Cooperative perception, or collective perception (CP) is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). We present three different experiments to demonstrate the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in the experiments that a connected vehicle (CV) can "see" a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.
The University of Sydney（悉尼大学）；Cohda Wireless（科达无线公司）
The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety of road transportation. Recent advances in V2X communication primarily address the definition of V2X messages and data dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a largely unsolved problem is how a connected vehicle (CV) can efficiently and consistently fuse its local perception information with the data received from other I…
CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction
UCLA Mobility Lab, University of California, Los Angeles, USA（加州大学洛杉矶分校移动实验室）；Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China（教育部道路与交通工程重点实验室）
Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；University of Macau（澳门大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of New South Wales（新南威尔士大学）
The idea of cooperative perception is to benefit from shared perception data between multiple vehicles and overcome the limitations of on-board sensors on single vehicle. However, the fusion of multi-vehicle information is still challenging due to inaccurate localization, limited communication bandwidth and ambiguous fusion. Past practices simplify the problem by placing a precise GNSS localization system, manually specify the number of connected vehicles and determine the fusion strategy. This paper proposes a map-based cooperative perception framework, named map container, to improve the acc…
Nav-SCOPE: Swarm Robot Cooperative Perception and Coordinated Navigation
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心，清华大学）；School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University（机械与电子工程学院，北京信息科学与技术大学）
Italian Institute of Technology（意大利技术研究院）；Politecnico di Milano（米兰理工大学）；Xi’an Jiaotong University（西安交通大学）；Hanyang University ERICA（汉阳大学ERICA校区）
This article proposes a method for learning and robotic replication of dynamic collaborative tasks from offline videos. The objective is to extend the concept of learning from demonstration (LfD) to dynamic scenarios, benefiting from widely available or easily producible offline videos. To achieve this goal, we decode important dynamic information, such as the Configuration Dependent Stiffness (CDS), which reveals the contribution of arm pose to the arm endpoint stiffness, from a three-dimensional human skeleton model. Next, through encoding of the CDS via Gaussian Mixture Model (GMM) and deco…
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）
In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. Cooperative perception (CP) solutions based on Vehicle-to-Everything (V2X) communications aim to avoid such issues by cooperatively leveraging additional points of view for the world around the AV. This approach usually requires many…
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学合作 Medianet 创新中心）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Warwick（华威大学）
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unrel…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sony AI（索尼人工智能）；Stanford University（斯坦福大学）
Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce COOPERNAUT, an end-to-end learning model that uses cross…
CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset for Vehicle-Infrastructure Cooperation in Adverse Weather
Department of Mechanical and Mechatronics Engineering, University of Waterloo（滑铁卢大学机械与机电工程系）；Department of Mechanical Engineering, University of New Brunswick（新不伦瑞克大学机械工程系）；Department of Data and Systems Engineering, University of Hong Kong（香港大学数据与系统工程系）；Technology Partnerships and Innovations, Rogers Communications, Canada Inc.（罗杰斯通讯公司技术创新部）；Department of Mechanical Engineering, University of Alberta（阿尔伯塔大学机械工程系）
Purdue University（普渡大学）；Saline High School（萨林高中）；University of Michigan（密歇根大学）
Existing data collection methods for traffic operations and control usually rely on infrastructure-based loop detectors or probe vehicle trajectories. Connected and automated vehicles (CAVs) not only can report data about themselves but also can provide the status of all detected surrounding vehicles. Integration of perception data from multiple CAVs as well as infrastructure sensors (e.g., LiDAR) can provide richer information even under a very low penetration rate. This paper aims to develop a cooperative data collection system, which integrates Lidar point cloud data from both infrastructur…
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；China Automotive Innovation Corporation（中国汽车创新公司）；The University of Hong Kong（香港大学）
Carnegie Mellon University（卡内基梅隆大学）；InfoTech Labs, Toyota Motor North America R&D（丰田北美研发公司InfoTech实验室）
Sensor-based perception on vehicles are becoming prevalent and important to enhance the road safety. Autonomous driving systems use cameras, LiDAR, and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding…
MMUSV-Sim: A Perception-Oriented Simulation and Data-Generation Platform for Multi-USV Cooperative Perception
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）；School of Intelligent Imagery Engineering, Beijing Film Academy（北京电影学院智能影像工程学院）
City University of Hong Kong（香港城市大学）；The Chinese University of Hong Kong（香港中文大学）
Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking without considering information hidden in surgical scenes are not intelligent enough, while the latest supervised imitation learning (IL)-based methods require expensive sensor data and suffer from distribution mismatch issues caused by limited demonstrations. In this paper, we propose a novel Imitation Learning framework for Laparoscope Control (ILLC) with reinforcement learning (RL), which can efficiently learn the con…
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 …
NVIDIA（英伟达）；University of Southern California（南加州大学）
We present a system to infer and execute a human-readable program from a real-world demonstration. The system consists of a series of neural networks to perform perception, program generation, and program execution. Leveraging convolutional pose machines, the perception network reliably detects the bounding cuboids of objects in real images even when severely occluded, after training only on synthetic images using domain randomization. To increase the applicability of the perception network to new scenarios, the network is formulated to predict in image space rather than in world space. Additi…
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Beijing Jiaotong University（北京交通大学）
Timely and reliable environment perception is fundamental to safe and efficient automated driving. However, the perception of standalone intelligence inevitably suffers from occlusions. A new paradigm, Cooperative Perception (CP), comes to the rescue by sharing sensor data from another perspective, i.e., from a cooperative vehicle (CoV). Due to the limited communication bandwidth, it is essential to schedule the most beneficial CoV, considering both the viewpoints and communication quality. Existing methods rely on the exchange of meta-information, such as visibility maps, to predict the perce…
Multi-Agent Systems (MASs) have been used to solve complex problems that demand intelligent agents working together to reach the desired goals. These Agents should effectively synchronize their individual behaviors so that they can act as a team in a coordinated manner to achieve the common goal of the whole system. One of the main issues in MASs is the agents' coordination, being common domain experts observing MASs execution disapprove agents' decisions. Even if the MAS was designed using the best methods and tools for agents' coordination, this difference of decisions between experts and MA…
Programming by demonstration (PbD) is an effective technique for developing complex robot manipulation tasks, such as opening bottles or using human tools. In order for such tasks to generalize to new scenes, the robot needs to be able to perceive objects, object parts, or other task-relevant parts of the scene. Previous work has relied on rigid, task-specific perception systems for this purpose. This paper presents a flexible and open-ended perception system that lets users specify perceptual "landmarks" during the demonstration, by capturing parts of the point cloud from the demonstration sc…
University of Warwick（华威大学）；Jaguar Land Rover Ltd.（捷豹路虎有限公司）；University of Surrey（萨里大学）
3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, limited field-of-view and low-point density of the sensor data cannot be reliably and cost-effectively addressed by multi-modal sensing from a single point of view. Alternatively, cooperative perception incorporates information from spatially diverse sensors distributed around the…
Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads
Zhejiang University（浙江大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Michigan（密歇根大学）
Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation
CARISSMA Institute for Electric, COnnected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt（CARISSMA电动、连接与安全移动研究所（C-ECOS）、因戈尔施塔特技术大学）；University of Applied Sciences Aschaffenburg（阿施发堡应用科学大学）
