Driving Perception

Driving perception research focuses on enabling autonomous vehicles to accurately understand their surroundings using sensor data, primarily cameras and LiDAR, aiming for robust and reliable object detection, scene understanding, and motion prediction. Current efforts concentrate on improving model accuracy and efficiency through advanced architectures like transformers and convolutional neural networks, addressing challenges such as adverse weather conditions and computational constraints via techniques like multi-modal fusion and efficient feature extraction. These advancements are crucial for enhancing the safety and reliability of autonomous driving systems, impacting both the development of more sophisticated perception models and the validation of their performance in real-world scenarios.