Panoptic Driving Perception

Panoptic driving perception aims to create a comprehensive understanding of a driving scene by simultaneously performing multiple perception tasks, such as object detection, drivable area segmentation, and lane detection. Current research emphasizes developing efficient and accurate models, often employing multi-task learning architectures like variations of YOLO and other lightweight networks, and incorporating data fusion techniques (e.g., combining camera and radar data) to improve robustness and reduce computational demands, particularly for resource-constrained edge devices. This research area is crucial for advancing autonomous driving systems by enabling safer and more reliable navigation, with ongoing efforts focused on improving both accuracy and real-time performance.