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Towards robust sensor-based maneuvers for a car-like vehicle

This paper presents a novel control architecture for a car-like vehicle moving in a dynamic and partially known environment. The key idea is to plan and carry out sensor-based maneuvers. The paper focuses on the reactive part of the architecture that features control experts, i.e., parametrized control programs adapted to a specific maneuver and capable to react in real-time to unforeseen events. This paper aims to show why and how we made use of artificial neural network to improve the performance of our control architecture. Simulation and experimental results obtained with an automatic car are presented to illustrate the advantages of our approach.