A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle

A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It also needs to deal with the internal parametric variations of vehicle sub-systems, including power-train efficiency, measurement errors, time delay,so on.Moreover, as in most production vehicles, the low-control commands for the engine, brake, and steering systems are delivered through separate electronic control units.These aforementioned factors introduce opaque and ineffectiveness issues in controller performance.In this paper, we design a feed-forward compensate process via a data-driven method to model and further optimize the controller performance.We apply the principal component analysis to the extraction of most influential features.Subsequently,we adopt a time delay neural network and include the accuracy of the predicted error in a future time horizon.Utilizing the predicted error,we then design a feed-forward compensate process to improve the control performance.Finally,we demonstrate the effectiveness of the proposed feed-forward compensate process in simulation scenarios.

[1]  Tao Mei,et al.  Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID , 2012 .

[2]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[3]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[4]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Anthony B. Will,et al.  Modelling and control of an automated vehicle , 1997 .

[6]  Richard Johansson,et al.  Carsim: A system to visualize written road accident reports as animated 3D scenes , 2004 .

[7]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[8]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[9]  Ching-Yao Chan,et al.  Automated Driving Maneuvers under Interactive Environment based on Deep Reinforcement Learning , 2018, 1803.09200.

[10]  Marin Marinescu,et al.  Using Neural Networks to Modeling Vehicle Dynamics , 2014 .

[11]  Yang Li,et al.  Motion control of an autonomous vehicle based on wheeled inverted pendulum using neural-adaptive implicit control , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  R. E. Benton,et al.  Nonlinear-gain-optimized controller development and evaluation for automated emergency vehicle steering , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[13]  Alberto Ferreira de Souza,et al.  Self-Driving Cars: A Survey , 2019, Expert Syst. Appl..

[14]  L.I. Silva,et al.  Vehicle dynamics using multi-bond graphs: Four wheel electric vehicle modeling , 2008, 2008 34th Annual Conference of IEEE Industrial Electronics.

[15]  Ching-Yao Chan,et al.  A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).