SafeShareRide: Edge-based attack detection in ridesharing services

Abstract

Ridesharing services, such as Uber and Didi, have enjoyed great popularity in our daily life. However, it remains a big challenge to guarantee passenger’s and driver’s safety. In this paper, we propose an edge-based attack detection in ridesharing services, namely SafeShareRide, which can detect dangerous events happening in the vehicle in near real time. The detection of SafeShareRide consists of three stages: speech recognition, driving behavior detection, and video capture and analysis. In our preliminary work, we implemented the three detection stages by leveraging open source algorithms and demonstrated the applicability of SafeShareRide. Furthermore, we identified several observations for smart phone based edge computing systems.