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

Main Article Content

Elaa Elgharbi
Máté Zöldy


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.

Article Details

How to Cite
Elgharbi, E., Zöldy, M. ., & Bhar Layeb, S. (2024). Autonomous Vehicle and Pedestrian Interaction : Leveraging The Use of Model Predictive Control & Genetic Algorithm. Cognitive Sustainability, 3(1).
Author Biographies

Elaa Elgharbi, Tunis Business School

Department of Business Analytics

Máté Zöldy, Faculty of Transportation Engineering and Vehicle Engineering

Department of Automotive Technologies

Safa Bhar Layeb, LR-OASIS

Department of Industrial Engineering


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