Cooperative Perception Research Articles

Slim-FCP: Lightweight-Feature-Based Cooperative Perception for Connected Automated Vehicles

Cooperative perception provides a novel way to conquer the sensing limitation on a single automated vehicle and potentially improves driving safety. To reduce the transmission data volume, existing solutions use the intermediate data generated by convolutional neural network (CNN) models, namely, feature maps, to achieve cooperative perception. The feature maps are however too large to be transmitted by the current V2X technology. We propose a novel approach, called Slim-FCP, to significantly reduce the transmission data size. It enables a channelwise feature encoder to remove irrelevant features for a better compression ratio. In addition, it adopts an intelligent channel selection strategy through which only representative channels of feature maps are selected for transmission. To evaluate the effectiveness of Slim-FCP, we further define a recall-to-bandwidth (RB) ratio metric to quantitatively measure how the recall of object detection changes with respect to the available network bandwidth. Experiment results show that Slim-FCP reduces the transmission data size by 75%, compared with the best state-of-the-art solution, with a subtle loss on object detection’s recall.

Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving

Cooperative computing is promising to enhance the performance and safety of autonomous vehicles benefiting from the increase in the amount, diversity as well as scope of data resources. However, effective and privacy-preserving utilization of multi-modal and multi-source data remains an open challenge during the construction of cooperative mechanisms. Recently, Transformers have demonstrated their potential in the unified representation of multi-modal features, which provides a new perspective for effective representation and fusion of diverse inputs of intelligent vehicles. Federated learning proposes a distributed learning scheme and is hopeful to achieve privacy-secure sharing of data resources among different vehicles. Towards privacy-preserving computing and cooperation in autonomous driving, this paper reviews recent progress of Transformers, federated learning as well as cooperative perception, and proposes a hierarchical structure of Transformers for intelligent vehicles which is comprised of Vehicular Transformers, Federated Vehicular Transformers and the Federation of Vehicular Transformers to exploit their potential in privacy-preserving collaboration.

Survey on Cooperative Perception in an Automotive Context

The idea of cooperation has been introduced to self-driving cars about a decade ago with the aim to reduce the occlusion caused by other users or the scene. More recently, the research efforts turned toward cooperative infrastructure bringing a new kind of the point of view as well as more processing power. This paper lies in this new field providing a survey that addresses the cooperative environment. We provide an overview of the architectures available to create such a system as well as the challenges introduced by the cooperation. Later, we review the main blocks involved in the perception: localization, object detection & tracking, map generation. Each block is reviewed under the prism of cooperation. We also provide a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of the cooperative perception as well as a list of related scenarios alongside experimentations. Finally, we list some related datasets before concluding our paper, underlining the perspectives for further works.

Effects of Context, Assistance, and Communication on Beliefs About Cooperation in Human-Machine Teaming

The efficacy of future human-machine teaming will be determined by the ability of machine agents to engage in teamwork behaviors and processes with their human teammates. A critical aspect of teamwork is cooperation, as team members must work interdependently to achieve common goals. In the current experiment we investigated how violations of cooperation expectations, presented to participants in a narrative vignette, influenced ratings of cooperation on a novel measure derived from social interdependence theory. Our results indicated that participants’ ratings of cooperation were strongly influenced by actions associated with assisting and communicating with a cooperating partner; violations in these expectations resulted in much lower estimates of cooperation, though the identity of the actors as two humans or a human and a robot did not appear to matter. Overall, this is an important first step to understanding the factors that influence perceptions of cooperation in human-machine teaming.

Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic

Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 – 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Pillar-Based Cooperative Perception from Point Clouds for 6G-Enabled Cooperative Autonomous Vehicles

3D object detection is a significant aspect of the perception module in autonomous driving; however, with current technology, data sharing between vehicles and cloud servers for cooperative 3D object detection under the strict latency requirement is limited by the communication bandwidth. The sixth-generation (6G) networks have accelerated the transmission rate of the sensor data significantly with extreme low-latency and high-speed data transmission. However, which sensor data format and when to transmit it are still challenging. To address these issues, this study proposes a cooperative perception framework combined with a pillar-based encoder and Octomap-based compression at edges for connected autonomous vehicles to reduce the amount of missing detection in blind spots and further distances. This approach satisfies the constraints on the accuracy of the task perception and provides drivers or autonomous vehicles with sufficient reaction time by applying fixed encoders to learn a representation of point clouds (LiDAR sensor data). Extensive experiment results show that the proposed approach outperforms the previous cooperative perception schemes running at 30 Hz, and the accuracy of the object bounding box results in further distances (greater than 12 m). Furthermore, this approach achieves a lower total delay for the procession of the fusion data and the transmission of the cooperative perception message. To the best of our knowledge, this study is the first to introduce a pillar-based encoder and Octomap-based compression framework for cooperative perception between vehicles and edges in connected autonomous driving.

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review.

Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

Perspectives of Farmers on the Decline in Pinus pinea Nut Yield and the Sustainability of the Production: A Case Study in Kozak Basin in Western Turkey

In the Kozak Basin, which is the most important pine nut production and trade center in Turkey, there has been a significant decrease in production recently. In this study, the perspectives of the farmers in this area were investigated, specifically about reasons for the decrease in yields. For this purpose, a face-to-face survey was conducted with 378 pine nut farmers from the Kozak Basin. It was determined that the average age of the farmers is high, their educational achievement level is low, they generally also work in alternative sectors and almost all of them grow pine nuts in their own pine groves. The farmers’ perception of cooperatives was found to be negative. In general, the local people continue to produce pine nuts in their own pine groves, but nearly half of the participants also worked in other sectors to maintain their standard of living. Farmers attribute the low yield in the Kozak Basin to both abiotic and biotic factors. Mining activities, which are increasing, are seen as a threat to the future of production in the area. Also, there is a growing perception of biotic harm, especially from Western Conifer Seed Bug, Leptoglossus occidentalis. Climate change and air pollution are other prominent factors.

Work Environment: Leadership, Communication, and Autonomy. The Perception of Work Among Polish Employees During Turbulent Time of COVID-19 Pandemic

Purpose: The article focuses on the assessment of knowledge workers’ relation with superiors, internal communication, perceived autonomy and feeling of appreciation. The goal of the article is also to extend current knowledge of the perception of cooperation between employees. The authors were able to identify interdependencies among the researched variables and the impact of COVID-19 on peoples’ professional situation. Design/methodology/approach: The article presents the results of a CAWI survey conducted on a large sample of 2778 specialists and managers in Poland. The researchers used the purposive sampling method to reach the population of white-collar workers who experienced remote work or remote cooperation with their colleagues. Findings: The empirical study showed that in most cases superiors passed the test and were positively evaluated by their subordinates. The correlation analysis also showed that certain positive qualities of a manager go together and are positively correlated with professional situation assessment during the COVID-19 period. Research limitations/implications: The study describes the results in a large sample; however, the sampling method was not probabilistic and therefore we cannot use statistical inference for the general population of working specialists in Poland. Originality/value: The value of the article is the presentation of the up-to-date opinion of almost 3 thousands specialists employed in Poland. The text plays an important role in recording the influence of the pandemic on Polish employees.

Utilizing collaborative writing strategy to create skills of 21st century: EFL learners’ perceptions

Utilizing collaborative writing strategy in a writing class seems very helpful and beneficial. Through collaborative writing, it can build up critical thinking, creativity, communication, and collaboration as the skills of 21st century of learners in the learning process. The purpose of this research is to investigate English as a foreign language (EFL) learners’ perceptions toward the use of collaborative writing strategy in a writing class. The research method employed was qualitative method with descriptive research design. 51 EFL learners were involved in this research as respondents of this research. The data was collected through questionnaire with 20 statements available underlying four general perceptions so called: perceptions of academic writing concept, perceptions of cooperation, perception of writing skills, and perceptions of learning atmosphere. The data from questionnaire was analyzed descriptively. The findings showed that EFL learners’ perceptions on the implementation of collaborative writing strategy in a writing class was positive. EFL learners’ response towards questionnaire were dominated by response “agree” and “strongly agree”. From this findings, it can be concluded that EFL learners’ perceptions on the application of collaborative writing strategy in a writing class are varied. It is recommends the skills of 21st century can be build up through the implementation of collaborative writing strategy in a writing class.

Design, Implementation, and Evaluation of a Roadside Cooperative Perception System

Recently, with the advancement in autonomous driving, artificial intelligence, and vehicle-to-everything (V2X) communications, cooperative perception between roadway infrastructure and connected and automated vehicles via V2X communications has attracted increasing attention. However, most of the systems and prototypes reported are still at an initial stage. This paper introduces a newly developed and deployed roadside cooperative perception system with an edge-cloud structure and multiple kinds of sensors, including fisheye cameras, thermal cameras, and long-distance radars. The system is deployed at a roundabout at the intersection of State St. and W. Ellsworth Rd. in Ann Arbor, Michigan, U.S. With an edge-cloud structure, it can execute high-performance detection algorithms on edge devices at the field and collect data and analyze it on the cloud. The performance, in relation to detection rate, localization accuracy, and latency, is analyzed using the data collected from the field. These results show that the system has high potential for a wide range of applications, including infrastructure-vehicle cooperative perception, traffic data collection, and road safety studies. Two example applications—traffic volume monitoring and road safety warning—are introduced in the latter part of the paper to demonstrate the system’s capability.

Medical Students’ Perception of Organization and Informatization of Teaching During the COVID-19 Pandemic

Background and aim: Outbreak of the COVID-19 pandemic has had big impact on higher education system. To prevent spreading of the disease, distance learning was organised. The aim of this research was to assess medical students’ perception of quality of educational process during the COVID-19 pandemic.Respondents and methods: Online research was conducted during May 2021 on 142 students of fourth, fifth and sixth year of School of Medicine University of Zagreb. This research was conducted using Questionnaire for Assessing Quality of Distance Learning in Biomedicine Studies. It consists of 28 items which are rated using 7-point Likert scale and are distributed in six subscales. All statistical analyses were conducted using IBM® SPSS® Statistics for Windows (Version 25.0).Results: Statistically significant difference was found in Perception of Teachers’ Work between fourth and sixth year (p = 0.012), as well as Perception of Educational Environment and Personal Academic Achievements between fourth and fifth, and fourth and sixth year of study (p < 0.001). The lowest mean for all subscales was found in fourth year, except subscales Perception of Personal Academic Activity (3.51 ± 1.32) and Perception of Equipment Quality (6.22 ± 1.04). The highest mean was found in sixth year for subscales Perception of Distance Learning Organization (3.47 ± 1.04), Perception of Cooperation (4.39 ± 1.04) and Perception of Teachers’ Work (4.73 ± 0.96). For subscales Perception of Educational Environment and Personal Academic Achievements and Perception of Equipment Quality, the highest mean was found in the fifth year (5.42 ± 1.14 and 6.28 ± 1.01, respectively). Majority of students were satisfied with equipment quality and Internet connection they used, however most of them (N = 68, 47.9 %) thought that teachers were not qualified enough to use tools for online teaching.Conclusion: The highest quality of educational process was perceived by sixth-year (64.9 % of the total score), and the lowest by fourth-year medical students (58.7 % of the total score). Teachers should continue with their education in order to maximize the use of digital technology in achieving educational outcomes, especially in biomedical area.

Time-Sensitive Cooperative Perception for Real-Time Data Sharing over Vehicular Communications: Overview, Challenges, and Future Directions

Time-Sensitive Cooperative Perception for Real-Time Data Sharing over Vehicular Communications: Overview, Challenges, and Future Directions

Object Perceptibility Prediction for Transmission Load Reduction in Vehicle-Infrastructure Cooperative Perception.

Vehicle-infrastructure cooperative perception is an ingenious way to eliminate environmental perception blind areas of connected and autonomous vehicles (CAVs). However, if the infrastructure transmits all environmental information to the nearby CAVs, the transmission load is so heavy that it causes a waste of network resources, such as time and bandwidth, because parts of the information are redundant for the CAVs. It is an efficient manner for the infrastructure to merely transmit the information about objects which cannot be perceived by the CAVs. Therefore, the infrastructure needs to predict whether an object is perceptible for a CAV. In this paper, a machine-leaning-based model is established to settle this problem, and a data filter is also designed to enhance the prediction accuracy in various scenarios. Based on the proposed model, the infrastructure transmits the environmental information selectively, which significantly reduces the transmission load. The experiments prove that the prediction accuracy of the model achieves up to 95%, and the transmission load is reduced by 55%.

Cooperation between Preschool Institutions and Parents within Early Childhood Environmental Education

The goal of this research was to explore the perceptions of cooperation between preschool institutions and parents within Early Childhood Environmental Education activities, primarily within the dimension of including parents in preschool activities. In the theoretical framework of the study, we discuss the major aspects of cooperation between families and preschool institutions and the significance of learning about the environment for the cooperation process. Many authors agree that cooperation between families and preschool institutions comprises the following dimensions: information sharing between preschool teachers and parents, including parents in preschool activities, parents’ involvement in supporting the child’s development at home, and parents’ participation in preschool institutions executive bodies. In this paper, we focus on the dimension of including parents in preschool activities through Early Childhood Environmental Education. The results show that the majority of questionnaire items exposed statistically significant differences between the perception of parents and the perception of preschool teachers when it comes to including parents in Early Childhood Environmental Education activities. We consider that a series of planned actions is necessary for including as many parents as possible in the Early Childhood Environmental Education activities. One of the main reasons for such an initiative is the veracity of such activities and the possibility for children to create and develop their creative abilities through research.

Perceptions of Police Among Kenyan Female Immigrants in the United States

We explored how highly educated and middle-class Kenyan female immigrants perceive their encounters with the police in the United States, including the decision to access the criminal justice system in response to their victimization. We found a positive correlation between perceptions of procedural justice and cooperation among Kenyan women immigrants. Conversely, prior victimization was inversely associated with help-seeking among these women. When Kenyan female immigrants perceived high police effectiveness in dealing with IPV, they were more likely to feel obligated to obey the U.S. police. Implications and recommendations for future research are discussed.

Fast and Robust Registration of Partially Overlapping Point Clouds

Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles and multi-agent SLAM. The relative translation between point clouds in these applications is higher than in traditional SLAM and odometry applications, which challenges the identification of correspondences and a successful registration. In this paper, we propose a novel registration method for partially overlapping point clouds where correspondences are learned using an efficient point-wise feature encoder, and refined using a graph-based attention network. This attention network exploits geometrical relationships between key points to improve the matching in point clouds with low overlap. At inference time, the relative pose transformation is obtained by robustly fitting the correspondences through sample consensus. The evaluation is performed on the KITTI dataset and a novel synthetic dataset including low-overlapping point clouds with displacements of up to 30 m. The proposed method achieves on-par performance with state-of-the-art methods on the KITTI dataset, and outperforms existing methods for low overlapping point clouds. Additionally, the proposed method achieves significantly faster inference times, as low as 410 ms, between 5 and 35 times faster than competing methods. Our code and dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/eduardohenriquearnold/fastreg</uri> .

Parents and teachers' perceptions of kindergarten-family cooperation in China

본 연구의 목적은 가정과 유치원 간 협력에 대하여 중국 길림성 사평시 유치원 교사와 학부모가 어떻게 생각하고 있는지를 탐색하기 위함이다. 이러한 목적을 달성하기 위하여 중국 길림성 사평시 유치원교사 4명과 학부모 4명을 선정하였고 2020년 6월부터 10월까지 4개월간 참여자 별로 2회씩 총 16회에 걸쳐 면담을 진행하여 자료를 수집하였다. 수집된 자료로 반복적이고 순환적인 자료 분석 과정을 거친 결과, 면담에 참여한 중국 유아 교사와 학부모는 유치원과 가정의 협력이 필요하다고 인식하고는 있었으나 주도적인 역할은 교사가 해야 한다고 여기고 있었으며 초등화를 지향하는 중국 사회적 분위기를 바탕으로 한 갈등에 노출되어 있었다. 또한 교사와 조부모, 학부모 사이에 태도적 차이와 소통의 방법 면에서의 갈등을 인식하고 있었다. 이처럼 중국 유아 교사와 학부모는 협력의 필요성은 인식하지만 실제 협력 과정에서 실천과정에 있어 어려움을 느끼고 있었다. 본 연구는 중국 가정과 유치원 간의 협력 주체인 학부모와 교사의 인식을 알아봄으로써효율적 협력이 이루어질 수 있는 실천적 방안을 모색하였다는 데 의의를 둔다.

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded.

Toward Lightweight, Privacy-Preserving Cooperative Object Classification for Connected Autonomous Vehicles

Collaborative perception enables autonomous vehicles to exchange sensor data among each other to achieve cooperative object classification, which is considered an effective means to improve the perception accuracy of connected autonomous vehicles (CAVs). To protect information privacy in cooperative perception, we propose a lightweight, privacy-preserving cooperative object classification framework that allows CAVs to exchange raw sensor data (e.g., images captured by HD camera), without leaking private information. Leveraging chaotic encryption and additive secret sharing technique, image data are first encrypted into two ciphertexts and processed, in the encrypted format, by two separate edge servers. The use of chaotic mapping can avoid information leakage during data uploading. The encrypted images are then processed by the proposed privacy-preserving convolutional neural network (P-CNN) model embedded in the designed secure computing protocols. Finally, the processed results are combined/decrypted on the receiving vehicles to realize cooperative object classification. We formally prove the correctness and security of the proposed framework and carry out intensive experiments to evaluate its performance. The experimental results indicate that P-CNN offers exactly almost the same object classification results as the original CNN model, while offering great privacy protection of shared data and lightweight execution efficiency.