A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection

Abstract

Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial-temporal (ST) sequence-to-one deep learning architecture. This architecture makes full use of the ST information in multiple continuous image frames to detect the lane markings in the very last frame. Specifically, the hybrid model integrates the following aspects: (a) the single image feature extraction module equipped with the spatial convolutional neural network; (b) the ST feature integration module constructed by ST recurrent neural network; (c) the encoder-decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model architecture can effectively handle challenging driving scenes and outperforms available state-of-the-art methods.

arXiv comment18 pages, 5 figures. Published by Computer-Aided Civil and Infrastructure Engineering (CACIE). Open access from https://doi.org/10.1111/mice.12829

References in corpus (14)

• Sequence to Sequence Learning with Neural Networks
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• On the difficulty of training Recurrent Neural Networks
• Real time Detection of Lane Markers in Urban Streets
• On the Variance of the Adaptive Learning Rate and Beyond
• Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
• Spatial As Deep: Spatial CNN for Traffic Scene Understanding
• Delving Deeper into Convolutional Networks for Learning Video Representations
• Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
• Lane Detection Model Based on Spatio-Temporal Network With Double Convolutional Gated Recurrent Units
• Ego-Lane Analysis System (ELAS): Dataset and Algorithms
• Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
• Fast Scene Understanding for Autonomous Driving
• End-to-end Lane Shape Prediction with Transformers

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