Lightweight Spatiotemporal Highway Lane Detection via 3D-ResNet and PINet with ROI-Aware Attention

TL;DR

This paper introduces a lightweight, end-to-end highway lane detection model combining 3D-ResNet and PINet with ROI-aware attention, achieving high accuracy and efficiency for real-world driving scenarios.

Contribution

The paper proposes a novel integrated 3D-ResNet and PINet architecture with ROI-aware attention for improved lane detection performance and reduced computational complexity.

Findings

Achieves 93.40% accuracy on TuSimple dataset

Reduces false negatives significantly

Outperforms existing 2D and 3D baselines in accuracy and efficiency

Abstract

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).