Towards time-optimal race car driving using nonlinear MPC in real-time

Verschueren, Robin; Bruyne, Stijn De; Zanon, Mario; Frasch, Janick V.; Diehl, Moritz

doi:10.1109/cdc.2014.7039771

Cited by 91 publications

(45 citation statements)

References 12 publications

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“…In particular, rapid, simultaneous optimization of the driven path and speed via MPC has been used successfully in the control of miniature cars. An early example is given by Verschueren et al in [12]. Work by Liniger et al with a vehicle model that accounted for tire slip appeared in [13] and was similarly demonstrated through the racing of 1:43 scale RC cars.…”

Section: A Related Workmentioning

confidence: 99%

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

Subosits

Gerdes

2019

IEEE Trans. Intell. Veh.

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In emergency situations, autonomous vehicles will be forced to operate at their friction limits in order to avoid collisions. In these scenarios, coordinating the planning of the vehicle's path and speed gives the vehicle the best chance of avoiding an obstacle. Fast reaction time is also important in an emergency, but approaches to the trajectory planning problem based on nonlinear optimization are computationally expensive. This paper presents a new scheme that simultaneously modifies the desired path and speed profile for a vehicle in response to the appearance of an obstacle, significant tracking error, or other environmental change. By formulating the trajectory optimization problem as a quadratically constrained quadratic program, solution times of less than 20 ms are possible even with a 10-s planning horizon. A simplified point mass model is used to describe the vehicle's motion, but the incorporation of longitudinal weight transfer and road topography means that the vehicle's acceleration limits are modeled more accurately than in comparable approaches. Experimental data from an autonomous vehicle in two scenarios demonstrate how the trajectory planner enables the vehicle to avoid an obstacle even when the obstacle appears suddenly and the vehicle is already operating near the friction limits.

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Section: A Related Workmentioning

confidence: 99%

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

Subosits

Gerdes

2019

IEEE Trans. Intell. Veh.

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“…In related literature, several control techniques for autonomous racing are known. They can be roughly categorized into two categories: predictive (Funke, Brown, Erlien, & Gerdes, 2017; Liniger, Domahidi, & Morari, 2015; Liniger & Lygeros, 2019; Rosolia, Carvalho, & Borrelli, 2017; Verschueren, De Bruyne, Zanon, Frasch, & Diehl, 2014) and nonpredictive controllers (Kapania & Gerdes, 2015; Klomp, Olsson, & Sandberg, 2014; Kritayakirana & Gerdes, 2012; Zhang, Xu, & Rachid, 2001). Since nonpredictive racing controllers are not able to plan the motion of the car, it is necessary to compute the racing line beforehand.…”

Section: Introductionmentioning

confidence: 99%

AMZ Driverless: The full autonomous racing system

Kabzan

Valls

Reijgwart

et al. 2020

Journal of Field Robotics

104

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This paper presents the algorithms and system architecture of an autonomous racecar. The introduced vehicle is powered by a software stack designed for robustness, reliability, and extensibility. To autonomously race around a previously unknown track, the proposed solution combines state of the art techniques from different fields of robotics. Specifically, perception, estimation, and control are incorporated into one high‐performance autonomous racecar. This complex robotic system, developed by AMZ Driverless and ETH Zürich, finished first overall at each competition we attended: Formula Student Germany 2017, Formula Student Italy 2018 and Formula Student Germany 2018. We discuss the findings and learnings from these competitions and present an experimental evaluation of each module of our solution.

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“…NMPC strategies for autonomous racing are tested also in [14], where the authors compared two control methodologies based on different parametrizations of the vehicle's model. In [15] the authors compared two approaches, the first one based on a tracking MPC and the second one based on a MPC formulated in a space dependent frame. A Model Predictive U.…”

Section: Introductionmentioning

confidence: 99%

Learning How to Autonomously Race a Car: A Predictive Control Approach

Rosolia

Borrelli

2020

IEEE Trans. Contr. Syst. Technol.

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In this paper we present a Learning Model Predictive Controller (LMPC) for autonomous racing. We model the autonomous racing problem as a minimum time iterative control task, where an iteration corresponds to a lap. The system trajectory and input sequence of each lap are stored and used to systematically update the controller for the next lap. In the proposed approach the race time does not increase at each iteration. The first contribution of the paper is to propose a local LMPC which reduces the computational burden associated with existing LMPC strategies. In particular, we show how to construct a local safe set and approximation to the value function, using a subset of the stored data. The second contribution is to present a system identification strategy for the autonomous racing iterative control task. We use data from previous iterations and the vehicle's kinematic equations of motion to build an affine time-varying prediction model. The effectiveness of the proposed strategy is demonstrated by experimental results on the Berkeley Autonomous Race Car (BARC) platform.

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Towards time-optimal race car driving using nonlinear MPC in real-time

Cited by 91 publications

References 12 publications

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving

AMZ Driverless: The full autonomous racing system

Learning How to Autonomously Race a Car: A Predictive Control Approach

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