Dangerous driving behavior detection using video-extracted vehicle trajectory histograms

Chen, Zhijun; Wu, Chaozhong; Huang, Zhen; Lyu, Nengchao; Hu, Zhaozheng; Zhong, Ming; Yang, Cheng; Ran, Bin

doi:10.1080/15472450.2017.1305271

Cited by 58 publications

(33 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key statistical parameters that can capture most of the distribution information of vehicle acceleration a f , relative distance x r , and relative velocity v r are also selected for recognition. The statistical parameters are the maximum, minimum, mean, standard deviation, and 85% percentiles, which were proved useful in previous driving behavior study [20].…”

Section: Statistical Methodmentioning

confidence: 99%

“…Therefore, a more suitable and effective method should be found to identify the driving style. SVM has been widely applied to various kinds of pattern recognition problems, including voice identification, text categorization, and face detection [6,20,21]. In addition, SVM performs well with a limited number of training samples, and SVM has fewer parameters to be determined [22,23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The driver's driving style depends on risky behavior or accident happened during observation. Chen et al [20] defined the dangerous driving behaviors according to criteria as frequent lane changes, abrupt double lane change, and illegal lane occupation. The accidents data are also adopted to determine the risk level of driving behavior [38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Three surrogates, i.e., ITTC, THW, and MMTC, are used to effectively measure the rear-end collision risk and label the driving style. This labeling method is more efficient and objective compared with questionnaires [10] and expert scoring [20]. Then the SVM is applied to build a driving style recognition model.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

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

Xue

Wang

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

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.

show abstract

Section: Statistical Methodmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

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

Xue

Wang

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Some studies proposed Hidden Markov Model (HMM) to detect dangerous driving behaviors [27], which could be challenging with a large number of states to be estimated [28]. SVM also has been widely applied to various kinds of pattern recognition problems, including voice identification, text categorization, and face detection [29,30].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Xue

Xing

et al. 2020

IJERPH

View full text Add to dashboard Cite

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.

show abstract

DriveSense: Adaptive System for Driving Behaviour Analysis and Ranking

Behera¹,

Bhardwaj²,

Rose³

et al. 2022

Machine Learning Techniques for Smart City Applications: Trends and Solutions

View full text Add to dashboard Cite

Dangerous driving behavior detection using video-extracted vehicle trajectory histograms

Cited by 58 publications

References 29 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

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

DriveSense: Adaptive System for Driving Behaviour Analysis and Ranking

Contact Info

Product

Resources

About