Advances in Multimodal Sensor Fusion and Deep Learning for Autonomous Truck Reverse Parking

Reversing trucks into parking spaces is challenging, especially in complex and constrained environments due to limited visibility and real-time environmental changes. With the development of automated driving technology, sensory detection technology has been widely used, through multi-sensor fusion technology, such as cameras, LiDAR, radar, etc., to enhance the vehicles ability to perceive the surrounding environment and reduce the safety risk. Meanwhile, the introduction of deep learning algorithms further optimizes the detection and decision-making ability of the system. This study explores the sensing technology of truck reverse parking, focusing on the application of sensor devices, data processing and fusion technology, and artificial intelligence in target detection and behavior prediction. In addition, this study analyzes the challenges faced by trucks in the process of reversing, such as blind field, obstacle detection, and misjudgment in complex environments, and proposes targeted solutions. Ultimately, this study offers insights for the continued development of safer, more efficient, and robust truck reverse parking systems.