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理解周围环境的常见任务，其中可行驶区域分割与车道检测对于实现安全高效的道路导航尤为重要。然而，传统语义分割模型计算成本高昂且依赖高端硬件，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化模型——TwinLiteNet，该模型设计简洁，却能实现精确高效的分割效果。我们在BDD100K数据集上对TwinLiteNet进行评估，并与现有模型进行对比。实验结果表明：所提TwinLiteNet在计算资源需求显著降低的条件下，性能与现有方法相当。具体而言，TwinLiteNet仅需0.4百万参数即可在可行驶区域任务上达到91.3%的mIoU得分，在车道检测任务上达到31.08%的IoU，并在RTX A5000 GPU上实现415 FPS的推理速度。此外，TwinLiteNet可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}。