  Urban segmentation and lane detection are two important tasks for traffic
scene perception. Accuracy and fast inference speed of visual perception are
crucial for autonomous driving safety. Fine and complex geometric objects are
the most challenging but important recognition targets in traffic scene, such
as pedestrians, traffic signs and lanes. In this paper, a simple and efficient
topology-aware energy loss function-based network training strategy named
EIEGSeg is proposed. EIEGSeg is designed for multi-class segmentation on
real-time traffic scene perception. To be specific, the convolutional neural
network (CNN) extracts image features and produces multiple outputs, and the
elastic interaction energy loss function (EIEL) drives the predictions moving
toward the ground truth until they are completely overlapped. Our strategy
performs well especially on fine-scale structure, \textit{i.e.} small or
irregularly shaped objects can be identified more accurately, and discontinuity
issues on slender objects can be improved. We quantitatively and qualitatively
analyze our method on three traffic datasets, including urban scene
segmentation data Cityscapes and lane detection data TuSimple and CULane. Our
results demonstrate that EIEGSeg consistently improves the performance,
especially on real-time, lightweight networks that are better suited for
autonomous driving.
