LAUSR

Smartphone built-in sensors are essential components of vehicle steering mode recognition. Related driver assistance systems (DAS) and abnormal driving behavior detection systems have been studied for many years. However, the existing solutions and systems simply collect sensor data with a fixed sliding window and fuse data from multiple sensors using simple thresholds to detect different driving behaviors. The weakness of these solutions can have an adverse impact on the energy consumption and computation complexity of power-limited devices such as smartphones, and may provide coarse-grained results. In this paper, we present a new method to reduce both the energy consumption and the computation complexity, and improve the recognition accuracy of vehicle steering patterns using the following three improvements: 1) a MultiWave filter is designed to replace the fixed sliding window, which is used to identify vehicle steering events; 2) a set of eight statistical sensor features reflecting the vehicle steering modes are identified by extracting statistical features from different sensors and different axes; 3) different machine learning methods are compared based on this feature set in order to improve classifier training (Decision Tree and Random Forest). Evaluation results based on real vehicle datasets show that our improved classifiers have high real-time recognition accuracy of the five most common steering modes: left/right turns, left/right lane changes and U-turns. We also took a simple on-road testing, in which both of the two models detected all the steering behaviors under low vehicle speed.