RONELDv2: A faster, improved lane tracking method
- URL: http://arxiv.org/abs/2202.13137v1
- Date: Sat, 26 Feb 2022 13:12:09 GMT
- Title: RONELDv2: A faster, improved lane tracking method
- Authors: Zhe Ming Chng, Joseph Mun Hung Lew, Jimmy Addison Lee
- Abstract summary: Lane detection is an integral part of control systems in autonomous vehicles and lane departure warning systems.
This paper proposes an improved, lighter weight lane detection method, RONELDv2.
Experiments using the proposed improvements show a consistent increase in lane detection accuracy results across different datasets and deep learning models.
- Score: 1.3965477771846408
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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