On-Edge Driving Maneuvers Detection in Challenging Environments from Smartphone Sensors

Traffic fatalities are increasing in developing countries where there are few investments in road safety. Culture and road conditions also affect driving habits. Therefore, automatic detection and reporting of driver behavior to concerned entities can potentially save lives. In particular, we analyze a driving maneuvers dataset collected from one environment (country) but tested in another environment with aggressive driving habits and irregular road conditions. We also develop an on-edge system with fast response time to serve users on a large scale. Specffically, we propose an approach for detecting aggressive and normal events using random forest classifier. We utilize the accelerometer and gyroscope smartphone readings to classify driving maneuvers events to five types (aggressive acceleration, suddenly break, aggressive turn right, aggressive turn left, and normal). We achieved an accuracy of only 63.4% by training our model on an available dataset collected from a foreigner environment and tested on our environment. The lowest precision value was 54% while the lowest recall was 42%. However, we achieved an accuracy of 98.4% when augmenting an available dataset with data collected with our application. The lowest precision value was 98% while the lowest recall was 90%. From the results, it is shown that the available datasets do not generalize well to different driving habits and road conditions. Finally, an implementation of the random forest model using OpenCV on an Android platform is analyzed.