Scene flow provides crucial motion information for autonomous driving. Recent LiDAR scene flow models utilize the rigid-motion assumption at the instance level, assuming objects are rigid bodies. However, these instance-level methods are not suitable for sparse radar point clouds. In this work, we present a novel Traffic-Aware Radar Scene-Flow (TARS) estimation method, which utilizes motion rigidity at the traffic level. To address the challenges in radar scene flow, we perform object detection and scene flow jointly and boost the latter. We incorporate the feature map from the object detector, trained with detection losses, to make radar scene flow aware of the environment and road users. From this, we construct a Traffic Vector Field (TVF) in the feature space to achieve holistic traffic-level scene understanding in our scene flow branch. When estimating the scene flow, we consider both point-level motion cues from point neighbors and traffic-level consistency of rigid motion within the space. TARS outperforms the state of the art on a proprietary dataset and the View-of-Delft dataset, improving the benchmarks by 23% and 15%, respectively.
RadarXFormer: Robust Object Detection via Cross-Dimension Fusion of 4D Radar Spectra and Images for Autonomous Driving
Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（中国教育部系统控制与信息处理重点实验室）；SAIC GM Wuling Automobile Company Co., Ltd.（上汽通用五菱汽车有限公司）；Guangxi Laboratory of New Energy Automobile（广西新能源汽车实验室）
BMW of North America（北美宝马公司）；Graz University of Technology（格拉茨工业大学）
Collecting realistic driving trajectories is crucial for training machine learning models that imitate human driving behavior. Most of today's autonomous driving datasets contain only a few trajectories per location and are recorded with test vehicles that are cautiously driven by trained drivers. In particular in interactive scenarios such as highway merges, the test driver's behavior significantly influences other vehicles. This influence prevents recording the whole traffic space of human driving behavior. In this work, we present a novel methodology to extract trajectories of traffic objec…
ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving
School of Data Science, Fudan University（复旦大学数据科学学院）；ZF (China) Investment Co., Ltd.（采埃孚（中国）投资有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
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…
Tracking in urban street scenes plays a central role in autonomous systems such as self-driving cars. Most of the current vision-based tracking methods perform tracking in the image domain. Other approaches, eg based on LIDAR and radar, track purely in 3D. While some vision-based tracking methods invoke 3D information in parts of their pipeline, and some 3D-based methods utilize image-based information in components of their approach, we propose to use image- and world-space information jointly throughout our method. We present our tracking pipeline as a 3D extension of image-based tracking. F…
UT Austin（德克萨斯大学奥斯汀分校）；Waymo（Waymo公司）；Google Brain（谷歌大脑）
Autonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential. However, current sensor simulators leverage gaming engines such as Unreal or Unity, requiring manual creation of environments, objects and material properties. Such approaches have limited scalability and fail to produce realistic approximations of camera, lidar, and radar data without significant additional work. In this paper, we presen…
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）
Institute of Automotive Technology, Technical University of Munich（慕尼黑工业大学汽车技术研究所）；mLab:Real-Time and Embedded Systems Lab, University of Pennsylvania（宾夕法尼亚大学mLab实时与嵌入式系统实验室）
Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and weather conditions that need to be handled. In contrast to more constrained environments, such as automated underground trains, automotive perception systems cannot be tailored to a narrow field of specific tasks but must handle an ever-changing environment with unforeseen events. As currently no single sensor is able to reliably perceive all relevant activity in the surroundings, sensor data fusion is applied to perceive as much information as possible. Data fusion of different sensors and sensor moda…
Convergence Center, LG Electronics, Korea（LG电子韩国融合中心）；Arnia Software, Romania（Arnia软件公司（罗马尼亚））
Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each fra…
In this paper we presented mmPose-NLP, a novel Natural Language Processing (NLP) inspired Sequence-to-Sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data. To the best of the author's knowledge, this is the first method to precisely estimate upto 25 skeletal key-points using mmWave radar data alone. Skeletal pose estimation is critical in several applications ranging from autonomous vehicles, traffic monitoring, patient monitoring, gait analysis, to defense security forensics, and aid both preventative and actionable decision making. The use of mmWave radar…
Automotive radar sensors output a lot of unwanted clutter or ghost detections, whose position and velocity do not correspond to any real object in the sensor's field of view. This poses a substantial challenge for environment perception methods like object detection or tracking. Especially problematic are clutter detections that occur in groups or at similar locations in multiple consecutive measurements. In this paper, a new algorithm for identifying such erroneous detections is presented. It is mainly based on the modeling of specific commonly occurring wave propagation paths that lead to cl…
