LANE SEGMENTATION TO OPTIMIZE TRAFFIC FLOW
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.4.3.8Keywords:
lane segmentation; computer vision; deep learning; intelligent transportation systems; traffic flow optimization; traffic management; LaneATT; ERFNet; ENet; PINet; CondLaneNetAbstract
The increasing intensity of urban traffic leads to growing congestion, higher air pollution levels, and reduced safety for road users, making effective traffic flow management a critically important task for modern metropolises. Computer vision technologies, particularly lane segmentation, open new possibilities for intelligent traffic management and optimization of existing transport infrastructure. This study presents a comprehensive analysis of modern neural network architectures for lane segmentation to determine their potential in traffic flow optimization and assess their applicability in traffic management systems. A comparative analysis of five advanced deep learning architectures was conducted: LaneATT with attention mechanism for keypoint detection; ERFNet and ENet as efficient real-time architectures; PINet for instance-based lane segmentation; and CondLaneNet with conditional lane shape generation. The characteristics of each architecture, their computational efficiency and accuracy were investigated, and the impact of segmentation quality on traffic flow optimization metrics was analyzed. It was shown that CondLaneNet provides the highest segmentation accuracy and best lane geometry recovery at low traffic levels and simple road topology, while LaneATT demonstrates slightly lower peak accuracy but exhibits smoother quality degradation with increasing traffic density. ENet and ERFNet provide an acceptable trade-off between accuracy and computational complexity, making them suitable for real-time systems with limited resources. PINet, due to keypoint-based representation and clustering, shows the best resistance to occlusions and complex road scenarios, maintaining the highest proportion of baseline quality under medium and high traffic conditions. The obtained results enable informed selection of optimal architecture for specific application scenarios in intelligent transportation systems.
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