Vehicle Collision Warning Method Based on Trajectory Prediction

Wang, Rongxia; Yang, Weihuang

doi:10.1007/978-3-030-74814-2_11

Cited by 1 publication

(1 citation statement)

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“…The road scene semantic segmentation results on raw surround view images can be used as an information source for other tasks, e.g., visual semantic odometry [6], Stixel World [40], [41] and collision warning [42]. For example, in order to get semantic segmentation results on a bird's eye view image, we can first compute semantic segmentation results on raw surround view images and then map the results to the bird's eye view plane using Inverse Perspective Mapping (IPM), as illustrated in Fig.…”

Section: Semantic Segmentation Using Surround View Camerasmentioning

confidence: 99%

Restricted Deformable Convolution-Based Road Scene Semantic Segmentation Using Surround View Cameras

Deng

Yang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

119

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Understanding the surrounding environment of the vehicle is still one of the challenges for autonomous driving. This paper addresses 360-degree road scene semantic segmentation using surround view cameras, which are widely equipped in existing production cars. First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images. Experiments demonstrate the effectiveness of the RDC to handle images with large distortions, and that the proposed approach shows a good performance using surround view cameras with the help of the transformed images.

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