On the Feasibility of Securing Vehicle-Pavement Interaction

Abstract

Road surface information (e.g., smooth road or bumpy road with potholes or bumps) is important for safe driving (i.e., it's necessary to be aware of the road surface conditions during driving). However, the high-cost sensor (e.g., LiDAR and camera) based road surface sensing approaches cannot work properly in inclement weather conditions (e.g., fogging and snowing) due to the line-of-sight requirement. The low-cost and ubiquitous smartphone-based road surface sensing approach is not reliable and safe to use, since it relies on the vibration of the vehicle body to sense the road surface (i.e., the vehicle's tires need to touch the bumps on the road surface). Can we automate the contact-free road surface sensing with low-cost sensors for safe driving without requiring the vehicle's tires to touch the bumps on the road surface? In this paper, we propose Tago, a system that can achieve contact-free road surface sensing with commodity passive RFID tags. Instead of deploying RFID tags or readers along the road or lamp post (i.e., infrastructure-based deployment), we deploy the reader inside of the vehicle and attach the tag and the reader's antenna at the front end of the vehicle like the vehicle's headlights (i.e., infrastructure-free deployment). However, there is a great challenge to obtain the clean reflection from the road surface, since the reflection may be drown in the backscattered signals due to multipath effect. Moreover, it is not reliable to use the composite signals received at the reader to sense the road surface conditions. Therefore, we first comprehensively analyse the variation of composite signals received at the reader. Then, we propose a signal cancellation approach to extract the clean reflections from the road surface, such that we can accurately sense the road surface conditions for safe driving. Our experiments with different vehicles (e.g., Honda Civic Frankenfish, Folsom, Flutter and CR-V Warner) driven on different roadways (e.g., urban and residential area) show that Tago can effectively sense the road surface information.