Real-time traffic monitoring has had widespread success via crowd-sourced GPS data. While drivers benefit from this low-level, low-latency road information, any high-level traffic data such as road closures and accidents currently have very high latency as such systems rely solely on human reporting. Increasing the detail and decreasing the latency of this information can have significant value. In this paper we explore this idea by using a camera along with an in-vehicle computer to run computer vision algorithms that continuously observe the road conditions in high-detail. Abnormalities are automatically reported via 4G LTE to a local server on the edge, which collects and stores the data, and relays updates to other vehicles inside its zone. In this paper we develop and test such a system, which we call LiveMap. We demonstrate its accuracy on detecting hazards and characterize the system latency achieved.
Christensen, K., Mertz, C., Pillai, P., Hebert, M., Satyanarayanan, M.
Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications (HotMobile ’19), Santa Cruz, CA, February 2019