Autonomous vehicles are expected to have a significant impact on our societies by freeinghumans from the driving task, and thus eliminating the human factor in one of themost dangerous places: roads. As a matter of facts, road kills are one of the largest sourceof human deaths and many countries put the decrease of these casualties as one of their toppriorities. It is expected that autonomous vehicles will dramatically help in achieving that.Moreover, using controllers to optimize both the car behaviour on the road and higher leveltraffic management could reduce traffic jams and increase the commuting speed overall.To minimize road kills, an approach is to design controllers that would handle the car atits limits of handling, by integrating complex dynamics such as adherence loss it is possibleto prevent the car from leaving the road. A convenient setup to evaluate this type ofcontrollers is a racing context: a controller is steering a car to complete a track as fast aspossible without leaving the road and by brining the car to its limits of handling.In this thesis, we design a controller for an autonomous vehicle with the goal of driving itfrom A to B as fast as possible. This is the main motivation in racing applications. Thecontroller should steer the car with the goal to minimize the racing time.This controller was designed within the model predictive controller (MPC) framework,where we used the concept of road-aligned model. In contrast with the standard mpc techniques,we use the objective function to maximize the progress along the reference path,by integrating a linear model of the vehicle progression along the centerline. Combinedwith linear vehicle model and constraints, a optimization problem providing the vehiclewith the future steering and throttle values to apply is formulated and solved with linearprogramming in an on-line fashion during the race. We show the effectiveness of our controllerin simulation, where the designed controller exhibits typical race drivers behavioursand strategies when ...


    Access

    Download


    Export, share and cite



    Title :

    Autonomous racing using model predictive control


    Contributors:

    Publication date :

    2017-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Deep High-Level Policy Model Predictive Contour Control for Autonomous Racing

    Liu, Wenjun / Zeng, Minghao / Chen, Guang et al. | IEEE | 2023


    Learning model predictive control for quadrotors minimum-time flight in autonomous racing scenarios

    Calogero, Lorenzo / Mammarella, Martina / Dabbene, Fabrizio | BASE | 2023

    Free access


    Autonomous Overtaking Using Model Predictive Control

    Larsen, Oscar | BASE | 2020

    Free access

    Robust and Data-Driven Control for Autonomous Racing

    Wischnewski, Alexander | TIBKAT | 2023

    Free access