Poster Abstract: Analysis and Evaluation of Driving Behavior Recognition Based on a 3-axis Accelerometer Using a Random Forest Approach
Wangjing Cao, Xin Lin, Kai Zhang, Yuhan Dong, Shao‐Lun Huang, Lin Zhang · Information Processing in Sensor Networks · 2017
Understanding human drivers' behavior is critical for the self-driving cars, and has been intensively studied in the past decade. We exploit the widely available camera and motion sensor data from car recorders, and propose a hybrid method of recognizing driving events based on the random forest approach. The classification results are analyzed by comparing different features, classifiers and filters. A high accuracy of 98.1\% on driving behavior classification is obtained and the robustness is verified on a dataset including 2400 driving events.