Authors:Emmanuel Alao¹;Lounis Adouane¹ andPhilippe Martinet²

Affiliations:¹Université de Technologie de Compiègne (UTC), CNRS, Heudiasyc, Compiègne, France;²Centre Inria d’Université Côte d’Azur, (INRIA), ACENTAURI, Sophia Antipolis, France

Keyword(s):Autonomous Vehicles, Autonomous Driving, Predictive Navigation, Risk Assessment and Risk Management, PLEVs, Multi-Modal Prediction, Multi-Risk.

Abstract:This paper presents an approach to autonomous vehicle navigation in urban environments with dynamic and multi-modal agents like Personal Light Electric Vehicles (PLEVs). The traditional Predictive Inter-Distance Profile (PIDP) risk assessment metric (Bellingard et al., 2023) is extended to handle multiple multi-modal motions using a fusion of PIDPs (F-PIDP). This approach accounts for the uncertainties in the various trajectories that PLEVs can follow on the road. A priority-based strategy is then developed to select the most dangerous agent. Then F-PIDP and Model Predictive Control (MPC) algorithm is employed for risk management, ensuring safe and reliable navigation. The efficiency of the proposed method is validated through several simulations.