Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

Abstract

This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 $m/s$. The used offline global trajectory generation and the online model predictive controller are highly based on optimization and dynamic models of the vehicle, where the tires and camber effects are represented in an extended version of the basic Pacejka Magic Formula. The proposed single-track model is identified and validated using multi-body motorsport libraries which allow simulating the vehicle dynamics properly, especially useful when real experimental data are missing. The fundamental regularization terms and constraints of the controller are tuned to reduce the rate of change of the inputs while assuring an acceptable velocity and path tracking. The motion planning strategy consists of a Frenét-Frame-based planner which considers a forecast of the opponent produced by a Kalman filter. The planner chooses the collision-free path and velocity profile to be tracked on a 3 seconds horizon to realize different goals such as following and overtaking. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s^2$.

Accepted to the 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022)

References in corpus (4)

• Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
• Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
• A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver
• Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing

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• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential
• Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
• Hierarchical Time-Optimal Planning for Multi-Vehicle Racing
• Fast and Modular Autonomy Software for Autonomous Racing Vehicles
• A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
• Longitudinal Control for Autonomous Racing with Combustion Engine Vehicles
• Dynamic Constraint Tightening for Nonlinear MPC for Autonomous Racing via Contraction Analysis