A survey on driver behavior detection techniques for intelligent transportation systems

Chhabra, Rishu; Verma, Seema; Krishna, C. Rama

doi:10.1109/confluence.2017.7943120

Cited by 111 publications

(46 citation statements)

References 27 publications

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“…Driving simulator [24][25][26] Collect drivers' behavior in designed and controlled driving scenarios Driving behavior observed in the simulator may not always correspond to real-world driving Traffic video [35][36][37] Low expense; Easy to collect enormous vehicle data in a short time; Observe vehicle interaction in real traffic flow This paper is organized as follows. Section 2 presents the related work on driving behavior data analysis and machine learning algorithms.…”

Section: Collection Approaches Advantages Disadvantagesmentioning

confidence: 99%

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Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Xue

Wang

et al. 2019

Journal of Advanced Transportation

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Rear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition method based on vehicle trajectory data extracted from the surveillance video. First, three rear-end collision surrogates, Inversed Time to Collision (ITTC), Time-Headway (THW), and Modified Margin to Collision (MMTC), are selected to evaluate the collision risk level of vehicle trajectory for each driver. The driving style of each driver in training data is labelled based on their collision risk level using K-mean algorithm. Then, the driving style recognition model’s inputs are extracted from vehicle trajectory features, including acceleration, relative speed, and relative distance, using Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and statistical method to facilitate the driving style recognition. Finally, Supporting Vector Machine (SVM) is applied to recognize driving style based on the labelled data. The performance of Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) is also compared with SVM. The results show that SVM overperforms others with 91.7% accuracy with DWT feature extraction method.

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Section: Collection Approaches Advantages Disadvantagesmentioning

confidence: 99%

“…Traffic video contains all vehicle trajectory data on the road and can offer a full view of vehicle's interactions with other during car-following and lane-change, etc. However, extracting vehicle trajectory from video could be challenging, which depends on video quality and algorithms used [35][36][37].…”

Section: Literature Reviewmentioning

confidence: 99%

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Xue

Wang

et al. 2019

Journal of Advanced Transportation

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“…Example assisting systems include driver fatigue monitoring system, driver hypo-vigilance system, and drunk-driving prevention system [9], [10].…”

Section: A Driver Assisting Systems In Vehiclesmentioning

confidence: 99%

Cloud-Assisted Safe Driving Framework

Kao¹

2017

IJMO

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Abstract-As the technologies for vehicular safety grow and mature, several advanced driving assisting systems are deployed to mitigate negative impacts on driving safety and protect drivers and passengers efficiently. However, the rate of fatalities and serious injuries in traffic accidents is still high. Based on the statistics from the Ministry of the Interior and National Highway Police Bureau in Taiwan, most critical accidents are caused by the circumstance that drivers do not aware the potential danger to their surroundings. Currently, several driving assisting systems deployed in vehicles can sense surrounding information for potential accidents prevention in time. In order to prevent potential accidents in advance for safer driving, a cloud-assisted safe driving framework is proposed in this paper. The framework leverages sensing devices in vehicles to acquire particular information of the front vehicles. The information will be acquired, recognized, and analyzed based on historical data preserved in the cloud. In addition, the information and comments on social network sites based on the recognized information will be collected and analyzed. Based on historical data and social information, a reputation for the recognized vehicle will be calculated in the cloud. Thus, corresponding suggestion based on the reputation is given to the drivers to perform defensive driving if necessary. Through the cloud-assisted safe driving framework, the driving safety can be achieved in advance efficiently.

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“…Compared to naturalistic driving study and driving simulator, video surveillance deployed on the roadside can provide a large amount of traffic environment data and vehicle trajectory data at a relatively affordable expense [10]. However, driving behavior extraction from the video can be time-consuming and computation-intensive [11][12][13]. Therefore, it is very important to find effective features based on the data extracted from the video to aid in driving pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting

Wang

Xue

Xing

et al. 2020

IJERPH

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Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. First, a surrogate measurement of collision risk, called Average Crash Risk (ACR), is proposed to calculate a vehicle’s crash risk. Second, the driver’s driving aggressiveness is determined by his/her ACR with three anomaly detection methods. Third, we train classification models to identify aggressive drivers using partial trajectory data. Three imbalanced class boosting algorithms, SMOTEBoost, RUSBoost, and CUSBoost, are compared with cost-sensitive AdaBoost and cost-sensitive XGBoost. Additionally, we try two resampling techniques with AdaBoost and XGBoost. Among all algorithms tested, CUSBoost achieves the highest or the second-highest Area Under Precision-Recall Curve (AUPRC) in most datasets. We find the discrete Fourier coefficients of gap as the key feature to identify aggressive drivers.

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A survey on driver behavior detection techniques for intelligent transportation systems

Cited by 111 publications

References 27 publications

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

Cloud-Assisted Safe Driving Framework

Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting

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