In smart cities of the future, how to sense, understand, and manage urban-scale vehicular systems, e.g., taxis, in an autonomous fashion (with little or no human intervention) is an essential topic to improve urban mobility efficiency, such as shorter waiting time for passengers, lower cruising mileage for drivers, and higher revenues for vehicular system operators. However, the current management strategies for vehicular systems are mainly based on individual-level data knowledge, ignoring rich information from a fleet perspective. In this project, the investigators design and implement a fleet-oriented management strategy for vehicular systems, which utilizes real-time data from sensors installed in all vehicles to improve the overall performance of the vehicular system. In particular, the investigators aim to improve the taxi system performance by using onboard cameras to detect waiting passengers on streets and share this information with nearby vehicles and dispatch centers to pick up these passengers through a dispatching strategy. The research team will develop a clear understanding of how to design an adaptive autonomous vehicular sensing and dispatching strategy to improve urban mobility efficiency from a fleet-oriented perspective, with potential applications to future fully autonomous fleets. Such an understanding on vehicular sensing and dispatch will improve the quality of the every-day life such as more efficient commute for passengers, and lower energy uses for drivers, and finally improve the environment for the society by low vehicle mileage.
This research develops a fleet-oriented sensing and control framework to enable seamlessly integration of historical and real-time data within a fleet for adaptive vehicular sensing, modeling, and control. Specifically, this project studies how to best use spatiotemporally-correlated contextual information (e.g., vehicular mobility, service demand, disruptive events) among vehicles. Although such correlations decay over time and distance, it is possible to enable autonomous vehicular sensing, modeling, and control adaptively based on the following research to develop novel services: (i) reconfigurable fleet-wide coordinated sensing by autonomously learning correlated vehicular interactions; (ii) models of mobility phenomena by collectively interpreting implicit data from different vehicles with a combination of deep learning, structured learning, and attribute-based learning; (iii) designs of robust dispatching strategies with uncertainty sets and receding horizon control frameworks by iteratively considering fleet-wide knowledge to improve mobility efficiency.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
