Roadside Sensor

Roadside sensors are increasingly used in intelligent transportation systems to enhance perception and safety for autonomous and connected vehicles. Current research focuses on optimizing sensor placement and fusion techniques, often employing deep learning models like residual fusion networks for multimodal data integration (e.g., combining LiDAR and radar) and extended object tracking for self-localization. These advancements aim to improve the detection of vulnerable road users and enable more robust and accurate real-time traffic monitoring, contributing to safer and more efficient transportation systems.