Adaptive Safety Context Information for Vulnerable Road Users with MEC Support

Cooperative Vulnerable Road User (VRU) collision avoidance aims at preventing potential accidents between VRUs and vehicles by exchanging context information. In this paper, we present a Multi-access Edge Computing (MEC)-based VRU safety system as an alternative to earlier purely ad-hoc communication-based ones, in which VRU smartphones utilize the cellular connection to frequently send context messages to a MEC server. However, in such safety systems, calculating context information on smartphones, which are already resource-restricted, could lead to reduced battery lifetime and, thus, to poor user experiences. To deal with this issue, we propose an adaptive approach for VRU context information calculation, which considers the use of computation offloading when needed in order to save energy while still ensuring timeliness. As a baseline, we use our machine learning application for determining pedestrian activities. Both experimental and simulation results suggest that it is worth to offload context information computation to the MEC when the updating interval or the sensor sampling frequency is low, i.e., the amount of raw data collected is small; otherwise, local execution is preferable. We see our results as a basis for designing more energy-efficiency calculation models for VRU safety systems.