Driver Distraction Detection Through Driving Performance And Eye Movement: From Feature Extraction To Classifier Design

Driver distraction has been identified as one major cause of unsafe driving. Distraction is highly demanding on the real-time detection compared with fatigue detection. It is unclear about the common features of different distraction types under different driving contexts. To solve those problems above, the research on driver distraction indicated by driving performance and eye movement was conducted; from feature extraction to real-time detection.The experiments were based on driving simulation, two typical driving scenarios were reconstructed; the stop-controlled intersection(urban scenario) and the speed limited highway(highway scenario). There are two types of distraction in the study; visual distraction and cognitive distraction. Distracted driving is defined as driving with specifically selected secondary tasks; therefore the distraction detection is a 0-1 question. The statistical analysis of candidate features from driving performance and eye movement was applied first. The optimal feature subsets were extracted by applying Support Vector Machine – Recursive Feature Elimination(SVM-RFE). The significant features were further extracted for distraction detection among different combinations of distraction type and driving scenario. By testing the performance of SVM classification based on the optimal feature subsets, the advantages of multi-source information fusion were quantified and the Yerkes-Dodson Law was therefore partially validated. Finally based on the SVM classification model, the extracted features as input, the real-time distraction detection algorithm was designed and cross validatd. The optimal combination of the algorithm parameters was determined. The performance of this real-time distraction detection algorithm is good on both correct rate and rapidity.Stop-controlled intersection and speed limited highway were reconstructed as the driving scenarios. Two types of cognitive secondary tasks and two types of visual secondary tasks were applied to generate the specific kind of distraction. The Peripheral Detection Task device(PDT) used for quantifying the cognitive workload during driving was modified into a new version called Detection Response Task device(DRT) with improved portability. Measured by DRT, the cognitive workload is regarded as the important reference for distraction detection. Driving and eye movement data were logged under two conditions; normal driving and distracted driving. Twenty-two subjects participated in the experiment of urban scenario and 16 subjects for the experiment of highway scenario. The candidate features of driving performance and eye movement are calculated and statistically analyzed. The results indicate the relationship between these features impacted by the distracted driving and driver characteristics like age and gender.To cope with high-dimension and small sample size of collected data, SVM-RFE is adopted to extract the optimal feature subset for best classification performance. Based on the analysis of the structure and importance of extracted optimal feature subsets, the common and significant features across different distraction types and driving scenarios are determined. By testing the performance of SVM classification based on the optimal feature subsets, the advantages of multi-source information fusion are quantified. Seen from the measured workload under two driving scenarios, it is found that under high-workload driving scenario, the fusion of driving performance and eye movement features yield significantly improved correct rates of distraction recognition. Through this finding, the Yerkes-Dodson Law is validated and the importance of driving context is proved from the perspective of driving workload. These results provide method and data support for the feature extraction of distraction detection.A SVM-based real-time distraction detection algorithm with both driving performance and eye movement features as inputs is designed and cross validated. The algorithm performs well in both correct rate and detection rapidity which is also adaptive to different driving scenarios and distractions. The length of calculation time window and the overlap rate of it are two parameters of the algorithm. The optimal combination of these parameters, 5-second-length calculation window with 75% overlap rate, is determined by the best comprehensive performance of distraction detection. The algorithm gets correct rates on average between 86.6% and 98.9% while the degree of decision in advance(the indicator of the detection rapidity) reaches 88.7% to 95.0%. More specific, with 30 s as the length of extracted event sample, the distracted status of the driver can be recognized within 6.5s to 9.0s which indicates the good performance of detection rapidity as well as accuracy.