Journal articles on the topic 'Connected automated vehicles (CAV)'
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Chen, Bo, Darrell Robinette, Mahdi Shahbakhti, Kuilin Zhang, Jeff Naber, Jeremy Worm, Christopher Pinnow, and Christopher Morgan. "Connected Vehicles and Powertrain Optimization." Mechanical Engineering 139, no. 09 (September 1, 2017): S12—S18. http://dx.doi.org/10.1115/1.2017-sep-5.
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This article discusses the basic concept of connected and automated vehicles (CAV) technology. The common methods to improve fuel economy are also introduced. The effects of connectivity on vehicle/powertrain control and optimization are also elaborated. The Michigan Tech NEXTCAR project is also presented to provide a more detailed view of predictive vehicle/powertrain control enabled by CAV technologies. The U.S. Department of transportation (DOT) and other federal/state funding agencies have supported research and pilot deployment efforts to develop crosscutting CV technologies and evaluate the effectiveness of CV technologies in real-world transportation systems. The concurrent development of connected and automated vehicle technologies is anticipated to provide synergistic benefits to improve traffic safety, mobility, and energy efficiency. It is observed that increased CAV technologies are being deployed in real-world transportation systems.
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Shi, Yunpeng, Qing He, and Zhitong Huang. "Capacity Analysis and Cooperative Lane Changing for Connected and Automated Vehicles: Entropy-Based Assessment Method." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (April 28, 2019): 485–98. http://dx.doi.org/10.1177/0361198119843474.
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Connected and automated vehicles (CAVs) are poised to transform how we manage and control the existing traffic. CAVs can provide accurate distance sensing and adaptive cruise control which make shorter headway possible, and will eventually increase the roadway throughput or capacity. The vehicle-to-vehicle (V2V) communication technology equipment on CAVs allows vehicles to exchange information and form platoons more efficiently. This paper uses the intelligent driver model (IDM) as the behavior model to simulate CAVs in mixed traffic conditions with both CAVs and human-driven vehicles (HDVs) under different CAV penetration rates. A cooperative CAV lane-changing model is introduced to build more CAV platoons. The model develops two lane-changing algorithms. Partial CAV lane change (PAL) is applied at low CAV percentages, whereas full CAV lane change (FAL) is used at high CAV percentages. In addition, block entropy is employed as a performance measure for lane-changing results. The simulation experiments show that capacity will increase as the CAV percentage grows, and the peak growth rates occur in medium CAV percentage between 40% and 70%. The cooperative CAV lane-changing algorithm is found to decrease HDV–CAV conflicts remarkably by 37% as well as to marginally increase capacity by 2.5% under all CAV percentages. The simulation performance suggests that the threshold of CAV penetration rate for switching PAL to FAL is approximately 55%. Furthermore, it is demonstrated that block entropy can measure CAV lane-changing performance efficiently and represent capacity changes to some extent.
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Do, Wooseok, Omid M. Rouhani, and Luis Miranda-Moreno. "Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review." Journal of Advanced Transportation 2019 (June 26, 2019): 1–14. http://dx.doi.org/10.1155/2019/9343705.
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This study provides a literature review of the simulation-based connected and automated intelligent-vehicle studies. Media and car-manufacturing companies predict that connected and automated vehicles (CAVs) would be available in the near future. However, society and transportation systems might not be completely ready for their implementation in various aspects, e.g., public acceptance, technology, infrastructure, and/or policy. Since the empirical field data for CAVs are not available at present, many researchers develop micro or macro simulation models to evaluate the CAV impacts. This study classifies the most commonly used intelligent-vehicle types into four categories (i.e., adaptive cruise control, ACC; cooperative adaptive cruise control, CACC; automated vehicle, AV; CAV) and summarizes the intelligent-vehicle car-following models (i.e., Intelligent Driver Model, IDM; MICroscopic Model for Simulation of Intelligent Cruise Control, MIXIC). The review results offer new insights for future intelligent-vehicle analyses: (i) the increase in the market-penetration rate of intelligent vehicles has a significant impact on traffic flow conditions; (ii) without vehicle connections, such as the ACC vehicles, the roadway-capacity increase would be marginal; (iii) none of the parameters in the AV or CAV models is calibrated by the actual field data; (iv) both longitudinal and lateral movements of intelligent vehicles can reduce energy consumption and environmental costs compared to human-driven vehicles; (v) research gap exists in studying the car-following models for newly developed intelligent vehicles; and (vi) the estimated impacts are not converted into a unified metric (i.e., welfare economic impact on users or society) which is essential to evaluate intelligent vehicles from an overall societal perspective.
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Auld, Joshua, Vadim Sokolov, and Thomas S. Stephens. "Analysis of the Effects of Connected–Automated Vehicle Technologies on Travel Demand." Transportation Research Record: Journal of the Transportation Research Board 2625, no. 1 (January 2017): 1–8. http://dx.doi.org/10.3141/2625-01.
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Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single intersection, or along a corridor. However, travel demand and traffic flow constitute a complex, adaptive, nonlinear system. Therefore, in this study, an advanced transportation systems simulation model—POLARIS—was used. POLARIS includes cosimulation of travel behavior and traffic flow to study the potential effects of several CAV technologies at the regional level. Various technology penetration levels and changes in travel time sensitivity have been analyzed to determine a potential range of effects on vehicle miles traveled from various CAV technologies.
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Ban, Xuegang (Jeff), Diange Yang, Junmin Wang, and Samer Hamdar. "Editorial: Connected and automated vehicles (CAV) based traffic-vehicle control." Transportation Research Part C: Emerging Technologies 112 (March 2020): 116–19. http://dx.doi.org/10.1016/j.trc.2020.01.011.
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Harrison, Gillian, Simon P. Shepherd, and Haibo Chen. "Modelling Uptake Sensitivities of Connected and Automated Vehicle Technologies." International Journal of System Dynamics Applications 10, no. 2 (April 2021): 88–106. http://dx.doi.org/10.4018/ijsda.2021040106.
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Connected and automated vehicle (CAV) technologies and services are rapidly developing and have the potential to revolutionise the transport systems. However, like many innovations, the uptake pathways are uncertain. The focus of this article is on improving understanding of factors that may affect the uptake of highly and fully automated vehicles, with a particular interest in the role of the internet of things (IoT). Using system dynamic modelling, sensitivity testing towards vehicle attributes (e.g., comfort, safety, familiarity) is carried out and scenarios were developed to explore how CAV uptake can vary under different conditions based around the quality of IoT provision. Utility and poor IoT are found to have the biggest influence. Attention is then given to CAV ‘services' that are characterized by the attributes explored earlier in the paper, and it is found that they could contribute to a 20% increase in market share.
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Ma, Jiaqi, Fang Zhou, Zhitong Huang, Christopher L. Melson, Rachel James, and Xiaoxiao Zhang. "Hardware-in-the-Loop Testing of Connected and Automated Vehicle Applications: A Use Case for Queue-Aware Signalized Intersection Approach and Departure." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 22 (September 9, 2018): 36–46. http://dx.doi.org/10.1177/0361198118793001.
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Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods. HIL test environments enable physical test vehicles to interact with virtual vehicles from traffic simulation models, providing an evaluation environment that can replicate deployment conditions at early stages of CAV technology implementation without incurring excessive costs related to large field tests. In this study, a HIL testing system for vehicle-to-infrastructure (V2I) CAV applications is developed. The involved software and hardware includes a physical CAV controlled in real time, a traffic signal controller, communication devices, and a traffic simulator (VISSIM). Such HIL systems increase validity by considering the physical vehicle’s trajectories—which are constrained by real-world factors such as GPS accuracy, communication delay, and vehicle dynamics—in a simulated traffic environment. The developed HIL system is applied to test a representative early deployment CAV application: queue-aware signalized intersection approach and departure (Q-SIAD). The Q-SIAD algorithm generates recommended speed profiles based on the vehicle’s status, signal phase and timing (SPaT), downstream queue length, and system constraints and parameters (e.g., maximum acceleration and deceleration). The algorithm also considers the status of other vehicles in designing the speed profiles. The experiment successfully demonstrated this functionality with one test CAV driving through one intersection controlled by a fixed-timing traffic signal under various simulated traffic conditions.
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Fang, Xuan, Hexuan Li, Tamás Tettamanti, Arno Eichberger, and Martin Fellendorf. "Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario." Energies 15, no. 6 (March 9, 2022): 2008. http://dx.doi.org/10.3390/en15062008.
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There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay.
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Wang, Faan, Liwei Xu, Xianjian Jin, Guodong Yin, and Ying Liu. "A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion." Mathematical Problems in Engineering 2022 (January 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/5340693.
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The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%.
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Kavas-Torris, Ozgenur, Sukru Yaren Gelbal, Mustafa Ridvan Cantas, Bilin Aksun Guvenc, and Levent Guvenc. "V2X Communication between Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs)." Sensors 22, no. 22 (November 18, 2022): 8941. http://dx.doi.org/10.3390/s22228941.
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Connectivity between ground vehicles can be utilized and expanded to include aerial vehicles for coordinated missions. Using Vehicle-to-Everything (V2X) communication technologies, a communication link can be established between Connected and Autonomous vehicles (CAVs) and Unmanned Aerial vehicles (UAVs). Hardware implementation and testing of a ground-to-air communication link are crucial for real-life applications. In this paper, the V2X communication and coordinated mission of a CAV & UAV are presented. Four methods were utilized to establish communication between the hardware and software components, namely Dedicated Short Range communication (DSRC), User Datagram Protocol (UDP), 4G internet-based WebSocket and Transmission Control Protocol (TCP). These communication links were used together for a real-life use case scenario called Quick Clear demonstration. In this scenario, the first aim was to send the accident location information from the CAV to the UAV through DSRC communication. On the UAV side, the wired connection between the DSRC modem and Raspberry Pi companion computer was established through UDP to get the accident location from CAV to the companion computer. Raspberry Pi first connected to a traffic contingency management system (CMP) through TCP to send CAV and UAV location, as well as the accident location, information to the CMP. Raspberry Pi also utilized WebSocket communication to connect to a web server to send photos that were taken by the camera that was mounted on the UAV. The Quick Clear demonstration scenario was tested for both a stationary test and dynamic flight cases. The latency results show satisfactory performance in the data transfer speed between test components with UDP having the least latency. The package drop percentage analysis shows that the DSRC communication showed the best performance among the four methods studied here. All in all, the outcome of this experimentation study shows that this communication structure can be utilized for real-life scenarios for successful implementation.
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Wang, Ting, Meiting Tu, Hao Lyu, Ye Li, Olivier Orfila, Guojian Zou, and Dominique Gruyer. "Impact Evaluation of Cyberattacks on Connected and Automated Vehicles in Mixed Traffic Flow and Its Resilient and Robust Control Strategy." Sensors 23, no. 1 (December 21, 2022): 74. http://dx.doi.org/10.3390/s23010074.
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Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV.
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Huang, Chao, Haiping Du, Wanzhong Zhao, Yifan Zhao, Fuwu Yan, and Chen Lv. "Advanced Sensing and Control for Connected and Automated Vehicles." Sensors 22, no. 4 (February 16, 2022): 1538. http://dx.doi.org/10.3390/s22041538.
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In recent years, connected and automated vehicles (CAV) have been a transformative technology that is expected to reduce emissions and change and improve the safety and efficiency of the mobilities [...]
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Yin, Biao, Monica Menendez, and Kaidi Yang. "Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment." Sustainability 13, no. 3 (January 22, 2021): 1135. http://dx.doi.org/10.3390/su13031135.
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Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios.
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Qu, Dayi, Shaojie Wang, Haomin Liu, and Yiming Meng. "A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles." Sustainability 14, no. 12 (June 9, 2022): 7045. http://dx.doi.org/10.3390/su14127045.
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Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R2. The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.
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Huang, Chao, Yafei Wang, Peng Hang, Zhiqiang Zuo, and Bo Leng. "Advanced Sensing and Safety Control for Connected and Automated Vehicles." Sensors 23, no. 2 (January 16, 2023): 1037. http://dx.doi.org/10.3390/s23021037.
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Sobanjo, John O. "Civil Infrastructure Management Models for the Connected and Automated Vehicles Technology." Infrastructures 4, no. 3 (August 7, 2019): 49. http://dx.doi.org/10.3390/infrastructures4030049.
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The new concept of Connected and Automated Vehicles (CAVs) necessitates a need to review the approach of managing the existing civil infrastructure system (highways, bridges, sign structures, etc.). This paper provides a basic introduction to the CAV concept, assesses the infrastructure requirements for CAVs, and identifies the appropriateness of the existing infrastructure, and needs, in terms of the condition assessment and deterioration modeling. With focus on the Vehicle-to-Infrastructure (V2I) requirements for CAVs, the main elements required on the infrastructure are the Roadside Units (RSUs), which are primarily for communication; they are similar to non-structural transportation assets, such as traffic signals, signs, etc. The ongoing pertinent efforts of agencies and the private industry are reviewed, including the V2I Deployment Coalition (American Association of State Transportation Officials (AASHTO), the Institute of Transportation Engineers (ITE), and the Intelligent Transportation Society of America (ITS America)). Current methods of transportation asset management, particularly, of non-structural elements, are also reviewed. Two reliability-based models were developed and demonstrated for the deterioration of RSUs, including the age replacement model, and a combined survivor function considering the vulnerability of the CAV elements to natural hazards, such as the hurricanes. The paper also discusses the implications of the CAV technology on traffic models, particularly, how it affects user costs’ computations.
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Ding, Fan, Jiwan Jiang, Yang Zhou, Ran Yi, and Huachun Tan. "Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon." Sustainability 12, no. 23 (November 28, 2020): 9955. http://dx.doi.org/10.3390/su12239955.
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With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.
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Fossa, Fabio, Stefano Arrigoni, Giandomenico Caruso, Hafeez Husain Cholakkal, Pragyan Dahal, Matteo Matteucci, and Federico Cheli. "Operationalizing the Ethics of Connected and Automated Vehicles." International Journal of Technoethics 13, no. 1 (January 2022): 1–20. http://dx.doi.org/10.4018/ijt.291553.
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In response to the many social impacts of automated mobility, in September 2020 the European Commission published Ethics of Connected and Automated Vehicles, a report in which recommendations on road safety, privacy, fairness, explainability, and responsibility are drawn from a set of eight overarching principles. This paper presents the results of an interdisciplinary research where philosophers and engineers joined efforts to operationalize the guidelines advanced in the report. To this aim, we endorse a function-based working approach to support the implementation of values and recommendations into the design of automated vehicle technologies. Based on this, we develop methodological tools to tackle issues related to personal autonomy, explainability, and privacy as domains that most urgently require fine-grained guidance due to the associated ethical risks. Even though each tool still requires further inquiry, we believe that our work might already prove the productivity of the function-based approach and foster its adoption in the CAV scientific community.
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Li, Chenghao, Zhiqun Hu, Zhaoming Lu, and Xiangming Wen. "Cooperative Intersection with Misperception in Partially Connected and Automated Traffic." Sensors 21, no. 15 (July 23, 2021): 5003. http://dx.doi.org/10.3390/s21155003.
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The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.
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Andraško, Jozef, Ondrej Hamuľák, Matúš Mesarčík, Tanel Kerikmäe, and Aleksi Kajander. "Sustainable Data Governance for Cooperative, Connected and Automated Mobility in the European Union." Sustainability 13, no. 19 (September 24, 2021): 10610. http://dx.doi.org/10.3390/su131910610.
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The article focuses on the issue of data governance in connected vehicles through a novel analysis of current legal frameworks in the European Union. The analysis of relevant legislation, judicial decisions, and doctrines is supplemented by discussions relating to associated sustainability issues. Relevant notions of autonomous vehicles are analyzed, and a respective legal framework is introduced. Although fully automated vehicles are a matter for the future, the time to regulate is now. The European Union aims to create cooperative, connected, and automated mobility based on cooperation between different interconnected types of machinery. The essence of the system is data flow, as data governance in connected vehicles is one of the most intensively discussed themes nowadays. This triggers a need to analyze relevant legal frameworks in connection with fundamental rights and freedoms. Replacing human decision-making with artificial intelligence has the capacity to erode long-held and protected social and cultural values, such as the autonomy of individuals as has already been in evidence in legislation. Finally, the article deals with the issue of responsibility and liability of different actors involved in processing personal data according to the General Data Protection Regulation (GDPR) applied to the environment of connected and automated vehicle (CAV) smart infrastructure. Based on a definition and analysis of three model situations, we point out that in several cases of processing personal data within the CAV, it proves extremely demanding to determine the liable entity, due to the functional and relatively broad interpretation of the concept of joint controllers, in terms of the possibility of converging decisions on the purposes and means of processing within the vehicles discussed.
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Seraj, Mudasser, Jiangchen Li, and Zhijun Qiu. "Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making." Journal of Advanced Transportation 2018 (September 27, 2018): 1–15. http://dx.doi.org/10.1155/2018/7835010.
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Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.
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Khan, Mohsin, and Bhavna Arora. "Traffic Congestion Reduction and Accident Circumvention System via Incorporation of CAV and VANET." International Journal of Ambient Computing and Intelligence 12, no. 1 (January 2021): 53–72. http://dx.doi.org/10.4018/ijaci.2021010103.
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Connected automated vehicle (CAV) technology is the core for the new age vehicles in research phase to communicate with one another and assimilation of vehicular ad-hoc network (VANET) for the transference of data between vehicles at a quantified place and time. This manuscript is an enactment of the algorithms associated to the maintenance of secure distance amongst vehicles, lane shifting, and overtaking, which will diminish the occurrence of collisions and congestions especially phantom jams. Those implementations are centered over CAV and VANET technology for the interconnection of the vehicles and the data transmission. The data is associated to the aspects of a vehicle such as speed, position, acceleration, and acknowledgements, which acts as the fundamentals for the computation of variables. In accordance with the environment of a particular vehicle (i.e., its surrounding vehicles), real-time decisions are taken based on the real-time computation of the variables in a discrete system.
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Chen, Na, Meng Wang, Tom Alkim, and Bart van Arem. "A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays." Journal of Advanced Transportation 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/9852721.
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Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
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Adebisi, Adekunle, Yan Liu, Bastian Schroeder, Jiaqi Ma, Burak Cesme, Anxi Jia, and Abby Morgan. "Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Freeway Segments." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (July 28, 2020): 401–15. http://dx.doi.org/10.1177/0361198120934797.
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Connected and automated vehicles (CAVs) will undoubtedly transform many aspects of transportation systems in the future. In the meantime, transportation agencies must make investment and policy decisions to address the future needs of the transportation system. This research provides much-needed guidance for agencies about planning-level capacities in a CAV future and quantify Highway Capacity Manual (HCM) capacities as a function of CAV penetration rates and vehicle behaviors such as car-following, lane change, and merge. As a result of numerous uncertainties on CAV implementation policies, the study considers many scenarios including variations in parameters (including CAV gap/headway settings), roadway geometry, and traffic characteristics. More specifically, this study considers basic freeway, freeway merge, and freeway weaving segments in which various simulation scenarios are evaluated using two major CAV applications: cooperative adaptive cruise control and advanced merging. Data from microscopic traffic simulation are collected to develop capacity adjustment factors for CAVs. Results show that the existence of CAVs in the traffic stream can significantly enhance the roadway capacity (by as much as 35% to 40% under certain cases), not only on basic freeways but also on merge and weaving segments, as the CAV market penetration rate increases. The human driver behavior of baseline traffic also affects the capacity benefits, particularly at lower CAV market penetration rates. Finally, tables of capacity adjustment factors and corresponding regression models are developed for HCM implementation of the results of this study.
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Gao, Zhibo, Zhizhou Wu, Wei Hao, and Kejun Long. "Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks." Journal of Advanced Transportation 2020 (August 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/9401062.
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Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency.
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Li, Duo, and Peter Wagner. "A Novel Approach for Mixed Manual/Connected Automated Freeway Traffic Management." Sensors 20, no. 6 (March 22, 2020): 1757. http://dx.doi.org/10.3390/s20061757.
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Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates.
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Zhang, Yixin, Xumei Chen, and Lei Yu. "Evaluating the Emission and Energy Impacts of Automated Buses on Urban Expressways." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (October 16, 2020): 515–29. http://dx.doi.org/10.1177/0361198120954437.
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In recent years, a series of traffic problems have emerged with continuously increasing traffic. Connected and autonomous vehicles (CAV) technology is considered to be an effective way to relieve these problems. It is believed that buses, trucks, and other special vehicles could be among of the first application areas to promote the development of CAV technology. Because of their features of high emissions of pollutants and high energy consumption, the improvement of environmental benefits for such heavy vehicles as buses is the focus in this research. Therefore, this paper aims to evaluate the impact of automated buses on emissions and energy consumption on urban expressways. To achieve the research objectives in this paper, the established automated buses model is embedded into the simulation platform with VISSIM dynamic link library. Models are developed for emissions and energy consumption calculations based on vehicle-specific power to quantify the environmental impact of automated buses. Two improvement strategies: dedicated managed lane and dedicated bus lane, are designed. Finally, a VISSIM simulation platform based on the Fourth Ring Road in Beijing (Xueyuan Bridge to Haidian Bridge) is built to conduct case studies. The results show that CAV technology in buses can reduce exhaust emissions and save energy. Moreover, the managed lane strategy brings a significant reduction in the emissions and energy consumption of automated buses. These findings can be used for the development of automated bus operational strategies focused on environmental benefits.
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Zhong, Zijia, Joyoung Lee, and Liuhui Zhao. "Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicles in Mixed Traffic Conditions." Journal of Advanced Transportation 2021 (January 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/8816540.
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Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.
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Ahmed, Shofiq, Kakan Dey, and Ryan Fries. "Evaluation of Transportation System Resilience in the Presence of Connected and Automated Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 9 (May 9, 2019): 562–74. http://dx.doi.org/10.1177/0361198119848702.
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Large-scale natural disasters challenge the resilience of the surface transportation system. The objective of this research was to develop a resilience model of the surface transportation system with a mixed-traffic environment and considering varying Connected and Automated Vehicle (CAV) penetration scenarios. As deployment of CAVs is expected to improve traffic operations, a resilience model was developed in this research to evaluate the resilience performance of a transportation system with several CAV penetration levels (0%, 25%, 50%, 75%, and 100%) for a given budget and recovery time. The proposed resilience quantification model was applied on a roadway network considering several disaster scenarios. The network capacity in relation to trips at any phase of disaster was compared with the pre-disaster trips to determine the system resilience. The capacity variation and the travel time variation were also estimated. The analysis showed that the resilience of the transportation system improved with CAVs in relation to travel time and capacity improvement. Link travel times were significantly improved by higher CAV penetration rate. The findings also suggested that higher penetration of CAVs (i.e., 50% or more) increased the recovery costs. For example, the recovery costs needed for medium and large-scale disasters were 50% and 90% higher, respectively, compared with the recovery costs for a small-scale disaster. These higher costs were primarily for the repair and replacement of intelligent infrastructure required to support the operation of CAVs.
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Min, Haitao, Xiaoyong Xiong, Fang Yang, Weiyi Sun, Yuanbin Yu, and Pengyu Wang. "An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning." Machines 11, no. 2 (January 26, 2023): 168. http://dx.doi.org/10.3390/machines11020168.
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The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the vehicle trajectory is conducive to improving the energy efficiency of the vehicle. Therefore, an energy-efficient driving method for CAVs based on reinforcement learning is proposed in this paper. Firstly, a set of vehicle trajectory prediction models based on long and short-term memory (LSTM) neural networks are developed, which integrate driving intention prediction and lane change time prediction to improve the prediction accuracy of surrounding vehicle trajectories. Secondly, an energy-efficient driving model is built based on Proximity Policy Optimization (PPO) reinforcement learning. The model takes the current states and predicted trajectories of surrounding vehicles as input information, and outputs energy-saving control variables while taking into account various constraints, such as safety, comfort, and travel efficiency. Finally, the method is tested by simulation on the NGSIM dataset, and the results show that the proposed method can save energy consumption by 9–22%.
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Hamulák, Ondrej, Josef Andraško, and Matúš Mesarčík. "The digital development of the European Union: data governance aspects of cooperative, connected and automated mobility." IDP Revista de Internet Derecho y Política, no. 34 (December 13, 2021): 1–16. http://dx.doi.org/10.7238/idp.v0i34.387494.
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This article focuses on the issue of data governance in connected vehicles. Firstly, basic notions of autonomous vehicles are analyzed, and a legal framework is introduced. The European Union aims to create cooperative, connected, and automated mobility based on the cooperation of different inter-connected types of machinery. The essence of the system is data flow in connected vehicles, and the issue represents one of the heavily discussed themes in legal doctrine. Therefore, data governance is further discussed in the article. The final part of the article deals with the issue of responsibility and liability of different actors involved in the processing of personal data according to the General Data Protection Regulation applied to the environment of CAV smart infrastructure.
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Liu, Bo, Yanqing Cen, Zhihong Yao, Xianghui Song, Liu Hongben, and Huan Gao. "Combined Safety and Coordination of Connected Automated Vehicles in Merging Area with Featuring Optimal Merging Positions." Journal of Advanced Transportation 2022 (September 16, 2022): 1–14. http://dx.doi.org/10.1155/2022/2087510.
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Freeway on-ramp merging area is deemed to be typical bottlenecks section, which leads to low traffic efficiency, congestions, and frequent accidents. Most existing studies on merging for the connected and automated vehicles focus on merging at a single fixed merging point. However, the problem of coordination between merging vehicle and arterial traffic flow in the acceleration lane is ignored in the existing studies. This study proposes a merging model, which combined safety and coordination of CAVs with featuring optimal merging positions. The proposed model has two stages: one is analysis of merging velocity of the insertable gap and the other one is determining constraint condition of cooperative merging. The outputs of first stage are interval of merging speed and the mergeable range. The outputs of second stage are optional insertable gap and the corresponding driving scheme. Then, a traffic simulation experiment is designed to evaluate the proposed model. The simulation results show that the proposed model can effectively guarantee driving safety and make the merging process smoother with 28.7% reduction in travel time for the CAV merging. Furthermore, the proposed model does not sacrifice the interests of surrounding traffic to assist in CAV merging. The results indicate the promising potential of using the proposed methods can approximately get a fair use of road resources for each CAV.
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Chen, Liang, Yun Zhang, Kun Li, Qiaoru Li, and Qiang Zheng. "Car-following model of connected and autonomous vehicles considering both average headway and electronic throttle angle." Modern Physics Letters B 35, no. 15 (March 12, 2021): 2150257. http://dx.doi.org/10.1142/s0217984921502572.
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The connected and automated vehicle (CAV) is regarded as an effective way to improve traffic efficiency and safety, which can utilize vehicle-to-vehicle (V2V) communication technology to obtain real-time status information from multiple preceding vehicles. In view of the car-following characteristic of CAV in a V2V communications environment, an extended car-following model AHT-FVD is proposed which takes both average headway and electronic throttle angle difference into account. The stability of this model is examined via linear stability analysis. It is found that the proposed model has a larger stability region than both the full velocity difference (FVD) model and throttle-based FVD (T-FVD) model. Namely, this AHT-FVD model can effectively stabilize traffic flow and alleviate traffic congestion in theory. Moreover, a series of numerical simulations are carried out to explore how average headway together with electronic throttle angle difference influences the stability of traffic flow. Simulation results show that increasing either the average headway weight or the electronic throttle angle difference control signal coefficients can yield higher traffic flow stability. Simulation result is highly consistent with theoretical analysis.
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Bush, Brian, Laura Vimmerstedt, and Jeff Gonder. "Potential Energy Implications of Connected and Automated Vehicles: Exploring Key Leverage Points through Scenario Screening and Analysis." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 3, 2019): 84–94. http://dx.doi.org/10.1177/0361198119838840.
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Connected and automated vehicle (CAV) technologies could transform the transportation system over the coming decades, but face vehicle and systems engineering challenges, as well as technological, economic, demographic, and regulatory issues. The authors have developed a system dynamics model for generating, analyzing, and screening self-consistent CAV adoption scenarios. Results can support selection of scenarios for subsequent computationally intensive study using higher-resolution models. The potential for and barriers to large-scale adoption of CAVs have been analyzed using preliminary quantitative data and qualitative understandings of system relationships among stakeholders across the breadth of these issues. Although they are based on preliminary data, the results map possibilities for achieving different levels of CAV adoption and system-wide fuel use and demonstrate the interplay of behavioral parameters such as how consumers value their time versus financial parameters such as operating cost. By identifying the range of possibilities, estimating the associated energy and transportation service outcomes, and facilitating screening of scenarios for more detailed analysis, this work could inform transportation planners, researchers, and regulators.
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Wang, ShiHui, Min Zhao, DiHua Sun, and Xiaoyu Liu. "Merging Sequence Optimization Based on Reverse Auction Theory and Merging Strategy with Active Trajectory Adjustment of Heterogeneous Vehicles." Journal of Advanced Transportation 2022 (May 26, 2022): 1–20. http://dx.doi.org/10.1155/2022/3926976.
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This paper investigates the optimized merging sequence (MS) and on-ramp merging strategy in mixed traffic with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). To this end, a cooperative merging sequence optimization method is first proposed based on the reverse auction. In the method, a hybrid optimized computing structure is proposed to provide the foundation for connected and human-driving vehicles (CHVs) and connected and automated vehicles (CAVs) to obtain the MS more efficiently. And a cooperative merging strategy based on all cooperative merging vehicles under mixed traffic conditions is proposed. In particular, the downstream vehicles in the strategy can change their original velocities to actively participate in the cooperative merging process according to the merging requirements of on-ramp vehicles. And the vehicles in this strategy are all subject to state constraints to avoid the adverse effects of cooperative merging behavior on the following traffic on the main road. Results of numerical experiments illustrate that the merging sequence optimization method can reduce the time to obtain the optimal MS, and the increased computational efficiency is affected by CAV penetration. In addition, in mixed traffic conditions, the cooperative merging strategy can reduce fuel consumption and the time required for merging.
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Li, Ye, Yuntao Shi, Jaeyoung Lee, Chen Yuan, and Baojie Wang. "Safety Effects of Connected and Automated Vehicle-Based Variable Speed Limit Control near Freeway Bottlenecks considering Driver’s Heterogeneity." Journal of Advanced Transportation 2022 (January 30, 2022): 1–16. http://dx.doi.org/10.1155/2022/7996623.
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A control strategy using variable speed limit (VSL) is a proven solution to reduce freeway collision risks and improve safety. However, the heterogeneity of human drivers restricts the effectiveness of traditional VSL controls, which may be made up by recent advanced technologies of connected and automated vehicles (CAVs). This study aims to propose a CAV-based VSL control system to address the limitations caused by human drivers on VSL control’s effectiveness. First, the heterogeneity of human drivers is analyzed, and its impact on the safety performance of VSL is examined. Specifically, a microscopic simulation platform is established, and two vehicle dynamic models developed for CAVs and human-driven vehicles (HDVs) are incorporated into the simulation platform. Based on a widely utilized surrogate safety measurement, time-to-collision, its derivative metrics are applied to evaluate collision risks, and the total travel time is used to assess operational efficiency. Extensive simulations are conducted to examine the performance of the proposed CAV-VSL system. The results indicate the following: (1) the heterogeneity of human drivers negatively affects the performance of the VSL; (2) the performance of the proposed control system in a mixed flow can be improved by advanced wireless communication technology; (3) CAVs are able to implement the VSL control strategy effectively resulting in the proactive reduction of the heterogeneity.
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Bakibillah, Abu Saleh Md, Yi Feng Paw, Md Abdus Samad Kamal, Susilawati Susilawati, and Chee Pin Tan. "An Incentive Based Dynamic Ride-Sharing System for Smart Cities." Smart Cities 4, no. 2 (April 22, 2021): 532–47. http://dx.doi.org/10.3390/smartcities4020028.
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Connected and automated vehicle (CAV) technology, along with advanced traffic control systems, cannot ensure congestion-free traffic when the number of vehicles exceeds the road capacity. To address this problem, in this paper, we propose a dynamic ride-sharing system based on incentives (for both passengers and drivers) that incorporates travelers of similar routes and time schedules on short notice. The objective is to reduce the number of private vehicles on urban roads by utilizing the available seats properly. We develop a mobile-cloud architecture-based system that enables real-time ride-sharing. The effectiveness of the proposed system is evaluated through microscopic traffic simulation using Simulation of Urban Mobility (SUMO) considering the traffic flow behavior of a real smart city. Moreover, we develop a lab-scale experimental prototype in the form of Internet of Things (IoT) network. The simulation results show that the proposed system reduces fuel consumption, CO2 and CO emissions, and average waiting time of vehicles significantly, while increasing the vehicle’s average speed. Remarkably, it is found that only 2–10% ride-sharing can improve the overall traffic performance.
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Xiao, Xiao, Yunlong Zhang, Xiubin Bruce Wang, Shu Yang, and Tianyi Chen. "Hierarchical Longitudinal Control for Connected and Automated Vehicles in Mixed Traffic on a Signalized Arterial." Sustainability 13, no. 16 (August 7, 2021): 8852. http://dx.doi.org/10.3390/su13168852.
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This paper proposes a two-layer hierarchical longitudinal control approach that optimizes travel time and trajectories along multiple intersections on an arterial under mixed traffic of connected automated vehicles (CAV) and human-driven vehicles (HV). The upper layer optimizes the travel time in an optimization loop, and the lower layer formulates a longitudinal controller to optimize the movement of CAVs in each block of an urban arterial by applying optimal control. Four scenarios are considered for optimal control based on the physical constraints of vehicles and the relationship between estimated arrival times and traffic signal timing. In each scenario, the estimated minimized travel time is systematically obtained from the upper layer. As the results indicate, the proposed method significantly improves the mobility of the signalized corridor with mixed traffic by minimizing stops and smoothing trajectories, and the travel time reduction is up to 29.33% compared to the baseline when no control is applied.
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Ahmed, Hafiz Usman, Ying Huang, Pan Lu, and Raj Bridgelall. "Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview." Smart Cities 5, no. 1 (March 17, 2022): 382–404. http://dx.doi.org/10.3390/smartcities5010022.
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The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
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Wu, Wenjing, Renchao Sun, Anning Ni, Zhikang Liang, and Hongfei Jia. "Simulation and evaluation of speed and lane-changing advisory of CAVS at work zones in heterogeneous traffic flow." International Journal of Modern Physics B 34, no. 21 (August 20, 2020): 2050201. http://dx.doi.org/10.1142/s021797922050201x.
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Emerging connected autonomous vehicle (CAV) technologies provide an opportunity to the vehicle motion control to improve the traffic performance. This study simulated and evaluated the CAV-based speed and lane-changing (LC) control strategies at the expressway work zone in heterogeneous traffic flow. The control strategies of CAV are optimized by the multi-layer control structure based on model predictive control. The heterogeneous traffic flow composed of human-driven vehicles and CAVs is constructed based on cellular automata by the proposed Expected Distance-based Symmetric Two-lane Cellular Automate (ED-STCA) LC model and CAV car-following model. The six control strategies composed of variable speed limits (VSL), LC and their coordinated control strategies are experimented. The average travel time and throughput are selected to assess the advantages and disadvantages of each strategy under each combination of vehicles’ arrival rates and CAV mixed ratios. The numerical results show that: (i) the effect of the control strategy on the traffic is not obvious under free flow, and the control strategy may worsen the traffic under medium traffic. (ii) Early lane-changing control (ELC) is better than late lane-changing control (LLC) under medium traffic, and LLC is better under heavy traffic. (iii) [Formula: see text] is the best choice under heavy traffic and the mixed rate of CAVs is high. The simulation results obtained in the paper would provide some practical references for transportation agencies to manage the traffic in work zone under networking environment in the future.
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Raptis, George E., Christina Katsini, Christos Alexakos, Athanasios Kalogeras, and Dimitrios Serpanos. "CAVeCTIR: Matching Cyber Threat Intelligence Reports on Connected and Autonomous Vehicles Using Machine Learning." Applied Sciences 12, no. 22 (November 16, 2022): 11631. http://dx.doi.org/10.3390/app122211631.
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Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract malicious activities that jeopardize security and safety dimensions. The cybersecurity systems of CAVs detect such activities, collect and analyze related information during and after the activity, and use cyber threat intelligence (CTI) to organize this information. Considering that CTI collected from various malicious activities may share common characteristics, it is critical to provide the cybersecurity stakeholders with quick and automatic ways of analysis and interrelation. This aims to help them perform more accurate and effective forensic investigations. To this end, we present CAVeCTIR, a novel approach that finds similarities between CTI reports that describe malicious activities detected on CAVs. CAVeCTIR uses advanced machine learning techniques and provides a quick, automated, and effective solution for clustering similar malicious activities. We applied CAVeCTIR in a series of experiments investigating almost 3000 malicious activities in simulation, real-world, and hybrid CAV environments, covering seven critical cyber-attack scenarios. The results showed that the DBSCAN algorithm identified seven no-overlapping core clusters characterized by high density. The results indicated that cybersecurity stakeholders could take advantage of CAVeCTIR by adopting the same or similar methods to analyze newly detected malicious activity, speed up the attack attribution process, and perform a more accurate forensics investigation.
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Eziama, Elvin, Faroq Awin, Sabbir Ahmed, Luz Marina Santos-Jaimes, Akinyemi Pelumi, and Danilo Corral-De-Witt. "Detection and Identification of Malicious Cyber-Attacks in Connected and Automated Vehicles’ Real-Time Sensors." Applied Sciences 10, no. 21 (November 4, 2020): 7833. http://dx.doi.org/10.3390/app10217833.
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Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of 7.95%, 9%, 8.77% and 7.33%, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are 5%, 7.9%, 7.54% and 4.1% respectively.
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Raju, Narayana, and Haneen Farah. "Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles." Journal of Advanced Transportation 2021 (September 24, 2021): 1–29. http://dx.doi.org/10.1155/2021/2444363.
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Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.
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Jiang, Zhongtai, Dexin Yu, Huxing Zhou, Siliang Luan, and Xue Xing. "A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road." Sustainability 13, no. 17 (September 4, 2021): 9933. http://dx.doi.org/10.3390/su13179933.
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The phenomenon of stop-and-go traffic and its environmental impact has become a crucial issue that needs to be tackled, in terms of the junctions between freeway and urban road networks, which consist of freeway off-ramps, downstream intersections, and the junction section. The development of Connected and Automated Vehicles (CAVs) has provided promising solutions to tackle the difficulties that arise along intersections and freeway off-ramps separately. However, several problems still exist that need to be handled in terms of junction structure, including vehicle merging trajectory optimization, vehicle crossing trajectory optimization, and heterogeneous decision-making. In this paper, a two-stage CAV trajectory optimization strategy is presented to improve fuel economy and to reduce delays through a joint framework. The first stage considers an approach to determine travel time considering the different topological structures of each subarea to ensure maximum capacity. In the second stage, Pontryagin’s Minimum Principle (PMP) is employed to construct Hamiltonian equations to smooth vehicle trajectory under the requirements of vehicle dynamics and safety. Targeted methods are devised to avoid driving backwards and to ensure an optimal vehicle gap, which make up for the shortcomings of the PMP theory. Finally, simulation experiments are designed to verify the effectiveness of the proposed strategy. The evaluation results show that our strategy could effectively militate travel delays and fuel consumption.
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Utriainen, Roni, and Markus Pöllänen. "The Needed Features of Connected and Automated Vehicles to Prevent Passenger Car Crashes Caused by Driving Errors." Future Transportation 1, no. 2 (September 3, 2021): 370–86. http://dx.doi.org/10.3390/futuretransp1020021.
Full textAbstract:
Connected and automated vehicles (CAVs) can enhance traffic safety considerably. However, as CAVs are currently under development, the safety impact cannot be assessed directly. In this study, driver-managed passenger car crashes with fatalities in Finland were investigated qualitatively to evaluate the needed features of the CAVs to avoid these crashes. The focus was on single-car crashes and collisions between passenger cars, in which the immediate risk factor was a driving error (n = 48). Most of the analysed crashes (33 of 48) were due to loss of control with typically adverse weather or road conditions. To avoid these crashes, a CAV should be able to adjust its speed according to the conditions. In 13 of 48 crashes, the car was under control prior to the crash. A reliable capability to recognize other road users is an important CAV feature, because observational errors were common in these cases. In addition, communication between the vehicles could assist in avoiding intersection crashes and crashes caused by a sudden change in weather conditions. This study increases knowledge on crashes related to driving errors and the needed features of CAVs to avoid these crashes. In particular, CAVs’ feature to adjust the speed is important, because cases of loss of control in adverse weather or road conditions were typical events.
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Wang, Qinzheng, Xianfeng (Terry) Yang, Zhitong Huang, and Yun Yuan. "Multi-Vehicle Trajectory Design During Cooperative Adaptive Cruise Control Platoon Formation." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 4 (March 20, 2020): 30–41. http://dx.doi.org/10.1177/0361198120913290.
Full textAbstract:
Cooperative adaptive cruise control (CACC) organizes connected and automated vehicles (CAVs) in platoons to improve traffic flow and reduce fuel consumption. Platoon formation involves a very complex process, however, because lateral and longitudinal misbehavior of CAVs results in greater fuel consumption and risk of collision. This study aims to design optimal vehicle trajectories of CAVs during CACC platoon formation. First, a basic scenario and a destination-based protocol are described to determine vehicle sequence in the platoon. A space-time lattice based model is then formulated to construct vehicle trajectories considering boundary conditions of kinematic limits, vehicle-following safety, and lane-changing rules. The objective is to optimize the vehicle sequence and fuel consumption simultaneously. A two-phase algorithm is proposed to solve this model, where the first phase is a heuristic algorithm that determines vehicle sequence and in the second phase dynamic programming is adapted to optimize fuel consumption based on the determined sequence. To evaluate the effectiveness of the proposed model in designing CAV trajectories, extensive experimental tests have been conducted in this study. Results show that the proposed model and algorithm can effectively optimize CAV sequence in the platoon based on their destinations. After optimization, CAV fuel consumption was reduced by 42%, 46%, and 43%, respectively, in three different tested scenarios.
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Chavoshi, Kimia, Alexander Genser, and Anastasios Kouvelas. "A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections." Electronics 10, no. 14 (July 16, 2021): 1702. http://dx.doi.org/10.3390/electronics10141702.
Full textAbstract:
This paper studies the planning of conflict-free and efficient crossings of antagonistic vehicles’ movements at lightless intersections. A fully automated infrastructure environment is considered, where all vehicles that enter the intersection area are connected and automated (CAVs), i.e., they are equipped with advanced communication and automation technologies. In such a futuristic environment, traffic lights that regulate the right-of-way of different traffic streams are obsolete because of vehicle communication capabilities. The connectivity is utilized to derive vehicle trajectories such that a safe and efficient crossing of lightless intersections is possible. So far, published studies lack the application to complex intersection layouts. To fill this gap, we introduce a control method for CAV pairing allowing for the safe, collision-free crossing of the intersecting area and optimize traffic conditions, i.e., total delays of the system. Simulation results demonstrate the feasibility and applicability of the presented approach, given that all the technical specifications (e.g., communications, velocity actuators) are present. Finally, we conduct a sensitivity analysis for the algorithm’s main parameters, which provides practical insights for the studied experimental scenarios and other existing algorithms in the literature that tackle this problem.
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Shan, Mao, Karan Narula, Yung Fei Wong, Stewart Worrall, Malik Khan, Paul Alexander, and Eduardo Nebot. "Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations." Sensors 21, no. 1 (December 30, 2020): 200. http://dx.doi.org/10.3390/s21010200.
Full textAbstract:
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). The main contribution of the work lies in investigating and demonstrating 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 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.
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Xiu, Wei-Jie, Li Wang, Meng-Yang Guo, Li-Li Zhang, and Qi Zhao. "Investigation of Control Method for Connected-Automated Vehicle to Multiplex Dedicated Bus Lane." Journal of Advanced Transportation 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/2470738.
Full textAbstract:
Dedicated bus lanes (DBLs) have been widely utilized to ensure public transport priority. To improve overall road efficiency, various control methods of multiplexing DBL are developed and discussed. In this study, we focus on the control method which is based on the connected-automated vehicle (CAV) technology, and the proposed method is validated by using microscopic traffic simulation. The simulation results show that two proposed control methods of multiplexing DBL can reduce the average delay and the average number of stops and increase the travel speed. In comparison, the real-time control method based on the CAV technology offers better effects than the improved signal light control method.
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Guo, Xiaoyu, Yongxin Peng, Sruthi Ashraf, and Mark W. Burris. "Performance Analyses of Information-Based Managed Lane Choice Decisions in a Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (August 20, 2020): 120–33. http://dx.doi.org/10.1177/0361198120940311.
Full textAbstract:
Connected vehicle (CV) technology can connect, communicate, and share information between vehicles, infrastructure, and other traffic management systems. Recent research has examined and promoted CV and connected automated vehicle (CAV) technology on managed lane systems to increase capacity and reduce congestion, as managed lane systems could be equipped with advanced infrastructure relatively quickly. However, the effect on travel considering, information-based managed lane choice decisions in a CV environment is not clear. Therefore, this research analyzed the potential effects on a managed lane system with connected vehicles considering several travel behavior elements, including drivers’ willingness to reroute and their choice of managed lanes based on individual travel time savings. This study analyzed the potential effects on a managed lane system by assigning different market penetration rates (0%, 10%, 50%, 100%) of CVs and informing CV drivers about travel time savings for a 10-mi stretch at 5-min intervals. How the traffic performance measurements (i.e., throughput, travel time saving, average speed and average travel time) vary under different market penetration rates of CVs is then investigated. Two major conclusions are reached: (i) although information exchange was assumed to be instantaneous between vehicles and the system, there existed a response time (or time delay) in the macroscopic traffic reflection; (ii) managed lane use may decrease, when travel time information becomes available, since drivers perceive they are saving more travel time than they actually do save.