GPU based model-predictive path control for self-driving vehicles. Chajan, E., Schulte-Tigges, J., Reke, M., Ferrein, A., Matheis, D., & Walter, T. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 1243–1248, July, 2021.
GPU based model-predictive path control for self-driving vehicles [link]Ieeexpl  doi  abstract   bibtex   
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.
@InProceedings{ Chajan-etAl_IV2021_GPU-based-MPC,
  author       = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas},
  booktitle    = {2021 IEEE Intelligent Vehicles Symposium (IV)}, 
  title        = {GPU based model-predictive path control for self-driving vehicles}, 
  year         = {2021},
  month        = {July},
  pages        = {1243--1248},
  doi          = {10.1109/IV48863.2021.9575619},
  url_ieeexpl  = {https://ieeexplore.ieee.org/abstract/document/9575619},
  keywords     = {ADP;Heuristic algorithms;Computational modeling;Graphics
                  processing units;Stochastic processes;Prediction
                  algorithms;Trajectory;Vehicle dynamics;autonomous
                  driving;GPU;model-predictive control;grid
                  search;path control;ROS2},
  abstract     = {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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