Roadside Perception

Roadside perception focuses on developing systems that use cameras, lidar, and radar to monitor traffic from fixed infrastructure locations, enhancing the safety and efficiency of autonomous vehicles and intelligent transportation systems. Current research emphasizes robust 3D object detection and tracking using multi-sensor fusion and bird's-eye-view representations, often employing deep learning models tailored to handle diverse camera viewpoints and challenging weather conditions. The development of large-scale, publicly available datasets and standardized evaluation methodologies is crucial for advancing the field and enabling the deployment of reliable roadside perception systems in real-world scenarios.