Fast LIDAR-based road detection using fully convolutional neural networks

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Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection (2018, background): “Deep learning has been introduced to object detection in the autonomous driving settings that use cameras [1]–[4], Lidar [5]–[9], or both [10]–[15], and has set the benchmark on many popular datasets (e.”
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks (2018, methodology): “This type of architecture has been successfully used in previous publications, such as [9] and [20], and it is illustrated in Fig.”
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud (2017, background): “Work with two-dimensional data considers raw images with projections of LiDAR point clouds top-down [14] or from a number of other views [15].”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “For example, [54], [60], [61] focus on pixel-wise semantic segmentation for multiple classes including road, car, bicycle, column-pole, tree, sky, etc; [52] and [62] concentrate on road segmentation; and [51], [63], [64] deal”