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Autonomous Vehicle and Pedestrian Interaction - Leveraging The Use of Model Predictive Control & Genetic Algorithm

Author

Listed:
  • Mate Zoldy

    (Department of Automotive Technologies, Faculty of Transport Engineering and Vehicle Engineering, Budapest Univeristy of Technology and Economics, Budapest, Hungary)

  • Elaa Elgharbi
  • Safa Bhar Layeb

Abstract

Driving assistance systems and even autonomous driving have and will have an important role in sustainable mobility systems. Traffic situations where participants’ cognitive levels are different will cause challenges in the long term. When a pedestrian crosses the road, an autonomous vehicle may need to navigate safely while maintaining its desired speed. Achieving this involves using a predictive model to anticipate pedestrian movements and a strategy for the vehicle to adjust its speed proactively. This research combined model-based predictive control (MPC) with a social-force model (SFM) to effectively control the autonomous vehicle’s longitudinal speed. A genetic algorithm (GA) was also integrated into the approach to address the optimisation problem. A comparison between the proposed approach (MPC-GA) and the conventional MPC technique proved the outperformance of MPC-GA.

Suggested Citation

  • Mate Zoldy & Elaa Elgharbi & Safa Bhar Layeb, 2024. "Autonomous Vehicle and Pedestrian Interaction - Leveraging The Use of Model Predictive Control & Genetic Algorithm," Cognitive Sustainability, Cognitive Sustainability Ltd., vol. 3(1), pages 15-31, March.
    • Handle: RePEc:bcy:issued:cognitivesustainability:v:3:y:2024:i:1:p:15-31
    DOI: 10.55343/CogSust.90
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    References listed on IDEAS

    as
    1. Yasin Abdolahi & Sajad Yousefi & Jafar Tavoosi & Francesco Lo Iudice, 2023. "A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles," Complexity, Hindawi, vol. 2023, pages 1-9, January.
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      More about this item

      Keywords

      Autonomous vehicle MPC; GA pedestrian safety; Sustainability;
      All these keywords.

      JEL classification:

      • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
      • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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