AppNets: An Efficient Multi-Task Fusion Network for Comprehensive Driving Perception

Abstract Panoramic driving perception systems are critical for autonomous driving, as they provide essential traffic-related information. This study introduces AppNets, an efficient and effective multi-task learning framework designed for real-time panoptic driving perception. AppNets comprises an encoder for feature extraction and three decoders that concurrently perform traffic object detection, drivable area segmentation, and lane segmentation. We propose the C2fA module to enhance the model's extraction capability. To enhance our dataset, we expanded the SDExpressway dataset by adding 2,000 frames, particularly incorporating nighttime and adverse weather scenarios. Extensive experiments conducted on both the challenging BDD100K dataset and the augmented SDExpressway dataset demonstrate that AppNets achieves state-of-the-art performance, outperforming baseline models by significant margins. Specifically, on the SDExpressway dataset, AppNets attains a mean average precision (mAP) of 85.1% for traffic object detection, a mean intersection over union (mIoU) of 98.7% for drivable area segmentation, and an intersection over union (IoU) of 75.1% for lane segmentation. These results underscore the effectiveness of AppNets in complex driving scenarios, highlighting its potential for practical deployment in autonomous driving systems. the source codes are released at https://github.com/Huniki/Appnet.git