LAUSR

Vehicular cloud computing (VCC) has been widely utilized to enhance traffic management and road safety both in the static and dynamic scenarios. In VCC, the onboard resources of involved smart vehicles (SVs) would be integrated as a resource pool and collaboratively accommodate various computation tasks. In this system, SVs in a section of the road can be grouped as a vehicular cloud to complete the detection job, e.g., collecting and preprocessing the road data. Then, an SV will be selected to aggregate all SVs’ detection results in the road section and upload the results to the remote monitoring platform via base stations (BSs). However, due to the high dynamics of vehicular mobility and the poor coverage of BSs in some areas, this vision is challenging to achieve. In this article, we propose a collaborative road detection system and schedule the detection task to different SVs in the road section. Specifically, 1) we model the collaborative detection process and formulate it as a task scheduling problem to minimize the response time, which is NP-hard. An adaptive location-aware scheduling scheme is proposed for task scheduling and 2) as there are road sections without coverage of any BS, therefore we have to utilize the SVs on the opposite lane to transmit the aggregation result in these road sections. Accordingly, an uploading strategy is proposed to decide to upload the feedback through SVs on the opposite lane or in the coverage area of the next BS. Extensive experiments show that our scheme can significantly reduce the response time overall and is close to the optimal solution.