in the motion sickness-sensitive frequency band through steering command smoothing. CarSim-Simulink co-simulation validates the framework under baseline conditions (60 km/h, road adhesion coefficient \(\mu\) = 0.85), achieving an 82.6% reduction in Motion Sickness Dose Value (MSDV) compared to conventional approaches. Multi-condition experiments at three vehicle speeds (36, 60, and 72 km/h) and two road adhesion coefficients ( \(\mu\) = 0.3 and 0.85) demonstrate optimal performance at 60 km/h with a 20.0% MSDV improvement, stable effectiveness within typical highway speed ranges of 40 to 70 km/h and normal road surfaces with adhesion coefficients above 0.5, and acceptable performance under extreme scenarios. Results confirm the framework effectively balances ride comfort and tracking accuracy through synergistic optimization, providing a systematic anti-motion-sickness solution for autonomous vehicles across diverse operating conditions.