Deep Learning-Based Trajectory Tracking Method for Intelligently Network-Connected Driverless Vehicles in Narrow Areas

Abstract

Driverless vehicles are the development direction of intelligent transportation. In recent years, the rapid development of driverless transportation technology, especially the practical performance of intelligently network-connected driverless vehicles has improved rapidly. However, due to problems with traffic planning, many roads are still relatively narrow. When an intelligently networked driverless car moves in a narrow area, the lack of precision in trajectory tracking can easily cause traffic accidents due to small trajectory changes. In this paper, for the driving characteristics of intelligently networked driverless vehicles in narrow areas, an improved Faster R-CNN target detection network is proposed that introduces a deep residual network ResNet-50, a dual attention mechanism CBAM, and an ROI-Pooling to estimate the position information of driverless vehicles in the video of the traffic scene. Based on the target detection results of driverless vehicles and the appearance characteristics of vehicles, the novel DeepSORT vehicle tracking algorithm improved by OSNet full-scale network and complete intersection over union (CIoU), is employed to derive a vehicle trajectory within a single camera on a real road. The UA-DETRAC dataset in real scenarios is selected to run experiments, and the results demonstrate that the proposed target detection and tracking algorithms perform well, and effectively realize target detection and trajectory tracking of intelligently internet-connected driverless vehicles in narrow areas, which can help realize the further performance enhancement. The improved DeepSORT achieves an impressive MOTA of 96.1% and MOTP of 0.115.