AAC 2019 Paper Abstract

AAC 2019 Paper Abstract

Paper MoBT1.5

RECEVEUR, Jean-Baptiste (IMS), Victor, Stephane (Université de Bordeaux, IMS), MELCHIOR, Pierre (Université Bordeaux 1 - IPB/ENSEIRB-MATMECA)

Optimal Trajectory Planning and Robust Tracking Using Vehicle Model Inversion

Scheduled for presentation during the Regular Session "Motion and Path Planning for Autonomous Vehicle" (MoBT1), Monday, June 24, 2019, 16:50−17:10, Chambord

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on April 18, 2024

Keywords Autonomous Driving and Collision Avoidance: Sensor Fusion, Modeling of the Environment, Control Architectures, Intelligent Vehicles and Robotics Technology in Vehicles, Human Factors in Driver Dynamics or Driver Assistance Systems

Abstract

Trajectory planning for autonomous vehicles has significantly increased as more and more ADAS are included in modern cars. It A vehicle definitely needs to globally plan a trajectory, taking all the driving factors into account. For an autonomous terrestrial vehicle, this article proposes using an optimal trajectory on a specific range as a reference in a tracking loop. In previous studies, the optimization has been made using a Genetic Algorithm (GA), and the obtained trajectory has been injected into a Potential Field (PF) so as to be reactive to unforeseen events. Here, the previously developed GA-PF method is inserted in a new global planning and tracking method for longitudinal feedback control. An optimal trajectory is used as an input reference and a tracking schema is developed using an inverted bicycle model as an efficient feedforward control, and a robust controller to take the vehicle parameter variations into account. Autonomous car simulation results are given.