A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Abstract

The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

Driving Maneuvers Recognition and Classification Using A Hyprid Pattern Matching and Machine Learning

“…A wide range of techniques have been developed to recognize and classify driving maneuvers in the literature. In recent years, driving maneuver classification using machine learning techniques has received increasing attention for the evaluation of driving patterns and drivers' profiling [4]. Three machine learning techniques have been used in this study, namely RF, SVM, and KNN, for recognizing and classifying driving maneuvers.…”

“…Driving behaviors can be assessed from two different perspectives namely; drivers' actions or the vehicle's dynamic state. In the first approach the driver is considered as the focal element where a set of parameters that affect the driver's vigilance and attention are continuously observed to predict his/her competence to achieve the driving course in a robust and safe manner [4]. Drivers' state monitoring systems may contain different modules, such as facial recognition systems, physiological signals monitoring and drivers' interaction and control.…”

“…Drivers' state monitoring systems may contain different modules, such as facial recognition systems, physiological signals monitoring and drivers' interaction and control. For example, drivers' interaction and control, combined with facial recognition, have been shown to be effective in detecting driver fatigue, drowsiness, and distraction [4].…”

“…On the other hand, unsupervised learning approaches, such as K-means clustering [25] and Principal Component Analysis algorithms [21] can infer and generate rules and threshold-based discriminators for clustering purposes. During the past decade, different methodologies and techniques have been proposed and implemented successfully in the field of driving behavior classification [4,8].…”

Research on vehicle behavior prediction based on high precision trajectory data

“…Hou [9] established the recognition model of the lane-changing intention of drivers with different styles and studied the influence of driving style. Zhang [10] used the behavior recognition model and intention prediction model of Bi-LSTM (bidirectional long-term and short-term memory networks) to predict the lane-changing intention of the target vehicle. Zheng uses a convolutional neural network [11] to locate and predict the lateral position of vehicles on the road.…”