RONELDv2: A faster, improved lane tracking method

Abstract: Lane detection is an integral part of control systems in autonomous vehicles and lane departure warning systems as lanes are a key component of the operating environment for road vehicles. In a previous paper, a robust neural network output enhancement for active lane detection (RONELD) method augmenting deep learning lane detection models to improve active, or ego, lane accuracy performance was presented. This paper extends the work by further investigating the lane tracking methods used to increase robustness of the method to lane changes and different lane dimensions (e.g. lane marking thickness) and proposes an improved, lighter weight lane detection method, RONELDv2. It improves on the previous RONELD method by detecting the lane point variance, merging lanes to find a more accurate set of lane parameters, and using an exponential moving average method to calculate more robust lane weights. Experiments using the proposed improvements show a consistent increase in lane detection accuracy results across different datasets and deep learning models, as well as a decrease in computational complexity observed via an up to two-fold decrease in runtime, which enhances its suitability for real-time use on autonomous vehicles and lane departure warning systems.