Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis
Fraunhofer IVI（弗劳恩霍夫应用信息学研究所（IVI））；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Technical University of Munich（慕尼黑工业大学）；Fraunhofer IOSB（弗劳恩霍夫光学、电子和图像利用研究所（IOSB））
Longfei Han, Qiuyu Xu, Klaus Kefferpütz, Ying Lu, Gordon Elger, Jürgen Beyerer
4D mmWave radar sensors are suitable for roadside perception in city-scale Intelligent Transportation Systems (ITS) due to their long sensing range, weatherproof functionality, simple mechanical design, and low manufacturing cost. In this work, we investigate radar-based ITS for scalable traffic analysis. Localization of these radar sensors at city scale is a fundamental task in ITS. For flexible sensor setups, it requires even more effort. To address this task, we propose a self-localization approach that matches two descriptions of the "road": the one from the geometry of the motion trajectories of cumulatively observed vehicles, and the other one from the aerial laser scan. An Iterative Closest Point (ICP) algorithm is used to register the motion trajectory in the road section of the laser scan. The resulting estimate of the transformation matrix represents the sensor pose in a global reference frame. We evaluate the results and show that it outperforms other map-based radar localization methods, especially for the orientation estimation. Beyond the localization result, we project radar sensor data onto a city-scale laser scan and generate a scalable occupancy heat map as a traffic analysis tool. This is demonstrated using two radar sensors monitoring an urban area in the real world.
Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors
Fraunhofer IVI（弗劳恩霍夫应用中心“互联移动与基础设施”所属IVI研究所）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Fraunhofer IOSB（弗劳恩霍夫IOSB研究所）
Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China（自动化与智能感知学院，上海交通大学，上海200240，中国）；Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China（系统控制与信息处理重点实验室，中华人民共和国教育部，上海200240，中国）；Global College, Shanghai Jiao Tong University, Shanghai 200240, China（全球学院，上海交通大学，上海200240，中国）
Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has …
One essential step to realize modern driver assistance technology is the accurate knowledge about the location of static objects in the environment. In this work, we use artificial neural networks to predict the occupation state of a whole scene in an end-to-end manner. This stands in contrast to the traditional approach of accumulating each detection's influence on the occupancy state and allows to learn spatial priors which can be used to interpolate the environment's occupancy state. We show that these priors make our method suitable to predict dense occupancy estimations from sparse, highl…
Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion. To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is self-supervised using partial occupancy labels generated by lidar, allowing a robot to…
High and Low Resolution Tradeoffs in Roadside Multimodal Sensing
The Polytechnic School Arizona State University（亚利桑那州立大学理工学院）；Civil Engineering McGill University（麦吉尔大学土木工程系）；Maricopa County Department of Transportation（马里科帕县交通部门）
Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and with the possibility to directly measure the radial velocity, it brings it's own set of challenges, for which existing algorithms need to be adapted. One core algorithm of a perception system is dynamic occupancy grid mapping, which has traditionally relied on lidar. In this paper we present a dual-weight particle filter as an extension for a Bayesian occupancy grid mapping framework to allow to operate it with radar a…
National Yang Ming Chiao Tung University（阳明交通大学）；Industrial Technology Research Institute（工业技术研究院）
Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the …
LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学 cyber 系统与控制研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Technical University of Munich（慕尼黑工业大学）
Ford Werke GmbH（福特汽车有限公司）；RWTH Aachen University（亚琛工业大学）
With the recent boost in autonomous driving, increased attention has been paid on radars as an input for occupancy mapping. Besides their many benefits, the inference of occupied space based on radar detections is notoriously difficult because of the data sparsity and the environment dependent noise (e.g. multipath reflections). Recently, deep learning-based inverse sensor models, from here on called deep ISMs, have been shown to improve over their geometric counterparts in retrieving occupancy information. Nevertheless, these methods perform a data-driven interpolation which has to be verifie…
Mercedes-Benz RD/ASF（梅赛德斯-奔驰研发/自动驾驶安全基础部门）；University of Pennsylvania（宾夕法尼亚大学）；Fraunhofer FKIE（弗劳恩霍夫通信、信息处理和人机工程学研究所）
Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examined because of the increased complexity of dynamic grids concerning both velocity measurement models for radar sensors and the representation of velocity in a grid framework. In this paper, both issues are addressed: sensor models and an efficient grid framework, which are required to ensure efficient and robust environment perception with radar. To that, we introduce new inverse radar sensor models covering radar sen…
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
State Key Laboratory of Ocean Sensing, Zhejiang University（浙江大学海洋传感国家重点实验室）；Interdisciplinary Student Training Platform for Marine areas, Zhejiang University（浙江大学海洋交叉学科学生培养平台）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；School of AI and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Ocean College, Zhejiang University（浙江大学海洋学院）；Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）
An Autonomous Driving Model Integrated with BEV-V2X Perception, Fusion Prediction of Motion and Occupancy, and Driving Planning, in Complex Traffic Intersections
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
IDLab - Faculty of Applied Engineering, University of Antwerp - imec（IDLab - 应用工程学院，安特卫普大学 - imec）；Cosys-Lab - Faculty of Applied Engineering, University of Antwerp（Cosys-Lab - 应用工程学院，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
本文提出 LiDAR-BIND-T，通过时间嵌入相似度、运动对齐损失和窗口化时间融合增强异构传感器到 LiDAR 的转换，并以新指标验证其提升 SLAM 稳定性与精度。
