As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR data for off-road scenarios. We present a novel pipeline that utilizes LIDAR data and an existing annotated off-road LIDAR dataset for generating RADAR labels, in which the RADAR data are represented as images. Validated with real-world datasets, our pragmatic approach underscores the potential of RADAR technology for navigation applications in off-road environments.
General Motors, Advanced Technical Center（通用汽车先进技术中心）
Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches.In this work, we propose learning the inverse sensor model used for occup…
University of Ottawa（渥太华大学）；Sensorcortek Inc（Sensorcortek 公司）
Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multi-class ob…
DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）
Massachussetts Institute of Technology（麻省理工学院）；University of Oxford（牛津大学）
Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 …
This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave radar - a commercially promising sensor poised for exploitation in mobile autonomy. We show how a rotationally-invariant metric embedding for radar scans can be integrated into sequence-based trajectory matching systems typically applied to videos taken by visual sensors. Due to the complete horizontal field of view inherent to the radar scan formation process, we show how this off-the-shelf sequence-based trajectory matching system can be manip…
DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
Infineon Technologies AG, Austria（英飞凌科技公司，奥地利）；Graz University of Technology, Austria（格拉茨技术大学，奥地利）；Virtual Vehicle Research GmbH, Austria（虚拟车辆研究有限公司，奥地利）
University of Science and Technology of China（中国科学技术大学）
In this letter, we propose MAROAM, a millimeter wave radar-based SLAM framework, which employs a two-step feature selection process to build the global consistent map. Specifically, we first extract feature points from raw data based on their local geometric properties to filter out those points that violate the principle of millimeter-wave radar imaging. Then, we further employ another round of probabilistic feature selection by examining how often and how recent the feature point has been detected in the proceeding frames. With such a two-step feature selection, we establish a global consist…
SNAIL Radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM
Wuhan University（武汉大学）；The state key Lab of Information Engineering in Surveying, Mapping And Remote Sensing (LIESMARS), Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Wuhan Quantum Institute（武汉量子研究院）
RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather
Infineon Technologies AG（英飞凌科技有限公司）；Institute of Visual Computing（视觉计算研究所）；Graz University of Technology（格拉茨工业大学）；Virtual Vehicle Research GmbH（虚拟车辆研究有限公司）
mmPlace: Robust Place Recognition with Intermediate Frequency Signal of Low-cost Single-chip Millimeter Wave Radar
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Technology Directions Office (TDO) of Google（谷歌技术方向办公室）
This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar target- and object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are gene…
Carnegie Mellon University（卡内基梅隆大学）；Bosch Research and Technology Center（博世研究与技术中心）
mmWave radars offer excellent depth resolution even at very long ranges owing to their high bandwidth. But their angular resolution is at least an order-of-magnitude worse than camera and lidar systems. Hence, mmWave radar is not a capable 3-D imaging solution in isolation. We propose Metamoran, a system that combines the complimentary strengths of radar and camera to obtain accurate, high resolution depth images over long ranges even in high clutter environments, all from a single fixed vantage point. Metamoran enables rich long-range depth imaging with applications in security and surveillan…
Institute of Sensors, Signals and Systems, Heriot-Watt University（赫瑞-瓦特大学传感器、信号与系统研究所）
Datasets for autonomous cars are essential for the development and benchmarking of perception systems. However, most existing datasets are captured with camera and LiDAR sensors in good weather conditions. In this paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to facilitate research on object detection, tracking and scene understanding using radar sensing for safe autonomous driving. RADIATE includes 3 hours of annotated radar images with more than 200K labelled road actors in total, on average about 4.6 instances per radar image. It covers 8 different categories of a…
STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation
Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能交叉学科项目，首尔国立大学）；Department of Electrical and Computer Engineering, Seoul National University（电气与计算机工程系，首尔国立大学）
Automatic Road Subsurface Distress Recognition from Ground Penetrating Radar Images using Deep Learning-based Cross-verification
Department of Engineering Mechanics, School of Civil Engineering and Transportation, South China University of Technology（华南理工大学土木工程与交通学院力学系）；Guangdong Provincial Academy of Building Research Group Co. Ltd.（广东省建筑科学研究集团有限公司）；School of Civil and Transportation Engineering, Guangdong University of Technology（广东工业大学土木与交通学院）
Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA（迈克·沃克66机械工程系，德克萨斯A&M大学，学院站，德克萨斯州，美国）；DEVCOM Army Research Laboratory, Adelphi, MD, USA（陆军研究实验室，阿德菲，马里兰州，美国）；University of Texas at Austin Center for Autonomy, Austin, TX, USA（德克萨斯大学奥斯汀分校自主性中心，奥斯汀，德克萨斯州，美国）
Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic Aperture Radar satellites can provide high resolution topographical maps. However roads are difficult to identify in these data as they look visually similar to targets such as rivers and railways. Most road extraction methods on Synthetic Aperture Radar images still rely on a prior segmentation performed by classical computer vision algorithms. Few works study the potential of deep learning techniques,…
Radar-Based NLoS Pedestrian Localization for Darting-Out Scenarios Near Parked Vehicles with Camera-Assisted Point Cloud Interpretation
Seoul National University（首尔国立大学）；Samsung Electro-Mechanics Co., Ltd.（三星机电有限公司）；Chungnam National University（全南国立大学）；Ajou University（亚运会大学）；Institute of Engineering Research at Seoul National University（首尔国立大学工程研究所）
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception
School of Vehicle and Mobility, Tsinghua University（车辆与移动性学院，清华大学）；Nanyang Technological University（南洋理工大学）；CUMTB（中国交通车辆技术研究所）；University of California, Los Angeles（加州大学洛杉矶分校）；Beijing Jiaotong University（北京交通大学）；ByteDance（字节跳动）
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Kassel（卡塞尔大学）
Radar-based road user detection is an important topic in the context of autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to refine during subsequent signal processing. On the other hand, a new sensor generation is waiting in the wings for its application in this challenging field. In this article, two sensors of different radar generations are evaluated against each other. The evaluation criterion is the performance on moving road user object detection and classification tasks. To this end, two data …
Chemnitz University of Technology（开姆尼茨工业大学）；Drexel University（德雷塞尔大学）
Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learnin…
University Politehnica of Bucharest（布加勒斯特理工大学）；University of Bucharest（布加勒斯特大学）
Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors increases the chance of interference among sensors from different vehicles, generating corrupted range profiles and range-Doppler maps. In order to extract distance and velocity of multiple targets from range-Doppler maps, the interference affecting each range profile needs to be mitigated. In this paper, we propose a fully convolutional neural network for automotive radar interference mitigation. In order to train our…
Daimler AG R&D（戴姆勒股份公司研发部）；University of Ulm（乌尔姆大学）
This paper proposes an approach that predicts the road course from camera sensors leveraging deep learning techniques. Road pixels are identified by training a multi-scale convolutional neural network on a large number of full-scene-labeled night-time road images including adverse weather conditions. A framework is presented that applies the proposed approach to longer distance road course estimation, which is the basis for an augmented reality navigation application. In this framework long range sensor data (radar) and data from a map database are fused with short range sensor data (camera) t…
Télécom Paris（巴黎电信学院）；Valeo North America Inc.（法雷奥北美公司）
With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades. Recent progress toward High Definition (HD) Imaging radar has driven the angular resolution below the degree, thus approaching laser scanning performance. However, the amount of data a HD radar delivers and the computational cost to estimate the angular positions remain a challenge. In this paper, we propose a novel HD radar sensing model, FFT-RadNet, that eliminates the overhead of computing the range-azimuth-Doppler 3D tensor, …
Graz University of Technology（格拉茨技术大学）；Infineon Technologies Austria AG（英飞凌科技奥地利公司）
Autonomous driving highly depends on capable sensors to perceive the environment and to deliver reliable information to the vehicles' control systems. To increase its robustness, a diversified set of sensors is used, including radar sensors. Radar is a vital contribution of sensory information, providing high resolution range as well as velocity measurements. The increased use of radar sensors in road traffic introduces new challenges. As the so far unregulated frequency band becomes increasingly crowded, radar sensors suffer from mutual interference between multiple radar sensors. This interf…
In this paper, we address the problem of road segmentation and free space detection in the context of autonomous driving. Traditional methods either use 3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or stereo cameras or 2-dimensional (2D) cues such as lane markings, road boundaries and object detection. Typical 3D point clouds do not have enough resolution to detect fine differences in heights such as between road and pavement. Image based 2D cues fail when encountering uneven road textures such as due to shadows, potholes, lane markings or road restoration. We propos…
calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
The main goal of this paper is to introduce the data collection effort at Mcity targeting automated vehicle development. We captured a comprehensive set of data from a set of perception sensors (Lidars, Radars, Cameras) as well as vehicle steering/brake/throttle inputs and an RTK unit. Two in-cabin cameras record the human driver's behaviors for possible future use. The naturalistic driving on selected open roads is recorded at different time of day and weather conditions. We also perform designed choreography data collection inside the Mcity test facility focusing on vehicle to vehicle, and v…
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
FZI Research Center for Information Technology（弗劳恩霍夫信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Charles III of Madrid（马德里第三大学）；Delft University of Technology（代尔夫特理工大学）
ETH Zürich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）
Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0.5m/px. Segmenting SAR data still requires skilled personnel, limiting the potential for large-scale use. We show that it is possible to automatically and reliably perform urban scene segmentation from next-gen resolution SAR data (0.15m/px) using deep neural networks (DNNs), achieving a pixel accuracy of 95.19% and a mean IoU of 74.67% with data collected over a region of merely 2.2km${}^2$. The presented DNN is not only effective, but…
