Wei Sun, Kannan Srinivsan (The Ohio State University)

Being followed by other vehicles during driving is scary and causes privacy leakage (e.g., location), which can make our blood run cold and even make run moves. Moreover, deliberately following the other vehicles may cause significant traffic accidents. The following vehicle needs to maintain an appropriate separation from the following vehicle without getting lost and uncovered. To put the driver’s privacy and safety first, it is essential to discriminate between stalking vehicles (i.e., following abnormal vehicles) and normal following vehicles. However, there are no infrastructure-free and ubiquitous in-vehicle systems that can achieve abnormal following vehicle detection while driving.

To this end, we propose P2D2, a Privacy-Preserving Defensive Driving system that can detect the abnormal following vehicles through the sensor fusion. Specifically, we will use the camera to extract each following vehicle’s following time, and use the IMU sensors (e.g., Gyroscope ) to extract our vehicle’s critical driving behavior (e.g., making a left or right turn). We harness the space diversity of IMU sensing data to remove the artifacts of road surface conditions (e.g., bumps on the road surface) on critical driving behavior (CDB) detection. Then, we leverage the machine learning-based anomaly detection algorithm to detect the abnormal following vehicles based on the following vehicle’s following time and our vehicle’s critical driving behavior within the following time. Our experimental results show the F-1 score of 97.45% for the abnormal following vehicle detection in different driving scenarios during our daily traffic commute.

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