Modeling of Autonomous Vehicle Operation in Intelligent Transportation Systems

The past decade has seen autonomous vehicles become the subject of considerable research and development activity. The majority of these advances have focused on individual vehicles, rather than the interactions that result when autonomous (unmanned) and conventional (manned) vehicles come together in an intelligent transportation system. The robustness of autonomous vehicles to contingencies caused by unpredictable human behavior is a critical safety concern. Assuring the reliability, availability, security, and similar non-functional attributes of autonomous vehicles is just as critical. The doctoral research proposed in this paper centers on developing models capable of accurately representing environments where manned and unmanned vehicles coexist. An established macroscopic transportation model serves as the basis for the proposed work, and will be extended to differentiate between manned and autonomous vehicles. Stochastic methods will be applied to reflect the non-determinism of the operating environment, especially as related to driver behavior, and will facilitate analysis of robustness. The goal is to capture both basic operation of autonomous vehicles, as well as advanced capabilities such as platooning and robotic adaptation. The insights gained from these models are expected to facilitate the design of intelligent transportation systems that are both safe and efficient.