Autonomous racing using learning Model Predictive Control

Rosolia, Ugo; Carvalho, Ashwin; Borrelli, Francesco

doi:10.23919/acc.2017.7963748

Cited by 105 publications

(97 citation statements)

References 31 publications

(97 reference statements)

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“…Learning techniques are commonly used to learn a dynamics model which in turn improves an a priori system model in iterative learning control (ILC; Kapania & Gerdes, ; Ostafew, Schoellig, & Barfoot, ; Panomruttanarug, ; Z. Yang, Zhou, Li, & Wang, ) and model predictive control (MPC; Drews, Williams, Goldfain, Theodorou, & Rehg, 2017; Lefevre, Carvalho, & Borrelli, ; Lefèvre, Carvalho, & Borrelli, ; Ostafew et al, , ; Pan et al, , ; Rosolia, Carvalho, & Borrelli, ).…”

Section: Motion Controllers For Ai‐based Self‐driving Carsmentioning

confidence: 99%

“…In previous works, learning controllers have been introduced based on simple function approximators, such as Gaussian process (GP) modeling (Meier, Hennig, & Schaal, 2014;Nguyen-Tuong, Peters, & Seeger, 2008;Ostafew et al, 2015;Ostafew, Schoellig, & Barfoot, 2016) or support vector regression (Sigaud, Salaün, & Padois, 2011 Lefèvre, Carvalho, & Borrelli, 2016;Ostafew et al, 2015Ostafew et al, , 2016Pan et al, 2018Pan et al, , 2017Rosolia, Carvalho, & Borrelli, 2017).…”

Section: Learning Controllersmentioning

confidence: 99%

“…Learning has been used in conjunction with MPC to learn driving models (Lefevre et al, 2015;Lefèvre et al, 2016), driving dynamics for race cars operating at their handling limits (Drews et al, 2017;Rosolia et al, 2017), as well as to improve path tracking accuracy (Brunner, Rosolia, Gonzales, & Borrelli, 2017;Ostafew et al, 2015Ostafew et al, , 2016. These methods use learning mechanisms to identify nonlinear dynamics that are used in the MPC's trajectory cost function optimization.…”

Section: Learning Controllersmentioning

confidence: 99%

See 2 more Smart Citations

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

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et al. 2019

Journal of Field Robotics

1,214

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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Section: Motion Controllers For Ai‐based Self‐driving Carsmentioning

confidence: 99%

Section: Learning Controllersmentioning

confidence: 99%

Section: Learning Controllersmentioning

confidence: 99%

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A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,214

495

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“…Liniger et al [6] utilizes the Real Time Iteration Sequential Quadratic Programming (RTI-SQP) paradigm [16] to jointly solve the trajectory planning and control problems. Rosolia [17] applied learning MPC to minimize lap completion time, given data from previous laps. Building on the experiences from the racing application, Gray et al [18] considered motion planning at the handling limits for obstacle avoidance, generating a high-level motion plan from a four-wheel dynamic model and a low-level plan using MPC.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Trajectory Planning and optimization at Limits of Handling

Svensson

Bujarbaruah

Kapania

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we tackle the problem of trajectory planning and control of a vehicle under locally varying traction limitations, in the presence of suddenly appearing obstacles. We employ concepts from adaptive model predictive control for run-time adaptation of tire force constraints that are imposed by local traction conditions. To solve the resulting optimization problem for real-time control synthesis with such time varying constraints, we propose a novel numerical scheme based on Real Time Iteration Sequential Quadratic Programming (RTI-SQP), which we call Sampling Augmented Adaptive RTI (SAA-RTI). Sampling augmentation of conventional RTI-SQP provides additional feasible candidate trajectories for warmstarting the optimization procedure. Thus, the proposed SAA-RTI algorithm enables real time constraint adaptation and reduces sensitivity to local minima. Through extensive numerical simulations we demonstrate that our method increases the vehicle's capacity to avoid accidents in scenarios with unanticipated obstacles and locally varying traction, compared to equivalent non-adaptive control schemes and traditional planning and tracking approaches.

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“…Furthermore, we linearize the kinematic equations of motion to approximate the evolution of the vehicle's position as a function of the velocities. Conversely to our previous works [20], [21], this strategy allow us to reformulate the LMPC as a Quadratic Program (QP) which can be solved efficiently.…”

Section: Introductionmentioning

confidence: 99%

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

Rosolia

Borrelli

2020

IEEE Trans. Contr. Syst. Technol.

Self Cite

102

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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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Autonomous racing using learning Model Predictive Control

Cited by 105 publications

References 31 publications

A survey of deep learning techniques for autonomous driving

A survey of deep learning techniques for autonomous driving

Adaptive Trajectory Planning and optimization at Limits of Handling

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

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