Traffic Event Augmentation via Vehicular Edge Computing: A Vehicle ReID based Solution

Traditional traffic event monitoring and detection solutions mainly rely on roadside surveillance cameras. However, existing solutions cannot be applied for traffic event augmentation due to both restricted monitoring angles and limited camera coverage. Therefore, this paper investigates a novel architecture for traffic event augmentation via vehicular edge computing. In particular, multiple vehicles can collaborate with roadside infrastructures for detecting, re-identification and augmenting certain traffic event via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To enable such an application, we formulate the problem of multi-view augmentation task offloading (MATO) by considering the heterogeneous capabilities of vehicles and edge servers, which aims at minimizing average request delay. On this basis, we design the offloading scheduling framework and propose an adaptive real-time offloading algorithm (ARTO), which makes online offloading decision of object detection and re-identification, by balancing real-time workload among heterogeneous devices. Finally, we implement the hardware-in-the-loop testbed for performance evaluation. The comprehensive results demonstrate the superiority of the proposed algorithm in various realistic traffic scenarios.