Accurate, up-to-date maps of transient traffic and hazards are invaluable to drivers, city managers, and the emerging class of self-driving vehicles. We present LiveMap, a scalable, automated system for acquiring, curating, and disseminating detailed, continually-updated road conditions in a region. LiveMap leverages in-vehicle cameras, sensors, and processors to crowd-source hazard detection without human intervention. We build a real-time simulation framework that allows a mix of real and simulated components to be tested together at scale. We demonstrate that LiveMap can work well at city scales within the limits of today's cellular network bandwidth. We also show the feasibility of accurate, in-vehicle, computer-vision-based hazard detection.
Hu, W., Feng, Z., Chen, Z., Harkes, J., Pillai, P., Satyanarayanan, M.
Proceedings of the 20th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2017), Miami Beach, FL, November 2017