Awareness on Present and Future Trajectory of Vehicle Using Multiple Hypotheses in the Mixed Traffic of Intersection

Abstract

In the transition period, autonomous vehicles are mixed with unconnected traffic occupants, such as non-autonomous vehicles and pedestrians, resulting in a major hurdle toward autonomy in urban areas, especially at intersections. In this context, the cooperative-intelligent transportation system (C-ITS) affords a promising solution to achieve a breakthrough with its omniscient sensors network and computing capability. From the perspective of a C-ITS-based service, the trajectory of non-autonomous vehicle is a critical uncertainty that resides at the intersection. Therefore, this paper proposes a unique interactive framework, which is installed in the edge server of C-ITS and can estimate the present trajectories and predict the future trajectories of the non-autonomous vehicles at intersections. The proposed framework was based on multiple hypotheses of possible maneuvers that formed the confined prior set to reduce the high uncertainties posed by the complicated environment of the urban intersection. The resulting all-in-one framework provided a stable long-term trajectory prediction with intrinsic maneuver classification and improved tracking in an integrated way by incorporating the interactions between the multiple hypotheses. This situation awareness can assist autonomous vehicles to drive safely and defensively. The proposed framework was verified using a dataset collected at a real urban intersection.