Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road. However, original semantic segmentation models are computationally expensive and require high-end hardware, which is not feasible for embedded systems in autonomous vehicles. This paper proposes a lightweight model for the driveable area and lane line segmentation. TwinLiteNet is designed cheaply but achieves accurate and efficient segmentation results. We evaluate TwinLiteNet on the BDD100K dataset and compare it with modern models. Experimental results show that our TwinLiteNet performs similarly to existing approaches, requiring significantly fewer computational resources. Specifically, TwinLiteNet achieves a mIoU score of 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection task with only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000. Furthermore, TwinLiteNet can run in real-time on embedded devices with limited computing power, especially since it achieves 60FPS on Jetson Xavier NX, making it an ideal solution for self-driving vehicles. Code is available: url{https://github.com/chequanghuy/TwinLiteNet}.

翻译：语义分割是自动驾驶中用于理解周围环境的常见任务，其中可行驶区域分割和车道检测对道路上的安全高效导航尤为重要。然而，原始语义分割模型计算成本高，需要高端硬件，难以应用于自动驾驶车辆的嵌入式系统。本文提出一种面向可行驶区域与车道线分割的轻量模型。TwinLiteNet 设计简洁，却能实现准确高效的分割结果。我们在 BDD100K 数据集上对 TwinLiteNet 进行了评估，并与现代模型进行了比较。实验结果表明，与现有方法相比，TwinLiteNet 在显著减少计算资源消耗的前提下取得了相近的性能。具体而言，TwinLiteNet 在可行驶区域任务上达到 91.3% 的 mIoU，在车道检测任务上达到 31.08% 的 IoU，参数量仅为 40 万，在 GPU RTX A5000 上可实现 415 FPS。此外，TwinLiteNet 能在计算能力有限的嵌入式设备上实时运行，尤其在 Jetson Xavier NX 上达到 60 FPS，使其成为自动驾驶车辆的理想解决方案。代码已开源：url{https://github.com/chequanghuy/TwinLiteNet}。