Recognizing driver behaviors is becoming vital for in-vehicle systems that seek to reduce the incidence of car accidents rooted in cognitive distraction. In this paper, we harness the exceptional feature extraction abilities of deep learning and propose a dedicated Interwoven Deep Convolutional Neural Network (InterCNN) architecture to tackle the accurate classification of driver behaviors in real-time. The proposed solution exploits information from multi-stream inputs, i.e., in-vehicle cameras with different fields of view and optical flows computed based on recorded images, and merges through multiple fusion layers abstract features that it extracts. This builds a tight ensembling system, which significantly improves the robustness of the model. We further introduce a temporal voting scheme based on historical inference instances, in order to enhance accuracy. Experiments conducted with a real world dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73.97 accuracy, and 5 aggregated behaviors with 81.66 accuracy. Our architecture is highly computationally efficient, as it performs inferences within 15ms, which satisfies the real-time constraints of intelligent cars. In addition, our InterCNN is robust to lossy input, as the classification remains accurate when two input streams are occluded.
In @cite_36 , Liu employ Laplacian Support Vector Machine (SVM) and extreme learning machine techniques to detect drivers' distraction, using labelled data that captures vehicle dynamic and drivers' eye and head movements. Experiments show that this semi-supervised approach can achieve up to 97.2 Ragab compare the prediction accuracy of different machine learning methods in driving distraction detection @cite_7 , showing that Random Forests perform best and require only 0.05 s per inference. Liao consider drivers' distraction in two different scenarios, i.e., stop-controlled intersections and speed-limited highways @cite_25 . They design an SVM classifier operating with Recursive Feature Elimination (RFE) to detect driving distraction. The evaluation results suggest that by fusing eye movements and driving performance information, classification accuracy can be improved in stop-controlled intersection settings.
@cite_36 @cite_25 @cite_7
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Real-time driver distraction detection is the core to many distraction countermeasures and fundamental for constructing a driver-centered driver assistance system. While data-driven methods demonstrate promising detection performance, a particular challenge is how to reduce the considerable cost for collecting labeled data. This paper explored semi-supervised methods for driver distraction detection in real driving conditions to alleviate the cost of labeling training data. Laplacian support vector machine and semi-supervised extreme learning machine were evaluated using eye and head movements to classify two driver states: attentive and cognitively distracted. With the additional unlabeled data, the semi-supervised learning methods improved the detection performance ( @math -mean) by 0.0245, on average, over all subjects, as compared with the traditional supervised methods. As unlabeled training data can be collected from drivers' naturalistic driving records with little extra resource, semi-supervised methods, which utilize both labeled and unlabeled data, can enhance the efficiency of model development in terms of time and cost. Driver distraction has been identified as one major cause of unsafe driving. The existing studies on cognitive distraction detection mainly focused on high-speed driving situations, but less on low-speed traffic in urban driving. This paper presents a method for the detection of driver cognitive distraction at stop-controlled intersections and compares its feature subsets and classification accuracy with that on a speed-limited highway. In the simulator study, 27 subjects were recruited to participate. Driver cognitive distraction is induced by the clock task that taxes visuospatial working memory. The support vector machine (SVM) recursive feature elimination algorithm is used to extract an optimal feature subset out of features constructed from driving performance and eye movement. After feature extraction, the SVM classifier is trained and cross-validated within subjects. On average, the classifier based on the fusion of driving performance and eye movement yields the best correct rate and F-measure ( @math for stop-controlled intersections and @math for a speed-limited highway) among four types of the SVM model based on different candidate features. The comparisons of extracted optimal feature subsets and the SVM performance between two typical driving scenarios are presented. Driver distraction and fatigue are considered the main cause of most car accidents today. This paper compares the performance of Random Forest and a number of other well-known classifiers for driver distraction detection and recognition problems. A non-intrusive system, which consists of hardware components for capturing the driver’s driving sessions on a car simulator, using infrared and Kinect cameras, combined with a software component for monitoring some visual behaviors that reflect a driver’s level of distraction, was used in this work.