A Novel Concept of Traffic Data Collection and Utilization: Autonomous Vehicles as a Sensor

Urban traffic state estimation aims at providing accurate and reliable information about traffic flow characteristics, which can be used for urban traffic management. Traditional estimation approaches mainly use loop detectors and/or floating car data, which are labour- and cost-intensive. With the ongoing technological development in autonomous driving, more and more research focuses on the use of onboard sensor data. In this paper, a novel concept for the collection and utilization of traffic data is presented: Autonomous Vehicles as a Sensor. The proposed concept capitalizes on the advanced sensor technologies deployed in autonomous vehicles, particularly those employed in Mobility-on-Demand services, to collect link-level traffic states. These microscopic traffic states are further utilized by e.g., Mobility-on-Demand operators to estimate network-level traffic states. A first proof of concept was examined through a case study in a microscopic traffic simulation with a grid network and generic demand. The results demonstrate that both, moving and parked autonomous vehicles, can effectively contribute to the estimation of the macroscopic fundamental diagram. When the results from both are combined, the resulting estimation yields the most accurate fit compared to the ground truth. These findings underline the potential of the 'Autonomous Vehicles as a Sensor' concept for accurate and reliable traffic state estimation.