Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions

• URL: http://arxiv.org/abs/2408.06772v1
• Date: Tue, 13 Aug 2024 09:55:38 GMT
• Title: Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions
• Authors: Miao Zhang, Sherif Abdulatif, Benedikt Loesch, Marco Altmann, Marius Schwarz, Bin Yang,
• Abstract summary: This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities. transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts.
• Score: 15.767261586617746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability.

Related papers

• DiffuBox: Refining 3D Object Detection with Point Diffusion [74.01759893280774]
  We introduce a novel diffusion-based box refinement approach to ensure robust 3D object detection and localization. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets.
  arXiv  Detail & Related papers  (2024-05-25T03:14:55Z)
• RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar [15.776076554141687]
  3D occupancy-based perception pipeline has significantly advanced autonomous driving. Current methods rely on LiDAR or camera inputs for 3D occupancy prediction. We introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction.
  arXiv  Detail & Related papers  (2024-05-22T21:48:17Z)
• DIDLM:A Comprehensive Multi-Sensor Dataset with Infrared Cameras, Depth Cameras, LiDAR, and 4D Millimeter-Wave Radar in Challenging Scenarios for 3D Mapping [7.050468075029598]
  This study presents a comprehensive multi-sensor dataset designed for 3D mapping in challenging indoor and outdoor environments. The dataset comprises data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar. Various SLAM algorithms are employed to process the dataset, revealing performance differences among algorithms in different scenarios.
  arXiv  Detail & Related papers  (2024-04-15T09:49:33Z)
• An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains [6.4288046828223315]
  3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks, especially autonomous driving. Here we focus on the details of 3D-ODs, focusing on fundamental factors that influence robustness prior to domain adaptation. Our main findings are: (1) transformer backbones with local point features are more robust than 3D CNNs, (2) test-time anchor size adjustment is crucial for adaptation across geographical locations, significantly boosting scores without retraining, and (4) surprisingly, robustness to bad weather is improved when training directly on more clean weather data than on training with bad weather data.
  arXiv  Detail & Related papers  (2024-02-27T15:02:17Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques [0.0]
  The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Deep learning techniques transform the huge amount of data from the sensors into semantic information. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object.
  arXiv  Detail & Related papers  (2022-02-05T09:34:58Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
  We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
  arXiv  Detail & Related papers  (2021-04-22T12:47:37Z)
• SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving [6.810856082577402]
  We have proposed a deep neural network Self Supervised Thermal Network (SSTN) for learning the feature embedding to maximize the information between visible and infrared spectrum domain by contrastive learning. The proposed method is extensively evaluated on the two publicly available datasets: the FLIR-ADAS dataset and the KAIST Multi-Spectral dataset.
  arXiv  Detail & Related papers  (2021-03-04T16:42:49Z)
• LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
  We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors. LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs. It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
  arXiv  Detail & Related papers  (2020-03-13T06:17:39Z)

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.