Self-driving cars hold significant potential to reduce traffic accidents, alleviate congestion, and enhance urban mobility. However, developing reliable AI systems for autonomous vehicles remains a substantial challenge. Over the past decade, multi-task learning has emerged as a powerful approach to address complex problems in driving perception. Multi-task networks offer several advantages, including increased computational efficiency, real-time processing capabilities, optimized resource utilization, and improved generalization. In this study, we present AurigaNet, an advanced multi-task network architecture designed to push the boundaries of autonomous driving perception. AurigaNet integrates three critical tasks: object detection, lane detection, and drivable area instance segmentation. The system is trained and evaluated using the BDD100K dataset, renowned for its diversity in driving conditions. Key innovations of AurigaNet include its end-to-end instance segmentation capability, which significantly enhances both accuracy and efficiency in path estimation for autonomous vehicles. Experimental results demonstrate that AurigaNet achieves an 85.2% IoU in drivable area segmentation, outperforming its closest competitor by 0.7%. In lane detection, AurigaNet achieves a remarkable 60.8% IoU, surpassing other models by more than 30%. Furthermore, the network achieves an mAP@0.5:0.95 of 47.6% in traffic object detection, exceeding the next leading model by 2.9%. Additionally, we validate the practical feasibility of AurigaNet by deploying it on embedded devices such as the Jetson Orin NX, where it demonstrates competitive real-time performance. These results underscore AurigaNet's potential as a robust and efficient solution for autonomous driving perception systems. The code can be found here https://github.com/KiaRational/AurigaNet.

翻译：自动驾驶汽车在减少交通事故、缓解交通拥堵以及提升城市出行效率方面具有巨大潜力。然而，为自动驾驶车辆开发可靠的人工智能系统仍然是一项重大挑战。在过去十年中，多任务学习已成为解决驾驶感知中复杂问题的一种强大方法。多任务网络具有多项优势，包括更高的计算效率、实时处理能力、优化的资源利用以及更好的泛化性能。在本研究中，我们提出了AurigaNet，这是一种先进的多任务网络架构，旨在突破自动驾驶感知的边界。AurigaNet整合了三个关键任务：目标检测、车道线检测以及可行驶区域实例分割。该系统使用以驾驶条件多样性著称的BDD100K数据集进行训练和评估。AurigaNet的核心创新包括其端到端的实例分割能力，这显著提升了自动驾驶车辆路径估计的准确性和效率。实验结果表明，AurigaNet在可行驶区域分割任务中达到了85.2%的交并比（IoU），比其最接近的竞争对手高出0.7%。在车道线检测任务中，AurigaNet实现了60.8%的显著IoU，超过其他模型30%以上。此外，该网络在交通目标检测任务中达到了47.6%的平均精度均值（mAP@0.5:0.95），比次优模型高出2.9%。另外，我们通过在Jetson Orin NX等嵌入式设备上部署AurigaNet，验证了其实际可行性，其展示了具有竞争力的实时性能。这些结果凸显了AurigaNet作为自动驾驶感知系统的一个鲁棒且高效解决方案的潜力。相关代码可见于 https://github.com/KiaRational/AurigaNet。