Reliable Risk Assessment and Management using Probabilistic Fusion of Predictive Inter-Distance Profile for Urban Autonomous Driving

Emmanuel Alao (1) , Lounis Adouane (1, 2) , Philippe Martinet (3)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 IP - Institut Pascal
3 ACENTAURI - Intelligence artificielle et algorithmes efficaces pour la robotique autonome

Autonomous driving in urban scenarios has become more challenging due to the increase in Personal Light Electric Vehicles (PLEVs). PLEVs correspond mostly to electric devices such as gyropods and scooters. They exhibit varying velocity profiles as a result of their high acceleration capacity. Multiple hypotheses about their possible motion make autonomous driving very difficult, leading to the highly conservative behavior of most control algorithms. This paper proposes to solve this problem by performing a continuous risk assessment using a Fusion of Predictive Inter-Distance Profile (F-PIDP). Then a stochastic MPC algorithm performs effective risk management using the F-PIDP while taking into account adaptive constraints. The advantages of the proposed approach are demonstrated through simulations of multiple scenarios.

Domaines: Sciences de l'ingénieur [physics]

Emmanuel Alao, Lounis Adouane, Philippe Martinet. Reliable Risk Assessment and Management using Probabilistic Fusion of Predictive Inter-Distance Profile for Urban Autonomous Driving. ECC 2024 - 22nd European Control Conference, Jun 2024, Stockholm, Sweden. ⟨hal-04525836⟩