Autonomous Vehicle and Pedestrian Interaction

Elaa  Elgharbi; Máté Zöldy; Safa  Bhar Layeb

doi:10.55343/CogSust.90

Elaa Elgharbi Lycee Ibn Abbad, Université Cadi Ayyad Faculté des Sciences Semlalia, Université Mohammed V Agdal Faculté des Sciences Rabat, Morocco https://orcid.org/0000-0002-6913-0351
Máté Zöldy Department of Automotive Technology, Budapest University of Technology and Economics Budapest, Hungary https://orcid.org/0000-0003-1271-840X
Safa Bhar Layeb LR-OASIS National Engineering School of Tunis, University of Tunis El Manar https://orcid.org/0000-0003-2536-7872

DOI: https://doi.org/10.55343/CogSust.90

Keywords: Autonomous vehicle, MPC, GA, Pedestrian safety, Sustainability

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.

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