Research Area:  Machine Learning

Abstract:

Inspired by the UNet architecture of semantic image segmentation, we propose a lightweight UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane detection and path prediction (PP) in autonomous driving. We also design and integrate a PP algorithm with convolutional neural network (CNN) to form a simulation model (CNN-PP) that can be used to assess CNN’s performance qualitatively, quantitatively, and dynamically in a host agent car driving along with other agents all in a real-time autonomous manner. DSUNet is 5.12 imes lighter in model size and 1.61 imes faster in inference than UNet. DSUNet-PP outperforms UNet-PP in mean average errors of predicted curvature and lateral offset for path planning in dynamic simulation. DSUNet-PP outperforms a modified UNet in lateral error, which is tested in a real car on real road. These results show that DSUNet is efficient and effective for lane detection and path prediction in autonomous driving.

Keywords:  

Author(s) Name:  Der-Hau Lee, Jinn-Liang Liu

Publisher name:  Springer

DOI:  10.1007/s11760-022-02222-2

Volume Information:  Volume 17, pages 199–205, (2023)

Paper Link:   https://link.springer.com/article/10.1007/s11760-022-02222-2