Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Jianhao Zhou, Jing Sun, Longqiang He, Yi Ding, Hanzhang Cao and Wanzhong Zhao
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Jianhao Zhou: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Jing Sun: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Longqiang He: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Yi Ding: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Hanzhang Cao: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Wanzhong Zhao: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Energies, 2019, vol. 12, issue 13, 1-20

Abstract: Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

Keywords: Brake intention; brake intensity; machine learning; electric vehicle; regenerative brake (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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