ADAPTIVE ROUTING OF AUTONOMOUS CONNECTED VEHICLES FOR SAFE AND EFFICIENT TRANSPORTATION SYSTEMS IN UNCERTAIN AND DYNAMIC ENVIRONMENTS
Unexpected traffic incidents and the consequent congestion have significant negative impact on the economy and the quality of people’s lives. Transportation systems can be improved by technologies of autonomous and connected vehicles, which allow vehicular control and communications, and enable effective information dissemination among vehicles. Adaptive vehicle routing is one of the most promising applications of such technologies to achieve better safety and efficiency of transportation systems. Processing and learning real-time traffic information from probe vehicles and social media (e.g., Twitter), vehicles can be routed safely and efficiently in a non-myopic way. Adaptive routing of autonomous connected vehicles has two main goals, 1) at the individual vehicle level, how to provide a routing strategy for each individual vehicle to minimize accident risk, crash rate, and travel time; 2) at the system management level, how to provide globally optimal routes for all the vehicles over time so that the total system crash rate and travel cost can be minimized.
Supported by the Russian Science Foundation under Grant No. 15-11-10032 from 2015-2017.
CoPI: Qiang Qu in collaboration with Siyuan Liu (Carnegie Mellon University)