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.

Highlights

  • The intelligent driver assistance system (i-DAS) plays an important role in improving vehicle efficiency, avoiding accidents, and alleviating road congestion

  • Support Vector Machine (SVM), K nearest neighbors (KNN), decision tree, and AdaBoost Trees are widely used in classification problems and their performance on brake intention prediction are compared with random forest (RF)

  • A novel composite machine learning approach is proposed for control-oriented prediction of driver brake intention and intensity

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Summary

Introduction

The intelligent driver assistance system (i-DAS) plays an important role in improving vehicle efficiency, avoiding accidents, and alleviating road congestion. Zhao et al [17] proposed a layering hidden Markov model and adaptive neuro-fuzzy inference system (LHMM/ANFIS) coupled braking intention recognition model, which was applied to develop the Automated Mechanical Transmission (AMT) shift control strategy in order to improve the safety and braking energy recovery efficiency. Ding et al [21] employed least mean square method to predict wheel pressure and tire–road friction based on hydraulic response models developed using control parameters in ABS. The brake intention and intensity are usually bound and recognized according to the pedal displacement and the state parameters of the vehicle (velocity, acceleration, etc.) using machine learning methods. The investigations on multi-step or long term prediction of brake intention and intensity are essential to the development of an intelligent braking system to enhance brake safety and efficiency, and the related studies are scarce.

Test Vehicle and Its Braking System
Data Collection
Hybrid Machine Learning Framework
Fuzzy C-means Clustering Algorithm
Brake Intention Labeling Results
ReliefF Rank Importance Analysis
Random Forest Classification Algorithm
Performance Analysis of Brake Intention Prediction
RReliefF-Based Rank Importance Analysis
NARX Network
Performance Analysis of Brake Intensity Prediction
Findings
Conclusions
Full Text
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