Cooperative Perception Research Articles

Consensus-Based Distributed Cooperative Perception for Connected and Automated Vehicles

Cooperative perception techniques incorporating Vehicle-to-Everything (V2X) information offer new possibilities for enhancing the perception capability of automated vehicles (AVs), but also present a new challenge of how to maximize the benefits of connected information with limited communication burden. In this context, this paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information. Given the common multi-sensor configurations of AVs, we design a consensus-based distributed cooperative perception (DCP) algorithm in the framework of multi-layer information fusion for local sensors and connected nodes, and give a nonlinear form based on cubature rules to include more accurate nonlinear system models and nonlinear sensors. Considering the high maneuverability of vehicle targets, we then extend the DCP algorithm to a multi-model form (DMMCP) to improve the model uncertainty of maneuvering targets via combining the prior knowledge of multiple models, which also gives a calculation method for model probability and its average consensus in the context of multiple local sensors. Besides, a new consensus information weight strategy and the properties from different consensus information weights are discussed. The simulation results demonstrate the superiority of both our algorithms over the traditional algorithms in accuracy and consistency, moreover, the DMMCP algorithm, which takes into account model uncertainty, shows better performance than DCP in complex conditions.

Moderating Effects of Organizational Social Capital on the Relationship between Public Welfare Officers' Perception of Public-Private Cooperation and the Performance of Outreach Health and Welfare Services

Moderating Effects of Organizational Social Capital on the Relationship between Public Welfare Officers' Perception of Public-Private Cooperation and the Performance of Outreach Health and Welfare Services

A Novel Cooperation-Guided Warning of Invisible Danger from AR-HUD to Enhance Driver’s Perception

Augmented Reality (AR) has the potential to help drivers become aware of invisible hazards through an Augmented Reality Head-Up Display (AR-HUD). However, this issue is still underexplored. To address it, a novel warning system for invisible dangers in AR-HUD user interfaces has been designed as a carrier for agents' cognitive information to enhance driver perception. This design was created by using a team cooperation perception model that combined the perception cycle of a human driver with a computational agent. Furthermore, user experiments were conducted to investigate the impact of this design on safe driving in two typical scenarios. The experimental results showed that this design can significantly improve drivers’ situation awareness and reaction time in both human-driving and auto-pilot modes, and enhance human drivers' trust in the auto-pilot system. The model and design can be generalized to more AR-HUD scenarios requiring human-machine perception and cognitive cooperation.

Energy Consumption Optimization of Connected and Autonomous Vehicles Based on Cooperative Perception in Ramp Overflow Scene

The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles’ energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow’s impact on autonomous vehicles’ energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles.

Exploring police legitimacy and other factors in predicting cooperation with police in the Atlanta Korean American community

PurposeLimited research exists on the perceptions of police within specific ethnic minority groups. The primary purpose of this study is to investigate the experiences of Korean and Korean American residents in the Metro Atlanta area regarding their perceptions of cooperation with the police, particularly in relation to hate crimes, along with their perceptions of police legitimacy and other relevant factors. By focusing on this specific population, the study aims to shed light on their unique perspectives and contribute to a deeper understanding of the complex dynamics between ethnic minorities and law enforcement.Design/methodology/approachThe authors’ sample comprised 128 Korean residents who were asked about their demographics, victimization experiences, self-rated English proficiency and police legitimacy. Multiple linear regression analyses were employed to investigate the impact of police legitimacy, victimization experiences and English-speaking skills on the participants' level of cooperation with the police.FindingsPolice legitimacy and self-rated levels of English proficiency emerged as the most significant factors in predicting the level of cooperation among residents with the police. Furthermore, individuals who have experienced crime victimization in the past were more willing to cooperate with the police compared to those who have not. Additionally, men showed a higher tendency to cooperate with the police compared to women participants.Originality/valueThe findings of this study suggest important implications for the policies and strategies aimed at enhancing the relationship between the Korean American community and the police. These implications include the need for improved language support for non-English speaking community members and the importance of building trust and fostering mutual understanding to cultivate positive police-community relations. By implementing measures based on these findings, it is recommended to promote a more inclusive and effective approach to policing within the Korean American population.

What drives perceptions of partisan cooperation?

Abstract What drives voters' perceptions of partisan cooperation? In this note, we investigate whether voters have accurate beliefs about which parties regularly cooperate with one another, and whether these beliefs follow the real-time portrait of cooperation and conflict between parties that is reported in the news. We combine original survey data of voters' perceptions of party cooperation in four countries over two time periods with a measure of parties' public relationships as reported by the media. We find that voters' perceptions of cooperation and conflict among parties do reflect actual patterns of interactions. This pattern holds even after controlling for policy differences between parties as well as joint cabinet membership. Furthermore, we show that the impact of contemporary events on cooperation perceptions is most pronounced for voters who monitor the political news more carefully. Our findings have important implications for partisan cooperation and mass–elite linkages. Specifically, we find that contrary to the usual finding that voters are generally uninformed about politics, voters hold broadly accurate beliefs about the patterns of partisan cooperation, and importantly, these views track changes in relevant news. This reflects positively on the masses' capacities to infer parties' behaviors.

ROS-Based Collaborative Driving Framework in Autonomous Vehicular Networks

This paper investigates the optimal data transmission for collaborative driving in autonomous vehicular networks (AVNs). We consider that vehicles communicate with each other based on the definition of Robot Operating System (ROS), which is a pervasively adopted middleware operating system for autonomous vehicles such as Baidu Apollo. ROS defines a publish/subscribe scheme for inter-vehicular communications, in which vehicles subscribe to “topics” published by adjacent vehicles; a “topic” is related to the real-time sensing/computing data that a vehicle intends to share with nearby neighbors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , radar or camera images captured. By sharing various sensing data using different topics at the real-time, vehicles in the same area therefore can collaboratively drive with cooperative perception and computing. However, multiple data flows for different topics would coexist along overlapping transmission paths during this process. As a result, a fundamental issue raised is how to schedule the contending data flows in the mobile and bandwidth-constrained vehicular networks. In this paper, we model the ROS-based publish/subscribe scheme as an optimization problem which jointly considers the power allocation and conflict avoidance of the communication process in AVNs. By applying the Lyapunov Optimization, we decompose the model to calculate the power and sub-carrier proportion on each node while avoiding link conflicts, and finally obtain the optimal resource allocation strategy. By combining the corresponding link conflict constraints, we are able to encapsulate the optimization model in a stable set to efficiently avoid link conflicts, and thereby reduce the resource waste caused by the link interference and data flow conflicts. Using simulations, we show that our method has good advantages in resource optimization.

Why do people legitimize and cooperate with the police? Results of a randomized control trial on the effects of procedural justice in Quito, Ecuador

The present study employs a randomized control trial design to evaluate the impact of deterrence and procedural justice on perceptions of legitimacy and cooperation with law enforcement among individuals in Quito, Ecuador. Specifically, a sample of 308 premises where alcohol is sold were randomly assigned to one of two conditions: an experimental group (n = 156) in which officers received training in the implementation of a procedural justice-based script, and a control group (n = 152) in which officers were not provided with any specific instructions. Results indicate that the manipulation of procedural justice was associated with a significant enhancement in perceptions of legitimacy and a greater willingness to cooperate with the police.

Scalable cooperative perception for connected and automated driving

Cooperative perception (a.k.a. Collective perception or cooperative sensing) will allow Connected and Automated Vehicles (CAVs) to share information about detected objects. Cooperative perception improves the sensing accuracy, confidence and range of CAVs, and extends their perception of the driving environment. First message generation rules based on the mobility and dynamics of detected objects have been proposed to decide when a cooperative perception message should be generated and what information it should include. Studies have shown that this type of generation rules can compromise the scalability of cooperative perception and vehicular networks, as they tend to transmit significant amounts of redundant information and generate small and frequent cooperative perception messages that increase the communications overhead. To combat these inefficiencies, this paper proposes and evaluates three techniques that combine, for the first time, baseline mobility-based generation rules for cooperative perception messages with mechanisms to control the redundancy and organize the information about detected objects in order to avoid the frequent transmission of small messages. This study demonstrates that the proposed techniques improve the perception of CAVs and reduce the information age. In addition, the techniques reduce the channel load and improve the scalability of cooperative perception services and vehicular networks. The study demonstrates that the most effective technique is based on: (1) first applying the generation rules to decide whether a cooperative perception message should be generated, (2) then applying redundancy control, and finally (3) organizing the information about all detected objects to avoid small and frequent messages.

Networking Based ISAC Hardware Testbed and Performance Evaluation

As one of the candidate technologies for 6G mobile system, integrated sensing and communication (ISAC) can support the mobile network evolving from the communicationonly network to the multi-functional network. The networking based ISAC can empower the effective multi-node cooperative perception and sensing data sharing among multiple sensing nodes. In this article, we explore different properties of signals available for radar sensing and aim to combine the ISAC technology with the cellular network by optimizing the multidimensional resource scheduling. In the developed simulation platform, different mapping patterns of reference signals are exploited to realize the performance trade-off between communication and sensing functions. Finally, the ISAC enabled cooperative perception hardware testbed has been designed and implemented, which can decrease the positioning error by 61% compared to a single ISAC system.

Editorial introduction: Punctuated equilibrium, multiple streams, environmental governance, and many more

Editorial introduction: Punctuated equilibrium, multiple streams, environmental governance, and many more

An automated driving systems data acquisition and analytics platform

In this paper, an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception are presented. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which is capable of processing the sensor data from multi-CAVs and extracting the objects’ Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multi-CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high-definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects’ coordinates in Frenet coordinates and the lane information. In addition, to refine trajectories from the object tracking algorithms, a post-processing method is proposed. Given the objects’ information from the object detection and tracking and the world model, a Kalman filter and Chi-square test method are applied to reduce the noise and remove the outlier in the trajectories. Aiming at tackling the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories belonging to the same object. Then, a vehicle-kinematics-based trajectory prediction method is used, and a forward–backward-smoothing technique is applied to reconstruct the trajectory between the discontinuous trajectories. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the improvements by the post-processing algorithm in terms of noise level and outlier removal are discussed, which confirm the functionality and effectiveness of the proposed holistic data collection and processing platform. In another aspect, the extracted objects’ information and generated HD maps can be used for several purposes in the transportation research community and ADS development community: analyzing the interaction between human-driven vehicles and ADS-equipped vehicles, car-following behavior analysis of ADS-equipped vehicles, traffic flow status analysis and modeling, and scenario generation for ADS testing.

Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

Deep learning has been widely used in intelligent vehicle driving perception systems, such as 3D object detection. One promising technique is Cooperative Perception, which leverages Vehicle-to-Vehicle (V2V) communication to share deep learning-based features among vehicles. However, most cooperative perception algorithms assume ideal communication and do not consider the impact of Lossy Communication (LC), which is very common in the real world, on feature sharing. In this paper, we explore the effects of LC on Cooperative Perception and propose a novel approach to mitigate these effects. Our approach includes an LC-aware Repair Network (LCRN) and a V2V Attention Module (V2VAM) with intra-vehicle attention and uncertainty-aware inter-vehicle attention. We demonstrate the effectiveness of our approach on the public OPV2V dataset (a digital-twin simulated dataset) using point cloud-based 3D object detection. Our results show that our approach improves detection performance under lossy V2V communication. Specifically, our proposed method achieves a significant improvement in Average Precision compared to the state-of-the-art cooperative perception algorithms, which proves the capability of our approach to effectively mitigate the negative impact of LC and enhance the interaction between the ego vehicle and other vehicles. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jinlong17/V2VLC</uri>

Optimizing Resource Allocation With High-Reliability Constraint for Multicasting Automotive Messages in 5G NR C-V2X Networks

Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3 rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5 G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update, and coordinated driving. These capabilities rely on the provision of low latency and high reliability from 5 G NR. Among them, a basic demand is broadcasting or multicasting environment update messages, such as cooperative perception data, with high reliability and low latency from a Road Side Unit (RSU) or a base station (BS). In other words, broadcasting multiple types of automotive messages with high reliability and low latency is one of the key issues in 5 G NR C-V2X. In this work, we consider how to select Modulation and Coding Scheme (MCS), RSU/BS, Forward Error Correction (FEC) code rate, to maximize the system utility, which is a function of message delivery reliability. We formulate the optimization problem as a nonlinear integer programming problem. Since the optimization problem is NP-hard, we propose an approximation algorithm, referred to as the Hyperbolic Successive Convex Approximation (HSCA) algorithm, which uses the successive convex approximation to find the optimal solution. In our simulations, we compare the performance of HSCA with those of three algorithms respectively, including the baseline algorithm, the heuristic algorithm, and the optimal solution. Our simulation results show that HSCA outperforms the baseline and the heuristic algorithms and is very competitive to the optimal solution.

Confidence Evaluation for Machine Learning Schemes in Vehicular Sensor Networks

In this paper, we study a cooperative perception scheme in a vehicular sensor network, attempting to fuse the semantic information provided by different sensors at multiple vehicles, so as to expand the vehicle’s perception range, eliminate blind spots, improve the ability to handle environmental interference and enhance the accuracy and robustness of the perception results. The key to guide the fusion process is the evaluation of the confidence levels of the outputs provided by various machine learning schemes implemented at individual sensors in the vehicular sensor network. We first propose an evaluation criterion termed as Environmental Sensitivity (ES), which is used to measure the sensitivity of the network to environmental changes. Based on the ES, we further evaluate the confidence of the perception output of neural networks and quantify the confidence level considering the abnormal level of the input data, the general performance of the perception algorithm, the detection performance and the ES of the network. Semantic information fusion algorithm is then developed based upon the confidence levels. Experiment results are provided to validate the proposed fusion method in various scenarios.

WHICH IS MORE RELATED TO LITERACY SKILLS, COMPETITION OR COOPERATION? EVIDENCE FROM PISA 2018 RESULTS

Although previous research has demonstrated that school climate (leading to a climate of competition or cooperation) is related to student achievement, including their literacy skills, there is still insufficient evidence which of the two school climates is more related to literacy skills. Therefore, this study examined the relationship between the two sets of variables in the PISA 2018 Results data: students’ perceptions of school climate (PERSCHL) and literacy skills (LITERACY). The PERSCHL set consists of perceptions of the level of competition (PERSCOMP) and cooperation (PERSCOOP) in schools, while LITERACY set consists of mathematical literacy (MATH), reading (READ), and science (SCIE). The research was conducted to answer the questions: (1) is there a relationship between a student’s perception of competition and cooperation with students’ literacy skills? and (2) which one of the two PERSCHL variable sets has the most influence on students’ literacy skills? The level of perception in the PERSCHL set was estimated using the Graded Response Model (GRM), while literacy skills was calculated from the average of 10 Plausible Value (PV) that has been available in the data of PISA 2018 Results. The research hypothesis was tested using canonical correlation analysis. The results of the data analysis showed that the positive students’ perceptions of competitive and cooperative school climate had a positive and significant association with literacy skills although with a small correlation index. Second, perceptions of competition have a higher relationship with literacy skills than perceptions of cooperation in schools but the difference between the two is small. Both competition and cooperation in schools still need to be developed to facilitate the improvement of student literacy skills individually and collectively.

Reviewing the Factors Affecting PISA Reading Skills by Using Random Forest and MARS Methods

The research aims to determine the factors affecting PISA 2018 reading skills using Random Forest and MARS methods and to compare their prediction abilities. This study used the information from 5713 students, 2838 (49.7%) male and 2875 (50.3%) female in the PISA 2018 Turkey. The analysis shows the MARS method performed better than the Random Forest method. The most significant factor affecting reading skills in Turkey is “the number of books in the house” in both methods. The variables the MARS method finds significant are “students' perception of difficulty, motivation for reading skills, father’s educational status, reading pleasure, bullying experience of the student, mother's educational status, attitude towards school, classical artifacts at home, supplementary school books at home, competition at school, competitive power, cooperation perception at school, reading frequency, self-efficacy, poetry books at home, anxiety about reading skills and teacher support.” However, the other variables had no relation to prediction. This study is expected to serve as an example of data mining application in educational research

Knowledge Evaluation of Oral Diseases and Perception of Cooperation with Dental Experts for Oral Care of Nurses in Intensive Care Units in Korea: A Preliminary Study

The aim of this study was to identify the status of education and knowledge concerning oral diseases for oral care as they relate to intensive care unit (ICU) nurses, as well as to investigate the perception of oral care education and practice, as led by dental experts. This study conducted a self-report survey consisting of 33 questions on education and knowledge about oral diseases, as well as perception of dental expert-led education and practice, targeting 240 nurses in the ICU. Finally, 227 questionnaires were analyzed, and 75.3% of the participants were staff nurses, and 41.4% were in the medical ICU. In the area of education and knowledge of major oral diseases, more than 50% of the respondents treating gingivitis, periodontitis, and dental caries did not complete dental education, and it was found that more than half of the respondents were unable to distinguish diseases of the mouth. It was recognized that more than half of nurses required dental expert-led education and practice. In this study, the education and knowledge of oral diseases of ICU nurses were found to be insufficient, and the need for the cooperation of dental experts was high. Therefore, collaboration to improve oral care practical guidelines for realistically applicable ICU patients will be needed.

Situational awareness in a creeping crisis: How the initial phases of the COVID‐19 pandemic were handled from a crisis management perspective in the Nursing Home Agency in Oslo

AbstractIn March 2020, the municipality of Oslo's Nursing Home Agency was hit by Norway's first COVID‐19 outbreak. Being responsible for a very vulnerable group, they had to deal with a situation never before encountered and of which they had very limited knowledge. In this study, we explored how situational awareness (SA) changed from a creeping to an urgent crisis. We undertook a case study of the Nursing Home Agency's top management during the initial period of the COVID‐19 pandemic (December 2019 through late March 2020). We conducted individual interviews with the management in charge of decisions. Thematic analysis yielded four main categories affecting SA: perception of event development, perception of available time, information, and cooperation and trust. We found that subjective experience of the geographical proximity of the crisis and subjective experience of time were essential in shaping SA. Perception of time was essential to the understanding of urgency, which was an important factor in reacting properly. Further, the perception of space was necessary for the crisis to be interpreted as critical. Time and space are objective factors but are perceived subjectively. Our model showed that the crisis must be perceived as urgent for proper actions to be decided upon.

A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services

Road infrastructure plays a critical role in the support and development of the Cooperative Intelligent Transport Systems (C-ITS) paradigm. Roadside Units (RSUs), equipped with vehicular communication capabilities, traffic radars, cameras, and other sensors, can provide a multitude of vehicular services and enhance the cooperative perception of vehicles on the road, leading to increased road safety and traffic efficiency. Moreover, the central C-ITS system responsible for overseeing the road traffic and infrastructure, such as the RSUs, needs an efficient way of collecting and disseminating important information to road users. Warnings of accidents or other dangers, and other types of vehicular services such as Electronic Toll Collection (ETC), are examples of the types of information that the central C-ITS system is responsible for disseminating. To remedy these issues, we present the design of an implemented roadside and cloud architecture for the support of C-ITS services. With the main objectives of managing Vehicle-to-Everything (V2X) communication units and network messages of a public authority or motorway operator acting as a central C-ITS system, the proposed architecture was developed for different mobility testbeds in Portugal, under the scope of the STEROID research project and the pan-European Connected Roads (C-Roads) initiative. RSUs, equipped with ETSI ITS-G5 communications, are deployed with a cellular link or fiber optics connection for remote control and configuration. These are connected to a cloud Message Queuing Telemetry Transport (MQTT) broker where communication is based on a geographical tiling scheme, which allows the selection of the appropriate coverage areas for the dissemination of C-ITS messages. The architecture is deployed in the field, on several Portuguese motorways, where road traffic and infrastructure are monitored through a C-ITS platform with visualization and event reporting capabilities. The provided architecture is independent of the underlying communication technology and can be easily adapted in the future to support Cellular-V2X (PC5 interface) or 5G RSUs. Performance results of the deployed architecture are provided.