In the current research and development of autonomous driving technology, most existing autonomous driving systems rely on perception methods based on self-vehicle sensors. This approach neglects a highly promising avenue for perception enhancement: leveraging intelligent roadside cameras to overcome visual limitations and achieve more comprehensive and in-depth perceptual capabilities. Current vision-centric detection methods exhibit low adaptability when applied to roadside camera detection in diverse intersection scenarios. Roadside cameras are primarily used for roadside 3D object detection. Early methods employed single sensors but suffered from limited accuracy and poor performance in complex scenarios. Subsequent advancements introduced multi-sensor fusion, though synergistic advantages were not fully exploited. Recent deep learning-based multi-sensor fusion methods have significantly improved detection accuracy and stability. Current research focuses on optimizing algorithm performance, expanding application scenarios, and leveraging communication technologies for collaborative processing. This paper proposes Bird’s-Eye-View Boost (BEVboost), a roadside 3D object detection method that addresses the low adaptability of existing methods across diverse intersection scenarios. Through experiments and comparative evaluations on a roadside 3D object detection benchmark platform, BEVboost outperforms vision-centric counterparts in performance. This achievement provides novel insights and technical support for advancing perception in autonomous driving.