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Contextual road lane and symbol generation for autonomous driving

arXiv 2201.07120 ↗First posted 18 Jan 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

In this paper we present a novel approach for lane detection and segmentation using generative models. Traditionally discriminative models have been employed to classify pixels semantically on a road. We model the probability distribution of lanes and road symbols by training a generative adversarial network. Based on the learned probability distribution, context-aware lanes and road signs are generated for a given image which are further quantized for nearest class label. Proposed method has been tested on BDD100K and Baidu ApolloScape datasets and performs better than state of the art and exhibits robustness to adverse conditions by generating lanes in faded out and occluded scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 18 Jan 2022.

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15 of 31 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2014Conditional Generative Adversarial Netscited by 2,152
  4. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  5. 2016Tutorial on Variational Autoencoderscited by 404
  6. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  7. 2018The ApolloScape Open Dataset for Autonomous Driving and its Applicationcited by 188
  8. 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
  9. 2019Learning Lightweight Lane Detection CNNs by Self Attention Distillationcited by 162
  10. 2018Towards End-to-End Lane Detection: an Instance Segmentation Approachcited by 129
  11. 2020Inter-Region Affinity Distillation for Road Marking Segmentationcited by 58
  12. 2017VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognitioncited by 56
3 more
  1. 2014Real time Detection of Lane Markers in Urban Streetscited by 41
  2. 2018LaneNet: Real-Time Lane Detection Networks for Autonomous Drivingcited by 39
  3. 2018EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detectioncited by 35

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