Far-field sensing in partial VANET environment

Today’s vehicles are capable of detecting environmental traffic participants, such as other vehicles, pedestrians, traffic lights etc, and communicating with each other or infrastructures. Typical on-board detectors include LiDAR, camera and so on. These vehicles which can make driving decisions based on the detected information without human intervention are named CAV (connected and autonomous vehicles). However, in a long period, the road traffic is mixed by traditional vehicles (human driven vehicles, or HVs) and CAV. The system can only “see” the near field vehicles around the CAVs by means of on-board detectors or VANET (vehicular ad hoc network). Far-field vehicles are either too far away or covered by near-field vehicles. In order to enhance the sensing capabilities of VANET or CAV, the manuscript propose a far-field vehicles sensing method, called F2-sensing. The method combines the deep learning and the car following logic. The rationale is that, as the vehicles react to downstream vehicles’ states variation, when the CAVs and the near field vehicles’ states are known, the downstream vehicles’ existence and its real-time location can be estimated. The proposed method is tested against real world dataset, which proves the usefulness of the method.