Dangerous Driving Behavior Recognition Based on Hand Trajectory

• Published in: Sustainability Vol. 14; no. 19; p. 12355
• Main Authors: Liu, Wenlong, Li, Hongtao, Zhang, Hui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Behavior; Comparative analysis; Distracted driving; Hand; Identification and classification; Machine learning; Memory; Neural networks; Support vector machines; Sustainability; Time series; Velocity; Vision systems; Wearable computers; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su141912355

Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by driving simulation test, and 30 subjects’ hand trajectory data were used to determine the dangerous driving behavior label. The matching degree of hand trajectory data of 15 subjects was calculated based on the dangerous driving behavior recognition model, and the threshold of dangerous driving behavior recognition was determined according to the calculation results. Finally, the dangerous driving behavior recognition algorithm and neural network algorithm are compared and analyzed. The dangerous driving behavior recognition algorithm has a fast calculation speed, small memory consumption, and simple program structure. The research results can be applied to dangerous driving behavior recognition and driving distraction warning based on wrist wearable devices.