Attention Transfer-Based Deep Distilled Architecture for 6G Driven-Smart Vehicle Transportation System

Autonomous 6G-enabled Vehicle Transportation System (VTS) is receiving significant attention from researchers to ensure robust and safe driving operations. 6G-supported communication technologies show remarkable advancements in several vehicular domains, including automated and accurate identification of lanes, vehicles, traffic signs, and obstacles within the vehicle’s proximity. Integrating IoT and AI technologies can leverage the extensive information gathered by Autonomous Vehicles (AVs) for precise vehicle detection. Despite the rapid advancements in object detection for completely visible objects from oncoming vehicles, detecting objects in low-visibility environments remains a challenging task, especially at night. This paper presents an efficient vehicle road cooperation method using automated real-time roadside object identification system by processing the captured video frames from LiDAR sensors. The developed method leverages the strength of the teacher-student-based distilled deep learning architecture for precisely identifying roadside objects. The teacher model utilizes the improved weighting factor to lower the false positives for better feature refinement. Meanwhile, the student model is equipped with convolutional attention and hierarchical feature fusion to capture balanced positional and semantic discriminative and multi-scale feature maps. Further, a gradient-based attention transfer mechanism has been utilized for significant knowledge transfer using attention maps from the teacher-to-student model to capture spatial feature information for night vision. Extensive experimental results demonstrate that the developed method overshadows the state-of-the-art object detection methods by achieving 41.45%, and 55.44% mAP and mAR, respectively. Additionally, the efficacy of the developed method has been validated by incorporating the different use cases in 6G-enabled VTS.