Deep learning for radar data exploitation of autonomous vehicle

• URL: http://arxiv.org/abs/2203.08038v1
• Date: Tue, 15 Mar 2022 16:19:51 GMT
• Title: Deep learning for radar data exploitation of autonomous vehicle
• Authors: Arthur Ouaknine
• Abstract summary: This thesis focuses on the on automotive RADAR, which is a low-cost active sensor measuring properties of surrounding objects. The RADAR sensor is seldom used for scene understanding due to its poor angular resolution, the size, noise, and complexity of RADAR raw data. This thesis proposes an extensive study of RADAR scene understanding, from the construction of an annotated dataset to the conception of adapted deep learning architectures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving requires a detailed understanding of complex driving scenes. The redundancy and complementarity of the vehicle's sensors provide an accurate and robust comprehension of the environment, thereby increasing the level of performance and safety. This thesis focuses the on automotive RADAR, which is a low-cost active sensor measuring properties of surrounding objects, including their relative speed, and has the key advantage of not being impacted by adverse weather conditions. With the rapid progress of deep learning and the availability of public driving datasets, the perception ability of vision-based driving systems has considerably improved. The RADAR sensor is seldom used for scene understanding due to its poor angular resolution, the size, noise, and complexity of RADAR raw data as well as the lack of available datasets. This thesis proposes an extensive study of RADAR scene understanding, from the construction of an annotated dataset to the conception of adapted deep learning architectures. First, this thesis details approaches to tackle the current lack of data. A simple simulation as well as generative methods for creating annotated data will be presented. It will also describe the CARRADA dataset, composed of synchronised camera and RADAR data with a semi-automatic annotation method. This thesis then present a proposed set of deep learning architectures with their associated loss functions for RADAR semantic segmentation. It also introduces a method to open up research into the fusion of LiDAR and RADAR sensors for scene understanding. Finally, this thesis exposes a collaborative contribution, the RADIal dataset with synchronised High-Definition (HD) RADAR, LiDAR and camera. A deep learning architecture is also proposed to estimate the RADAR signal processing pipeline while performing multitask learning for object detection and free driving space segmentation.

Related papers

• On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR [4.606106768645647]
  3D LiDAR point cloud data is crucial for scene perception in computer vision, robotics, and autonomous driving. We present DurLAR, the first high-fidelity 128-channel 3D LiDAR dataset featuring panoramic ambient (near infrared) and reflectivity imagery. To improve the segmentation accuracy, we introduce Range-Aware Pointwise Distance Distribution (RAPiD) features and the associated RAPiD-Seg architecture.
  arXiv  Detail & Related papers  (2024-11-01T14:01:54Z)
• Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
  We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning. Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations. This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
  arXiv  Detail & Related papers  (2024-05-08T17:59:53Z)
• Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection [1.0334138809056097]
  We propose a novel framework for real-time LiDAR odometry and mapping based on LOAM architecture for fast moving platforms. Our framework utilizes semantic information produced by a deep learning model to improve point-to-line and point-to-plane matching. We study the effect of improving the matching process on the robustness of LiDAR odometry against high speed motion.
  arXiv  Detail & Related papers  (2024-03-05T16:53:24Z)
• Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection [7.728838099011661]
  This paper presents RiCL, an instance contrastive learning framework to pre-train radar object detectors. We aim to pre-train an object detector's backbone, head and neck to learn with fewer data.
  arXiv  Detail & Related papers  (2024-02-13T12:53:33Z)
• Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection [78.59426158981108]
  We introduce a bi-directional LiDAR-Radar fusion framework, termed Bi-LRFusion, to tackle the challenges and improve 3D detection for dynamic objects. We conduct extensive experiments on nuScenes and ORR datasets, and show that our Bi-LRFusion achieves state-of-the-art performance for detecting dynamic objects.
  arXiv  Detail & Related papers  (2023-06-02T10:57:41Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)
• Probabilistic Oriented Object Detection in Automotive Radar [8.281391209717103]
  We propose a deep-learning based algorithm for radar object detection. We created a new multimodal dataset with 102544 frames of raw radar and synchronized LiDAR data. Our best performing radar detection model achieves 77.28% AP under oriented IoU of 0.3.
  arXiv  Detail & Related papers  (2020-04-11T05:29:32Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.