12 August 2020 Toward real-time road detection for autonomous vehicles
Lachachi M. Yazid, Ouslim Mohamed, Niar Smail, Abdelmalik Taleb-Ahmed
Author Affiliations +
Abstract

Road detection is a vital task for autonomous vehicles, as it has a direct link to passengers’ safety. Given its importance, researchers aimed to improve its accuracy and robustness. We look at the task from a holistic point of view, where we aim to balance computation and accuracy. A multimodal road detection pipeline is proposed, which fuses the camera image with the preprocessed LIDAR input. First, the LIDAR input is preprocessed using three-dimensional models inspired from computer graphics to generate image-like representations. Then, the preprocessed LIDAR input is combined with the camera image using a fusion module named inputs cross-fusion module, to reduce the computation amount required by other fusion strategies. To prevent the accuracy loss caused by the computation gain, we introduce the surface normal information to add distinctiveness. Furthermore, we propose a cost/benefit metric to evaluate the trade-off between computation cost and accuracy of road detection approaches. Several tests were conducted using the KITTI road detection benchmark based on deep convolutional neural networks, the obtained results were considered very satisfactory. In particular, the robustness of the proposed approach resulted in accuracies higher than 95% on different road types, comparable to those of the state-of-the-art techniques. In addition to marginally reducing the inference time of the used DCNN on images with a resolution of 1248  ×  352  pixels to 130 ms using an NVIDIA GTX-1080TI.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00© 2020 SPIE and IS&T
Lachachi M. Yazid, Ouslim Mohamed, Niar Smail, and Abdelmalik Taleb-Ahmed "Toward real-time road detection for autonomous vehicles," Journal of Electronic Imaging 29(4), 043022 (12 August 2020). https://doi.org/10.1117/1.JEI.29.4.043022
Received: 3 April 2020; Accepted: 28 July 2020; Published: 12 August 2020
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KEYWORDS
Roads

LIDAR

3D modeling

Cameras

Unmanned vehicles

Image fusion

3D image processing

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