Research on Adaptive 3D Object Detection Algorithm of Road Scene for Intelligent Driving

Object detection algorithms, as an essential component of intelligent driving systems, play a crucial role in ensuring the safe operation of vehicles. However, achieving precise detection and safe decision-making still poses significant challenges due to the dynamic nature of intelligent driving scenarios and the diversity of objects to be detected. Existing three-dimensional object detection methods typically focus on data correlation, with most improvement efforts directed towards innovating the overall network model while overlooking the correlation between real three-dimensional scenes and the objects to be detected. In this context, we propose a road scene-driven three-dimensional object detection algorithm that leverages road scene information as prior knowledge. When conducting three-dimensional object detection, current road scene information can first be obtained through existing intelligent traffic systems. Subsequently, adaptive fusion of different resolution global region depth fusion features is performed based on different road categories and congestion levels to enhance the representation ability of these fusion features for effective target-related information in complex scenes. Finally, three-dimensional object detection is carried out using these fusion features.