This paper presents a lightweight, end-to-end highway lane detection architecture that jointly captures spatial and temporal information for robust performance in real-world driving scenarios. Building on the strengths of 3D convolutional neural networks and instance segmentation, we propose two models that integrate a 3D-ResNet encoder with a Point Instance Network (PINet) decoder. The first model enhances multi-scale feature representation using a Feature Pyramid Network (FPN) and Self-Attention mechanism to refine spatial dependencies. The second model introduces a Region of Interest (ROI) detection head to selectively focus on lane-relevant regions, thereby improving precision and reducing computational complexity. Experiments conducted on the TuSimple dataset (highway driving scenarios) demonstrate that the proposed second model achieves 93.40% accuracy while significantly reducing false negatives. Compared to existing 2D and 3D baselines, our approach achieves improved performance with fewer parameters and reduced latency. The architecture has been validated through offline training and real-time inference in the Autonomous Systems Laboratory at City, St George's University of London. These results suggest that the proposed models are well-suited for integration into Advanced Driver Assistance Systems (ADAS), with potential scalability toward full Lane Assist Systems (LAS).

翻译：本文提出一种轻量级端到端高速公路车道检测架构，通过联合捕获时空信息实现实际驾驶场景中的鲁棒性能。在三维卷积神经网络与实例分割技术的基础上，我们构建了两种集成3D-ResNet编码器与点实例网络解码器的模型。第一个模型采用特征金字塔网络与自注意力机制增强多尺度特征表示，以优化空间依赖性。第二个模型引入感兴趣区域检测头，选择性聚焦车道相关区域，从而提升精度并降低计算复杂度。在TuSimple数据集（高速公路驾驶场景）上的实验表明，本文提出的第二个模型在显著降低假阴性率的同时达到93.40%的准确率。与现有二维及三维基线方法相比，本方法以更少的参数和更低的延迟实现了更优性能。该架构已在伦敦圣乔治大学城市校区自动驾驶系统实验室通过离线训练与实时推理验证。结果表明，所提模型适用于高级驾驶辅助系统的集成，并具有向全功能车道保持系统扩展的潜力。