LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation
University of Science and Technology Beijing（北京科技大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；The University of Hong Kong（香港大学）；Chengfang Financial Information Technology Services CO.LTD（诚方金融信息技术服务有限公司）
Temporal perception, defined as the capability to detect and track objects across temporal sequences, serves as a fundamental component in autonomous driving systems. While single-vehicle perception systems encounter limitations, stemming from incomplete perception due to object occlusion and inherent blind spots, cooperative perception systems present their own challenges in terms of sensor calibration precision and positioning accuracy. To address these issues, we introduce LET-VIC, a LiDAR-based End-to-End Tracking framework for Vehicle-Infrastructure Cooperation (VIC). First, we employ Temporal Self-Attention and VIC Cross-Attention modules to effectively integrate temporal and spatial information from both vehicle and infrastructure perspectives. Then, we develop a novel Calibration Error Compensation (CEC) module to mitigate sensor misalignment issues and facilitate accurate feature alignment. Experiments on the V2X-Seq-SPD dataset demonstrate that LET-VIC significantly outperforms baseline models. Compared to LET-V, LET-VIC achieves +15.0% improvement in mAP and a +17.3% improvement in AMOTA. Furthermore, LET-VIC surpasses representative Tracking by Detection models, including V2VNet, FFNet, and PointPillars, with at least a +13.7% improvement in mAP and a +13.1% improvement in AMOTA without considering communication delays, showcasing its robust detection and tracking performance. The experiments demonstrate that the integration of multi-view perspectives, temporal sequences, or CEC in end-to-end training significantly improves both detection and tracking performance. All code will be open-sourced.
The integration of multiple cameras and 3D Li- DARs has become basic configuration of augmented reality devices, robotics, and autonomous vehicles. The calibration of multi-modal sensors is crucial for a system to properly function, but it remains tedious and impractical for mass production. Moreover, most devices require re-calibration after usage for certain period of time. In this paper, we propose a single-shot solution for calibrating extrinsic transformations among multiple cameras and 3D LiDARs. We establish a panoramic infrastructure, in which a camera or LiDAR can be robustly localize…
With the development of autonomous driving, the improvement of autonomous driving technology for individual vehicles has reached the bottleneck. The advancement of vehicle-road cooperation autonomous driving technology can expand the vehicle's perception range, supplement the perception blind area and improve the perception accuracy, to promote the development of autonomous driving technology and achieve vehicle-road integration. This project mainly uses lidar to develop data fusion schemes to realize the sharing and combination of vehicle and road equipment data and achieve the detection and …
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Baidu Inc.（百度公司）；University of Chinese Academy of Science（中国科学院大学）
Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-infrastructure cooperation is required to achieve Level 5 autonomy. However, there is still NO dataset from real scenarios available for computer vision researchers to work on vehicle-infrastructure cooperation-related problems. To accelerate computer vision research and innovation for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first large-scale, multi-moda…
As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR range and attribute scan sequences including multiple-return range, signal, reflectivity, and ambient infrared. Our algorithm -- dubbed "Jiffy" -- achieves substantial compression by exploiting spatiotemporal redundancy and sparsity. Speed is accomplished by maximizing use of sin…
End-to-End Autonomous Driving through V2X Cooperation
The University of Hong Kong（香港大学）；AIR, Tsinghua University（清华大学智能产业研究院）；Beijing Institute of Technology（北京理工大学）；University of Science and Technology Beijing（北京科技大学）
PerceptIn（感知创新公司）；South China University of Technology（华南理工大学）；Northwestern University（西北大学）
The infrastructure-vehicle cooperative autonomous driving approach depends on the cooperation between intelligent roads and intelligent vehicles. This approach is not only safer but also more economical compared to the traditional on-vehicle-only autonomous driving approach. In this paper, we introduce our real-world deployment experiences of cooperative autonomous driving, and delve into the details of new challenges and opportunities. Specifically, based on our progress towards commercial deployment, we follow a three-stage development roadmap of the cooperative autonomous driving approach:i…
MIL-LC: A Robust Magnetometer-Inertial-LiDAR Fusion Multimodal Localization Framework
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Centre for Advanced Robotics Technology Innovation (CARTIN)（先进机器人技术创新中心）
University of Science and Technology of China（中国科学技术大学）；Google Cloud（谷歌云）
Recently, it has become popular to deploy sensors such as LiDARs on the roadside to monitor the passing traffic and assist autonomous vehicle perception. Unlike autonomous vehicle systems, roadside sensors are usually affiliated with different subsystems and lack synchronization both in time and space. Calibration is a key technology which allows the central server to fuse the data generated by different location infrastructures, which can deliver improve the sensing range and detection robustness. Unfortunately, existing calibration algorithms often assume that the LiDARs are significantly ov…
Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Shanghai AI Laboratory（上海人工智能实验室）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；Mogo Auto Intelligence and Telematics Information Technology Company Ltd.（摩智智行智能与远程信息技术有限公司）
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Delaware（特拉华大学）；University of Chinese Academy of Sciences（中国科学院大学）
Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is a new paradigm of autonomous driving, which relies on the cooperation between intelligent roads and autonomous vehicles. This paradigm has been shown to be safer and more efficient compared to the on-vehicle-only autonomous driving paradigm. Our real-world deployment data indicates that the effectiveness of IVCAD is constrained by reliability and performance of commercial communication networks. This paper targets this exact problem, and proposes INTERNEURON, a middleware to achieve high communication reliability between intellig…
Scuola Superiore Sant’Anna（圣安娜高等学校）；Hitachi Rail STS（日立铁路STS公司）
The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units. A promising approach for processing such sensory data is the use of deep learning models, which proved to achieve excellent performance in other application domains, as robotics and self-driving cars. However, testing new algorithms and solutions…
Bristol Research and Innovation Laboratory, Toshiba Europe Ltd.（东芝欧洲有限公司布里斯托尔研究与创新实验室）
Mobile robots have disrupted the material handling industry which is witnessing radical changes. The requirement for enhanced automation across various industry segments often entails mobile robotic systems operating in logistics facilities with little/no infrastructure. In such environments, out-of-box low-cost robotic solutions are desirable. Wireless connectivity plays a crucial role in successful operation of such mobile robotic systems. A wireless mesh network of mobile robots is an attractive solution; however, a number of system-level challenges create unique and stringent service requi…
The use of infrastructure sensor technology for traffic detection has already been proven several times. However, extrinsic sensor calibration is still a challenge for the operator. While previous approaches are unable to calibrate the sensors without the use of reference objects in the sensor field of view (FOV), we present an algorithm that is completely detached from external assistance and runs fully automatically. Our method focuses on the high-precision fusion of LiDAR point clouds and is evaluated in simulation as well as on real measurements. We set the LiDARs in a continuous pendulum …
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
In recent years, Onboard Self Localization (OSL) methods based on cameras or Lidar have achieved many significant progresses. However, some issues such as estimation drift and feature-dependence still remain inherent limitations. On the other hand, infrastructure-based methods can generally overcome these issues, but at the expense of some installation cost. This poses an interesting problem of how to effectively combine these methods, so as to achieve localization with long-term consistency as well as flexibility compared to any single method. To this end, we propose a comprehensive optimizat…
This work presents a technique for localization of a smart infrastructure node, consisting of a fisheye camera, in a prior map. These cameras can detect objects that are outside the line of sight of the autonomous vehicles (AV) and send that information to AVs using V2X technology. However, in order for this information to be of any use to the AV, the detected objects should be provided in the reference frame of the prior map that the AV uses for its own navigation. Therefore, it is important to know the accurate pose of the infrastructure camera with respect to the prior map. Here we propose …
Evaluation of Flight Parameters in UAV-based 3D Reconstruction for Rooftop Infrastructure Assessment
Ingenuity Labs Research Institute, Queen's University, Canada（Ingenuity Labs研究机构、女王大学，加拿大）；Department of Civil Engineering, Queen's University, Canada（土木工程系、女王大学，加拿大）；Department of Electrical and Computer Engineering, Queen's University, Canada（电气与计算机工程系、女王大学，加拿大）
RWTH Aachen University（亚琛工业大学）；Institute for Automotive Engineering（汽车工程研究所）
Enabling secure and reliable high-bandwidth lowlatency connectivity between automated vehicles and external servers, intelligent infrastructure, and other road users is a central step in making fully automated driving possible. The availability of data interfaces, which allow this kind of connectivity, has the potential to distinguish artificial agents' capabilities in connected, cooperative, and automated mobility systems from the capabilities of human operators, who do not possess such interfaces. Connected agents can for example share data to build collective environment models, plan collec…
Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless, their usage in traffic monitoring systems is less ubiquitous. We identify two significant obstacles in cost-effectively and efficiently developing such a LiDAR-based traffic monitoring system: (i) public LiDAR datasets are insufficient for supporting perception tasks in infrastructure systems, and (ii)…
Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception
Shenzhen Automotive Research Institute, Beijing Institute of Technology（北京理工大学深圳汽车研究院）；School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；School of Computer Science, The University of Hong Kong（香港大学计算机学院）
Large driving datasets are a key component in the current development and safeguarding of automated driving functions. Various methods can be used to collect such driving data records. In addition to the use of sensor equipped research vehicles or unmanned aerial vehicles (UAVs), the use of infrastructure sensor technology offers another alternative. To minimize object occlusion during data collection, it is crucial to record the traffic situation from several perspectives in parallel. A fusion of all raw sensor data might create better conditions for multi-object detection and tracking (MODT)…
GRASP Lab, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；Ford Autonomous Vehicles LLC（福特自动驾驶汽车有限责任公司）
Vehicle localization is essential for autonomous vehicle (AV) navigation and Advanced Driver Assistance Systems (ADAS). Accurate vehicle localization is often achieved via expensive inertial navigation systems or by employing compute-intensive vision processing (LiDAR/camera) to augment the low-cost and noisy inertial sensors. Here we have developed a framework for fusing the information obtained from a smart infrastructure node (ix-node) with the autonomous vehicles on-board localization engine to estimate the robust and accurate pose of the ego-vehicle even with cheap inertial sensors. A sma…
V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems
China University of Mining and Technology -Beijing（中国矿业大学（北京））；Tsinghua University（清华大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（自动化与智能感知学院，上海交通大学）；Global College, Shanghai Jiao Tong University（全球学院，上海交通大学）
LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrastructure, the profound impact of differing LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection an…
College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；School of Information Science and Engineering, Shenyang University of Technology（沈阳工业大学信息科学与工程学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 mobility学院）；School of Software, Beihang University（北京航空航天大学软件学院）；School of Information and Engineering, China University of Geosciences Beijing（中国地质大学（北京）信息与工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs. We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accuracy. To prove the effectiveness of our proposed modules, we train and evaluate the model on three different vehicle and infrastructure datasets. To show the domain adaptation ability of our detector, we train it on an infrastructure dataset from China and perform transfer learni…
Multispectral LiDAR data for extracting tree points in urban and suburban areas
Department of Geodesy and Geoinformation, TU Wien（大地测量与地理信息系，维也纳技术大学）；D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（三维光学计量（3DOM）单元，布鲁诺·凯斯勒基金会）；Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, National Land Survey of Finland（摄影测量与遥感系，芬兰地理研究 institute，芬兰国家土地测绘局）
Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）；ShanghaiTech University（上海科技大学）；Peking University（北京大学）；Nanyang Technological University（南洋理工大学）；University of Kentucky（肯塔基大学）
State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, \etc) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is th…
Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation
Engineering Research Institute of Elche (I3E)（埃尔切工程研究所 (I3E)）；Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI)（瓦伦西亚人工智能研究生院与研究网络 (ValgrAI)）
