Traffic accidents involving fatigue driving and their extent of casualties

Zhang, Guangnan; Yau, Kelvin K. W.; Zhang, Xun; Li, Yanyan

doi:10.1016/j.aap.2015.10.033

Cited by 260 publications

(129 citation statements)

References 58 publications

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“…Traffic accident analysis covers a wide range of topics such as analysis of traffic accident injury severity [37]- [39], relationship analysis between personality and traffic accident [40]- [42], accident hotpots detection or prediction [43], [44], risk factors analysis [45], and traffic accident classification [46]. As shown in reference [47], nearly about thirty approaches were applied to traffic accident analysis.…”

Section: A Traffic Accident Analysismentioning

confidence: 99%

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

Wang

Liu

Zhao

2017

IEEE Trans. Intell. Veh.

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Big data has shown its uniquely powerful ability to reveal, model, and understand driver behaviors. The amount of data affects the experiment cost and conclusions in the analysis. Insufficient data may lead to inaccurate models while excessive data waste resources. For projects that cost millions of dollars, it is critical to determine the right amount of data needed. However, how to decide the appropriate amount has not been fully studied in the realm of driver behaviors. This paper systematically investigates this issue to estimate how much naturalistic driving data (NDD) is needed for understanding driver behaviors from a statistical point of view. A general assessment method is proposed using a Gaussian kernel density estimation to catch the underlying characteristics of driver behaviors. We then apply the Kullback-Liebler divergence method to measure the similarity between density functions with differing amounts of NDD. A maxminimum approach is used to compute the appropriate amount of NDD. To validate our proposed method, we investigated the car-following case using NDD collected from the University of Michigan Safety Pilot Model Deployment (SPMD) program. We demonstrate that from a statistical perspective, the proposed approach can provide an appropriate amount of NDD capable of capturing most features of the normal car-following behavior, which is consistent with the experiment settings in many literatures.

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Section: A Traffic Accident Analysismentioning

confidence: 99%

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

Wang

Liu

Zhao

2017

IEEE Trans. Intell. Veh.

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“…The major issue seems to be the person driving the vehicle not being attentive enough to notice the traffic signs in due time. A safety harness should be placed which automates the recognition of traffic signs and alerts the driver in due time as they might suffer from fatigue or other causes [5], [6]. Solving this can greatly reduce the number of casualty in major roads and highways.…”

Section: Introductionmentioning

confidence: 99%

Total Recall: Understanding Traffic Signs Using Deep Convolutional Neural Network

Saha

Kamran

Sabbir

2018

2018 21st International Conference of Computer and Information Technology (ICCIT)

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Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening worldwide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular dataset, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity.

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“…. , −1 (for time window = 1 and its subsequent time windows), cannot be observed and cannot be directly applied either to (7) for estimating state transition probability or to (8) for predicting the state of the target time window. In order to meet the requirement for prediction and to improve the prediction accuracy, a MNLbased Markov chain algorithm with recursive feature variable estimation (referred to as RMNL-Markov) was proposed, as described in Algorithm 1.…”

Section: Outputsmentioning

confidence: 99%

“…In order to meet the requirement for prediction and to improve the prediction accuracy, a MNLbased Markov chain algorithm with recursive feature variable estimation (referred to as RMNL-Markov) was proposed, as described in Algorithm 1. The key idea of the proposed algorithm is that the state probability distribution could be determined by both of the Euclidean distance (to risk state cluster centroids) based estimation method (see (5)) and Markov property (see (8)), which leads to a set of three equations solving the three-dimension future feature variable x = [RL avg , RL last , CON ], = 1, . .…”

Section: Outputsmentioning

confidence: 99%

“…However, it is too simplified to describe the whole driving risk evolution process from the formation of risk to the occurrence of accident with only a single warning parameter, and more complex models and algorithms are required for more intelligent driving risk prediction. Although such advanced collision warning models/algorithms have received increasing attention over the years, many published studies usually only consider the vehicle operating characteristics (such as vehicle's relative position to potential conflicts, vehicle speed, and acceleration characteristics) [5][6][7], while ignoring the impact of dynamic driver behavior, road, and environmental characteristics on the driving risk status, which has been researched and confirmed in many traffic accident causation studies [8][9][10]. Thus, a driving risk prediction method that could reflect the dynamic changes in driver behavior, road, and environment is needed.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Driving Risk Prediction Based on Markov Chain Model

Xiong

Chen

2018

Discrete Dynamics in Nature and Society

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A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The "100-car" natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state.

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Traffic accidents involving fatigue driving and their extent of casualties

Cited by 260 publications

References 58 publications

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

Total Recall: Understanding Traffic Signs Using Deep Convolutional Neural Network

Vehicle Driving Risk Prediction Based on Markov Chain Model

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