Lane Detection with Centerline Calculation and Temporal Tracking for Autonomous Driving

• Rodrigo Santos de Carvalho UFSC
• Giovani Gracioli UFSC

Resumo

The precise detection of road lanes is a critical perception task for Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. While deep learning has significantly advanced this field, existing methods often focus solely on lane segmentation without providing a continuous, navigable path. This paper presents a complete end-to-end system that not only detects a variable number of lanes using instance segmentation but also introduces two major extensions: a robust module for calculating the geometric centerline of the current lane and a temporal memory system to ensure detection consistency across consecutive frames. These extensions are vital, as they transform raw pixel-level output into the smooth, actionable trajectory required by vehicle control systems. Our approach extends the work of Neven et al. by integrating a polynomial fitting and lane selection strategy to generate a smooth, actionable trajectory for vehicle control. The system is implemented as a modular pipeline with five integrated components: ingestion and preprocessing, neural network segmentation, polynomial fitting with centerline calculation, coordinate transformation, and temporal tracking. Trained and evaluated on the TuSimple benchmark dataset, our model achieves a competitive F1-Score of 93.37% and an accuracy of 93.06%. The result is a comprehensive and functional pipeline that provides an essential input for vehicle navigation, bridging the gap between raw perception and motion planning.

Palavras-chave: Instance segmentation, Tracking, Lane detection, Navigation, Pipelines, Fitting, Polynomials, Trajectory, Planning, Autonomous vehicles, Lane Detection, Instance Segmentation, Deep Learning, Convolutional Neural Networks, Autonomous Driving, Centerline Calculation, Temporal Tracking