Adaptive Decentralized Sensor Fusion for Autonomous Vehicle: Estimating the Position of Surrounding Vehicles

The tracking accuracy of nearby vehicles determines the safety and feasibility of driver assistance systems or autonomous vehicles. Recent research has been active to employ additional sensors or to combine heterogeneous sensors for more accurate tracking performance. Especially, autonomous driving technologies require a sensor fusion technique that considers various driving environments. In this research, a novel method for high-level data fusion is proposed to improve the accuracy of tracking surrounding vehicles. In response to the changing driving environment, the locations of the vehicles are estimated in real-time using an adaptive track-to-track fusion technique and an interacting multiple model filter. Asynchronous measurements from multiple sensors such as radar, camera, and LiDAR, are utilized for the estimation. For each sensor, two motion models representing the vehicle's movement are applied to increase the estimation accuracy. Utilizing a multimodal network-based track-to-track fusion approach, it combines the estimates of the target vehicle position from each sensor into a single estimate. The inputs of the network are intended to determine the reliability of each sensor, considering the driving conditions that may affect sensor accuracy. Also, multiple embeddings in the network are created so that the corresponding data maintains its relevance and enables the real-time computing. The proposed method is verified using real driving data collected from various environments.