GPU based model-predictive path control for self-driving vehicles

  • One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.

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Author:Eduard Chajan, Joschua Schulte-Tigges, Michael RekeORCiD, Alexander Ferrein, Dominik Matheis, Thomas Walter
Parent Title (English):IEEE Intelligent Vehicles Symposium (IV)
Document Type:Conference Proceeding
Year of Completion:2021
Date of the Publication (Server):2021/11/15
Tag:Computational modeling; GPU; Heuristic algorithms; autonomous driving; model-predictive control
First Page:1243
Last Page:1248
2021 IEEE Intelligent Vehicles Symposium (IV)
July 11-17, 2021. Nagoya, Japan
Institutes:FH Aachen / Fachbereich Elektrotechnik und Informationstechnik
FH Aachen / MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik