Academic literature on the topic 'Connected Vehicles (CVs)'
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Journal articles on the topic "Connected Vehicles (CVs)"
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Liang, Xiao (Joyce), S. Ilgin Guler, and Vikash V. Gayah. "Signal Timing Optimization with Connected Vehicle Technology: Platooning to Improve Computational Efficiency." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (July 16, 2018): 81–92. http://dx.doi.org/10.1177/0361198118786842.
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This paper develops a real-time traffic signal optimization algorithm in the presence of connected and autonomous vehicles (CAVs). The proposed algorithm leverages information from connected vehicles (CVs) arriving at an intersection to identify naturally occurring platoons that consist of both CVs and non-CVs. Signal timings are then selected to optimize the sequence at which these platoons are allowed to discharge through the intersection to minimize total vehicle delay. Longitudinal trajectory guidance that explicitly accounts for vehicle acceleration and deceleration behavior is provided to the lead autonomous vehicle (AV) in any platoon to minimize the total number of stopping maneuvers performed by all vehicles. Simulation tests reveal that the proposed platoon-based algorithm provides superior computational savings (over 95%) compared with a previously developed algorithm that focuses on optimizing departure sequences of individual vehicles, with negligible changes in operational performance. The computational savings allow the platoon-based algorithm to accommodate intersections with four multi-lane approaches and left turns, whereas large computational costs limited the previous vehicle-based algorithm to only two single-lane approaches without conflicting left turns. Additional simulation tests of the platoon-based algorithm on these more realistic intersection configurations show that intersection performance increases as the penetration rate of CAVs in the vehicle fleet increases. However, the marginal benefits decrease rapidly after the fleet is composed of 40% CAVs.
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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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Du, Mengxiao, Shiyao Yang, and Qun Chen. "Impacts of vehicle-to-infrastructure communication on traffic flows with mixed connected vehicles and human-driven vehicles." International Journal of Modern Physics B 35, no. 06 (March 10, 2021): 2150091. http://dx.doi.org/10.1142/s0217979221500910.
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This paper explored the impacts of vehicle-to-infrastructure (V2I) communication on the mixed traffic flow consisting of connected vehicles (CVs) and human-driven vehicles (HVs). We developed a cellular automaton model for mixed flow at the signalized intersection. In addition to considering the motion characteristics of CVs and the influence of HVs on the motion behavior of CVs, the model also considered the influence of signal lights. CVs determine their velocities via V2I communication in order to pass the signal light with less delay and avoid stopping. Through simulations, we found that the presence, frequency and range of V2I communication all make a difference in the mixed flow. Also, 1-Hz communication reduces the number of vehicles within 300 m before the red light from 36 to 26, and the 10-Hz communication reduces one more; 1-Hz communication increases the number of accelerations, but when the frequency increases to 10 Hz, the number of accelerations decreases to the same value as without V2I communication, but the value of number of accelerations increases monotonously with the frequency; traffic delay decreases and capacity increases as the frequency increases. However, as the communication range increases, except that the number of accelerations first decreases and then increases, other traffic characteristics remain unchanged. The number of accelerations reaches a minimum at about 500 m.
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Sahebi, Sina, and Habibollah Nassiri. "Assessing Public Acceptance of Connected Vehicle Systems in a New Scheme of Usage-Based Insurance." Transportation Research Record: Journal of the Transportation Research Board 2625, no. 1 (January 2017): 62–69. http://dx.doi.org/10.3141/2625-07.
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The consequences of road accidents are significant for drivers and society. The connected vehicle system (CVS) is a promising technology that can improve road safety by warning drivers of traffic hazards. Broad implementation of the CVS could mitigate the harmful consequences of road accidents. Widespread implementation requires schemes that can promote the pervasive adoption of the system by drivers. This study proposes the innovative idea of implementing the CVS in usage-based insurance (UBI) as a measurement probe and modeling drivers’ acceptance of the new UBI scheme. This study developed a random effect logit model demonstrating that the drivers of cheaper vehicles and middle-age drivers (30 to 60) were more inclined to accept the new UBI scheme and use the CVS in their vehicles. Risk-averse drivers were more likely to accept the scheme than were other drivers. The pervasive implementation of the CVS can be costly, but it can improve traffic safety. Because of the two-way spectrum of the costs and benefits of the CVS, providing comprehensive projects to develop the system is important for CVS investors and developers.
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Mohammadi, Roozbeh, and Claudio Roncoli. "Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment." Sensors 21, no. 24 (December 19, 2021): 8477. http://dx.doi.org/10.3390/s21248477.
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Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.
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Wang, Zhendong, Haoran Wei, Jianda Wang, Xiaoming Zeng, and Yuchao Chang. "Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey." Sustainability 14, no. 19 (September 29, 2022): 12409. http://dx.doi.org/10.3390/su141912409.
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Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer traffic. Artificial Intelligence (AI)-based CAV solutions play significant roles in sustainable cities. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on the driver; increased connectivity increases the exposures of vehicles’ vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles’ vulnerabilities to negatively impact the performance and operations of CAVs. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAVs, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAVs is elaborated from both viewpoints of intra-vehicle systems and inter-vehicle systems. We pointed out that securing the perception and operations of CAVs would be the top requirement to enable CAVs to be applied safely and reliably in practice. Additionally, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAVs.
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Yu, Bin, Miyi Wu, Shuyi Wang, and Wen Zhou. "Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment." International Journal of Environmental Research and Public Health 16, no. 22 (November 8, 2019): 4373. http://dx.doi.org/10.3390/ijerph16224373.
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Connected vehicles (CVs) exchange a variety of information instantly with surrounding vehicles and traffic facilities, which could smooth traffic flow significantly. The objective of this paper is to analyze the effect of CVs on running speed. This study compared the delay time, travel time, and running speed in the normal and the connected states, respectively, through VISSIM (a traffic simulation software developed by PTV company in German). The optimization speed model was established to simulate the decision-makings of CVs in MATLAB, considering the parameters of vehicle distance, average speed, and acceleration, etc. After the simulation, the vehicle information including speed, travel time, and delay time under the normal and the connected states were compared and evaluated, and the influence of different CV rates on the results was analyzed. In a two-lane arterial road, running speed in the connected state increase by 4 km/h, and the total travel time and delay time decrease by 5.34% and 16.76%, respectively, compared to those in the normal state. The optimal CV market penetration rate related to running speed and delay time is 60%. This simulation-based study applies user-defined lane change and lateral behavior rules, and takes different CV rates into consideration, which is more reliable and practical to estimate the impact of CV on road traffic characteristics.
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Mishra, Rohit, Yiqi Zhang, Fenglong Ma, and Anlong Li. "The Prediction of Collisions in Connected Vehicle Systems with A Long Short-Term Memory Model." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 775–79. http://dx.doi.org/10.1177/1071181320641178.
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The advances in connected vehicle systems (CVS) allow vehicles to communicate with each other and with infrastructures via wireless communication networks. This technology enables vehicles to detect potential hazards on the road, generate warnings, and assist the driver in taking preventive actions. To date, few mathematical models have been developed to predict the collision rates in connected vehicle systems. In this work, a Long Short-Term Memory model (LSTM) using time-series data of human drivers was developed to predict the collision rates in CVS by quantifying warning parameters and hazard scenario features. The model was validated with the driving performance data before and after warnings from thirty-two drivers in a behavioral experiment. The results indicated the LSTM model showed a prediction accuracy of 74% higher than SVM and logistic regression models. The LSTM model showed the potential to help optimize the warning algorithm in the connected vehicle systems to improve driver safety.
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Park, Hyung Geun, Sunghoon Kim, and Taehyung Kim. "Traffic-Responsive Signal Control at Intersections Using Real-Time Data of Vehicles Connected via V2X Communication." Journal of Advanced Transportation 2023 (January 10, 2023): 1–18. http://dx.doi.org/10.1155/2023/4025210.
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The positive effect of traffic-responsive signal control can be assured when real-time traffic data is reliable, but data reliability may be an issue that depends on the number of probe vehicles equipped with navigation devices or smartphones. However, there is a high chance of improving reliability with the recent deployment of connected vehicles (CVs) that use the vehicle-to-everything (V2X) communication data. Therefore, this paper proposes a traffic signal control strategy that utilizes V2X communication data obtained from CV operations, which is called the capacity waste reduction (CWR) strategy. In this strategy, vehicle queues on each road lane as an intersection approaches are initially estimated using V2X data. Then, the signal control algorithm determines the duration of the green signal for the currently applied phase based on the estimated vehicle queues. Furthermore, the strategy includes an algorithm for active priority signal control for the vehicles of bus rapid transit systems. The efficiency of the provided control strategy is tested with the VISSIM microsimulation program at different levels of the market penetration rate (MPR) of CVs. Based on the results of the experiment, the proposed strategy shows positive effects in both decreasing travel delay and increasing traffic flow even at the low levels of MPR of CVs. The results of the proposed strategy can be used as the base data for the development of smart intersection operations.
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Yang, Xianfeng (Terry), Gang-Len Chang, Zhao Zhang, and Pengfei (Taylor) Li. "Smart Signal Control System for Accident Prevention and Arterial Speed Harmonization under Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 27, 2019): 61–71. http://dx.doi.org/10.1177/0361198119837242.
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The intent of this paper is to develop a system that can integrate connected vehicle (CV) data and traffic sensor information to concurrently address the need to improve urban arterial safety and mobility. Under the mixed traffic pattern of CVs and human-driven vehicles (HVs), the system aims to achieve three primary objectives: proactively preventing rear-end collision, reactively protecting side-street traffic from red-light-running vehicles, and effectively facilitating speed harmonization along local arterials. The embedded safety function will integrate CV and roadside sensor data to compute the distribution of dilemma zones for vehicles of different approaching speeds in real-time. Such data fusion will enable the proposed system to offer the advice of either “stop” or “go” to both CVs and HVs so as to prevent rear-end collisions and side-angled crashes. Given the locations and speeds of CVs, and the number of vehicles monitored by sensors, the proposed system can further compute the time-varying intersection queue length. Then the embedded mobility function will optimize the arterial signal plan in real-time and produce the speed advisory for approaching vehicles to facilitate their progression through intersections. Results from extensive simulation experiments confirm the effectiveness of the proposed system in both reducing potential intersection crash rates and improving arterial progression efficiency. The proposed control framework also proves the effectiveness of using dilemma zone protection sensors for traffic mobility improvement.
Dissertations / Theses on the topic "Connected Vehicles (CVs)"
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McManus, Ian Patrick. "The Impact of Cyberattacks on Safe and Efficient Operations of Connected and Autonomous Vehicles." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104891.
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The landscape of vehicular transportation is quickly shifting as emerging technologies continue to increase in intelligence and complexity. From the introduction of Intelligent Transportation Systems (ITS) to the quickly developing field of Connected and Autonomous Vehicles (CAVs), the transportation industry is experiencing a shift in focus. A move to more autonomous and intelligent transportation systems brings with it a promise of increased equity, efficiency, and safety. However, one aspect that is overlooked in this shift is cybersecurity.
As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to develop a cyber-resilient network. Ensuring protection against cyberattacks and developing a system that can handle their consequences is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system.
This research aimed to quantify the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed using Veins software to model a network of intelligent intersections in an urban environment. Vehicles communicated with Road-Side Units (RSUs) to make intersection reservations – effectively simulating CAV vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping and delaying vehicle's intersection reservation requests, respectively. Attacks were modeled with varying degrees of severity by changing the number of infected RSUs in the system and their attack success rates.
Data analysis showed that severe attacks, either from a DoS or MITM attack, can have significant impact on the transportation network's operations. The worst-case scenario for each introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of compromised RSUs directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network.
These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a network that can respond to an initial attack and prevent an attack's dissemination through the network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for the next generation of transportation, adversaries could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network.Master of Science
The landscape of transportation is quickly shifting as transportation technologies continue to increase in intelligence and complexity. The transportation industry is shifting its focus to Connected and Autonomous Vehicles (CAVs). The move to more autonomous and intelligent transportation systems brings with it a promise of increased transportation equity, efficiency, and safety. However, one aspect that is often overlooked in this shift is cybersecurity. As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing cyber vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to prevent and handle attacks. Ensuring protection against cyberattacks is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system. This research aimed to measure the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed to model an intelligent urban road network. Vehicles made reservations at each intersection they passed – effectively simulating an autonomous vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping, and delaying vehicle's intersection reservation requests, respectively. These cyberattacks were modeled with varying degrees of severity to test the different impacts on the transportation network. Analysis showed that severe attacks can have significant impact on the transportation network's operations. The worst-case scenario for each attack introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of attacked intersections directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network. These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a transportation network that can respond to an initial attack and prevent it from impacting the entire network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for CAVs, hackers could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network.
Books on the topic "Connected Vehicles (CVs)"
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Wishart, Jeffrey, Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu, and Yezhou Yang. Fundamentals of Connected and Automated Vehicles. SAE International, 2022. http://dx.doi.org/10.4271/9780768099829.
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The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.
Book chapters on the topic "Connected Vehicles (CVs)"
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Parkes, Stephen, and Ed Ferrari. "The Challenges Posed by Cavs for the Built Environment." In Connected and Autonomous Vehicles, 37–51. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-3.
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Banerjee, Ian, and Tomoyuki Furutani. "Strategic spatial planning, “smart shrinking,” and the deployment of CAVs in rural Japan." In AVENUE21. Politische und planerische Aspekte der automatisierten Mobilität, 239–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63354-0_13.
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ZusammenfassungThe experiments taking place around connected and automated vehicles (CAVs) in global innovation networks today are largely technological in nature. This research takes a relational view of CAVs by investigating how they can be conceptualized within the larger context of strategic spatial planning. To do so, it takes Japan as a case study and explores how the current government is applying the tools of its new National Spatial Strategy (NSS) to strategically steer the development of its main economic and social sectors, including transport and the deployment of CAVs.
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Conti, Mauro, Denis Donadel, Radha Poovendran, and Federico Turrin. "EVExchange: A Relay Attack on Electric Vehicle Charging System." In Computer Security – ESORICS 2022, 488–508. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17140-6_24.
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AbstractTo support the increasing spread of Electric Vehicles (EVs), Charging Stations (CSs) are being installed worldwide. The new generation of CSs employs the Vehicle-To-Grid (V2G) paradigm by implementing novel standards such as the ISO 15118. This standard enables high-level communication between the vehicle and the charging column, helps manage the charge smartly, and simplifies the payment phase. This novel charging paradigm, which connects the Smart Grid to external networks (e.g., EVs and CSs), has not been thoroughly examined yet. Therefore, it may lead to dangerous vulnerability surfaces and new research challenges.In this paper, we present EVExchange, the first attack to steal energy during a charging session in a V2G communication: i.e., charging the attacker’s car while letting the victim pay for it. Furthermore, if reverse charging flow is enabled, the attacker can even sell the energy available on the victim’s car! Thus, getting the economic profit of this selling, and leaving the victim with a completely discharged battery. We developed a virtual and a physical testbed in which we validate the attack and prove its effectiveness in stealing the energy. To prevent the attack, we propose a lightweight modification of the ISO 15118 protocol to include a distance bounding algorithm. Finally, we validated the countermeasure on our testbeds. Our results show that the proposed countermeasure can identify all the relay attack attempts while being transparent to the user.
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Banerjee, Ian, Peraphan Jittrapirom, and Jens S. Dangschat. "Data-driven urbanism, digital platforms, and the planning of MaaS in times of deep uncertainty: What does it mean for CAVs?" In AVENUE21. Politische und planerische Aspekte der automatisierten Mobilität, 441–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63354-0_20.
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ZusammenfassungThis paper offers a critical review of three coevolving socio-technical paradigms: (a) “data-driven urbanism,” (b) digital platforms, and (c) “Mobility-as-a-Service” (MaaS). It explores the complex relationship unfolding between data-driven cities and digital platforms, while drawing on MaaS as a case to discuss the challenges of implementing mobility services via digital platforms. Inferences are drawn from the ongoing debate accompanying these three paradigms to identify potential criteria for the design of socially accountable governance models for the deployment of connected and automated vehicles (CAVs).
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Joerger, Mathieu, Cynthia Jones, and Valerie Shuman. "Testing Connected and Automated Vehicles (CAVs): Accelerating Innovation, Integration, Deployment and Sharing Results." In Lecture Notes in Mobility, 197–206. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94896-6_17.
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Hussain, Naziya, Preeti Rani, Harsha Chouhan, and Urvashi Sharma Gaur. "Cyber Security and Privacy of Connected and Automated Vehicles (CAVs)-Based Federated Learning: Challenges, Opportunities, and Open Issues." In Federated Learning for IoT Applications, 169–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85559-8_11.
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Wang, Shangbo. "Traffic State Prediction and Traffic Control Strategy for Intelligent Transportation Systems." In Intelligent Electronics and Circuits - Terahertz, IRS, and Beyond [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101675.
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The recent development of V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure), V2X (Vehicle-to-Everything) and vehicle automation technologies have enabled the concept of Connected and Automated Vehicles (CAVs) to be tested and explored in practice. Traffic state prediction and control are two key modules for CAV systems. Traffic state prediction is important for CAVs because adaptive decisions, control strategies such as adjustment of traffic signals, turning left or right, stopping or accelerating and decision-making of vehicle motion rely on the completeness and accuracy of traffic data. For a given traffic state and input action, the future traffic states can be predicted via data-driven approaches such as deep learning models. RL (Reinforcement Learning) - based approaches gain the most popularity in developing optimum control and decision-making strategies because they can maximize the long-term award in a complex system via interaction with the environment. However, RL technique still has some drawbacks such as a slow convergence rate for high-dimensional states, etc., which need to be overcome in future research. This chapter aims to provide a comprehensive survey of the state-of-the-art solutions for traffic state prediction and traffic control strategies.
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Alshaer, Hamada, Sami Muhaidat, Raed Shubair, and Moein Shayegannia. "Security and Connectivity Analysis in Vehicular Communication Networks." In Security, Privacy, Trust, and Resource Management in Mobile and Wireless Communications, 83–107. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4691-9.ch005.
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Reliable Vehicular Ad-Hoc Networks (VANETs) require secured uninterrupted uplink and downlink connectivity to guarantee secure ubiquitous vehicular communications. VANET mobility, multi-fading wireless, and radio channels could result in unsecured and disrupted vehicular communications, isolating some vehicle nodes and making them vulnerable to security attacks. A VANET is considered to be connected and secured if there is a secured path connecting any pair of Communication-Enabled Vehicles (CEVs) in this network. Among many parameters, VANET connectivity depends on two main elements: communication transmission range and statistical distribution characterizing inter-vehicle spacing. To guarantee persistent VANET connectivity, a vehicle transmission radio range must be set properly based on the characteristic of the statistical distribution modeling the inter-vehicle spacing. This chapter analyzes three inter-vehicle spacing models based on exponential, Generalized Extreme Value (GEV), and Exponential with Robustness Factor (EwRF) statistical distributions. Based on vehicle nodes spatial density on a road segment, each vehicle node can adjust its transmission range to increase network connectivity and guarantee ubiquitous vehicular communications. Communications among vehicle nodes are secured through trusted Road-Side Units (RSUs) which distribute efficiently secret keys to vehicle nodes under their coverage to establish secure communication sessions.
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Alimardani, Fatemeh, Nilesh Suriyarachchi, Faizan M. Tariq, and John S. Baras. "Models and Methods for Intelligent Highway Routing of Human-Driven and Connected-and-Automated Vehicles." In Transportation Systems for Smart, Sustainable, Inclusive and Secure Cities [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94332.
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Connected and automated vehicles (CAVs) have seen a rapid surge in interest over the past few years. A lot of focus is being placed on improving the efficiency and robustness of transportation systems by leveraging the sensors and capabilities of CAVs. However, the integration of CAVs into existing traffic infrastructure would give rise to certain issues that must be addressed before the CAVs can be seen ubiquitously on public roads. Since the highway networks are considered permanent investments that are expensive to build and maintain, the priority is to improve the efficiency of the current traffic system. This chapter explores the integration of two of the most common traffic management strategies, namely, ramp metering (RM) and route guidance (RG), into existing highway networks with human-driven vehicles (HDVs). The introduction of CAVs to public roads will engender issues pertaining to safe interactions between CAVs and HDVs. The later part of the chapter addresses the specific problems of improving highway on-ramp merging efficiency by optimally coordinating CAVs. The chapter concludes by presenting a scenario that requires an explicit consideration of interactions between HDVs and CAVs.
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Negrut, Dan, Asher Elmquist, Radu Serban, Dylan Hatch, and Parmesh Ramanathan. "A Connected Autonomous Vehicle Emulator (CAVE) for Testing Multi-agent, Conventional–Autonomous Mixed Vehicle Traffic Scenarios." In Advances in Computers and Information in Engineering Research, Volume 2, 339–58. ASME, 2021. http://dx.doi.org/10.1115/1.862025_ch11.
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We discuss a software infrastructure that provides a virtual proving ground for designing, training, and auditing the computer programs used to pilot connected autonomous vehicles (CAVs). This effort does not concentrate on developing the piloting computer programs (PCPs) responsible for path planning in autonomous vehicles (AVs). Instead, we have established a first version of an emulation platform that changes the PCP design/test/improve process, which is often times carried out covertly [46], or in actual traffic conditions with potentially fatal consequences [45, 47].
Conference papers on the topic "Connected Vehicles (CVs)"
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Ninos, Georgios, and José Diogo Forte de Oliveira Luna. "Model predictive control for connected and autonomous vehicles at road intersections." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1420.
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This work presents a model predictive control (MPC) to coordinate connected and autonomous vehicles (CAVs) with a Vehicle-to-Vehicle (V2V) communication when they enter intersections. With the purpose of minimizing energy as well as passing the intersection smoothly, it uses an individual linear quadratic optimal controller for each CAV, with a predefined path, that will respect mixed-integer linear constraints to guarantee collision avoidance in relation to the nearby vehicles. This method solves different scenarios with a different number of CAVs crossing the intersection, coming from more than one road, including a platoon formation. The results show that MPC is an efficient technique to integrate multiple CAVs to collaborate with the mutual objective of join merging zones without accidents.
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Pacheco, Lucas Sousa, Denis Lima Rosário, Eduardo Coelho Cerqueira, and Leandro Aparecido Villas. "Service Migration in Edge Computing Environments for Connected Autonomous Vehicles." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sbrc.2020.12307.
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In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a major role in the safety of the vehicles and passengers. The extensive amount of sensors in each vehicle will generate huge amounts of data that cannot be processed promptly by onboard units. Edge computing is a crucial solution to provide the required computation power and extremely low latency requirements for the future generation of CAVs. However, the high mobility of vehicles, together with dynamic 5G networking scenarios, poses a challenge to keep the services always close to the users, and therefore, keep the latency very low, such as expected by CAVs. In this paper, we propose MILT, a service migration algorithm for edge computing to perform predictive migration of services based on mobility prediction, available resources, and the quality level of the networks and applications. MILT supports a mobility-based handover prediction scheme to perform a pre-migration to the best available edge server while reducing the latency and increasing the processing capacity of the services of CAVs. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.
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Mehr, Goodarz, and Azim Eskandarian. "Development and Calibration of a Low-Cost Machine Vision Pipeline for Connected and Autonomous Vehicle (CAV) Research." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70836.
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Abstract Autonomous vehicles promise a safer future with a cleaner, more cost-efficient, and more reliable transportation system. However, the current approach to autonomy for vehicles has focused on building small, expensive, disparate intelligences that are closed off to the rest of the world. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) connectivity can bridge some of that gap through enabling communication between a vehicle and its surrounding environment. This has to be paired with a low-cost approach to perception to enable broader research into and adoption of connected and autonomous vehicles (CAVs). To that end, this paper presents the development and calibration process of a low-cost, easy-to-deploy machine vision pipeline for multiple cameras that does not sacrifice image quality or frame rate. The pipeline is integrated with the CARMA Platform, developed by the FHWA for connected vehicle research.
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Jan, Lung En, Junfeng Zhao, Shunsuke Aoki, Anand Bhat, Chen-Fang Chang, and Ragunathan (Raj) Rajkumar. "Speed Trajectory Generation for Energy-Efficient Connected and Automated Vehicles." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3148.
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Abstract Connected and automated vehicles (CAVs) have real-time knowledge of the immediate driving environment, actions to be taken in the near future and information from the cloud. This knowledge, referred to as preview information, enables CAVs to drive safely, but can also be used to minimize fuel consumption. Such fuel-efficient transportation has the potential to reduce aggregate fuel consumption by billions of gallons of gas every year in the U.S. alone. In this paper, we propose a planning framework for use in CAVs with the goal of generating fuel-efficient vehicle trajectories. By utilizing on-board sensor data and vehicle-to-infrastructure (V2I) communications, we leverage the computational power of CAVs to generate eco-friendly vehicle trajectories. The planner uses an eco-driver model and a predictive cost-based search to determine the optimal speed profile for use by a CAV. To evaluate the performance of the planner, we introduce a co-simulation environment consisting of a CAV simulator, Matlab/Simulink and a CAV software platform called the InfoRich Eco-Autonomous Driving (iREAD) system. The planner is evaluated in various urban traffic scenarios based on real-world road network models provided by the National Renewable Energy Laboratory (NREL). Simulations show an average savings of 14.5% in fuel consumption with a corresponding increase of 2% in travel time using our method.
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"CAVS 2019 Panel." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887839.
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"CAVS 2019 Keynotes." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887802.
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"CAVS 2019 Committees." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887808.
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"CAVS 2019 Schedule." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887810.
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"CAVS 2019 Reviewers." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887856.
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Vellamattathil Baby, Tinu, Pouria Karimi Shahri, Amir H. Ghasemi, and Baisravan HomChaudhuri. "Suggestion-Based Fuel Efficient Control of Connected and Automated Vehicles." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3193.
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Abstract This paper presents a suggestion-based fuel efficient controller for connected and automated vehicles (CAVs) in presence of human-driven vehicles (HDVs). The suggestion-based controller, apart from evaluating the fuel efficient control solution of the host CAV, provides suggested velocity commands to the HDVs so that the fuel efficiency of itself or the group can improve. We assume that in the connected vehicle system, the HDVs are also able to receive information though Vehicle to Vehicle (V2V) communication and they always try to follow the suggested commands. The suggestion-based control provides additional decision variables to the CAVs with which they can influence the actions of the HDVs and hence improve the fuel efficiency of the whole group. The controller is implemented in a model predictive control (MPC) framework where the suggested command velocities are held constant over some prescribed time so that the driver gets enough time to reach the suggested command velocities. For this control method to function, we present a model that captures the response of a HDV to different suggested-commands. The parameters of the model is obtained from a table-top drive simulator. The accuracy of this model is also validated with the experimental data (table-top drive simulator) and the results are presented in this paper. Simulation studies for the control strategies show the efficacy of the proposed control strategy when compared with existing baseline methods.
Reports on the topic "Connected Vehicles (CVs)"
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Kalaiyarasan, Arun, Ben Simpson, David Jenkins, Francesco Mazzeo, Hao Ye, Isi Obazele, Konstantinos Kourantidis, et al. Remote operation of Connected and Automated Vehicles. TRL, August 2021. http://dx.doi.org/10.58446/jtwi9672.
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Currently, during automated vehicle trials there are two personnel present within each test vehicle: the Safety Driver and the Test Assistant. This summary report presents recommendations on how to progress to advanced trials and perform the roles of the Safety Driver and Test Assistant remotely. The recommendations are based on a literature review and a stakeholder engagement. It was found that the terminology relating to remote operation used by the industry is inconsistent, so key terminology has been produced with the aim of promoting common use. The summary of roles, requirements and responsibilities of Safety Drivers, Test Assistants, and the Remote Operators (who would replace them) are presented. Following this, recommendations are given to enable remote operation of connected and automated vehicles (CAVs). Finally, these recommendations have been used to generate a roadmap to enable remote operation of CAVs in the UK.
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Gajera, Hardik, Srinivas S. Pulugurtha, and Sonu Mathew. Influence of Level 1 and Level 2 Automated Vehicles on Fatal Crashes and Fatal Crash Occurrence. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2034.
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Connected and automated vehicles (CAVs) are expected to improve safety by gradually reducing human decisions while driving. However, there are still questions on their effectiveness as we transition from almost 0% CAVs to 100% CAVs with different levels of vehicle autonomy. This research focuses on synthesizing literature and identifying risk factors influencing fatal crashes involving level 1 and level 2 CAVs in the United States. Fatal crashes involving level 0 vehicles—ones that are not connected and automated—were compared to minimize unobserved heterogeneity and randomness associated with the influencing risk factors. The research team used the fatal crash data for the years 2016 to 2019 for the analysis. A partial proportionality odds model is developed using crash, road, and vehicle characteristics as the independent variables and the fatal crash involving a vehicle with a specific level of automation as the dependent variable. The results of this research indicate that level 1 and level 2 CAVs are less likely to be involved in a fatal crash at four-way intersections, on two-way routes with wide medians, at nighttime, and in poor lighting conditions when compared to level 0 vehicles. However, they are more likely than level 0 vehicles to be involved in a fatal crash with pedestrians and bicyclists. Comparative analysis between vehicles with smart features and other vehicles indicated that pedestrian automatic emergency braking (PAEB) and lane-keeping assistance (LKA) improve the safety by reducing possible collision with a pedestrian and roadside departure, respectively. Contrarily, vehicles with other smart features are still highly likely to be involved in fatal crashes. This research adds to the growing body of literature that will identify potential areas for improvement in the safety of vehicular technologies and road geometry.
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Li, Howell, Tom Platte, Jijo K. Mathew, W. Benjamin Smith, Enrique Saldivar-Carranza, and Darcy M. Bullock. Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317321.
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The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.
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Benkraouda, Ouafa, Lindsay Braun, and Arnab Chakraborty. Policies and Design Guidelines to Plan for Connected and Autonomous Vehicles. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-012.
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This report chronicles the work undertaken by researchers at the University of Illinois Urbana Champaign to identify policies and design guidelines to plan for connected and autonomous vehicles (CAVs) in mid-sized regions in Illinois. The report starts with the goals of this work followed by a review of existing literature. The review addresses CAV technologies and scenario planning, including academic research articles, policies and guidance documents from federal and state agencies, and recent long-range transportation plans. The review findings are organized into three categories—drivers, levers, and impacts—to facilitate scenario-based planning and included key factors and trends in technology development and adoption (drivers), mechanisms that planners and policymakers may employ to intervene in or prepare for CAV futures (levers), and community-level outcomes of different plausible CAV futures (impacts). Primary research was undertaken first by interviewing practitioners in six mid-sized regions of Illinois to collect inputs about their needs and obstacles to planning for CAVs, as well as to understand their sense of their community’s preparedness for CAVs. The research team then conducted a detailed survey of over 700 residents from the Greater Peoria region to understand their would-be travel behavior and residential location decisions in a CAV future and general attitude toward self-driving cars. These inputs helped identify the key drivers, levers, and impacts to be employed in creating scenarios, a list of selected policies and design, and a framework to select appropriate responses based on the needs and desires of a community. The detailed scenarios are as follows: (1) continuation of the status quo, (2) private multimodal future, and (3) shared multimodal future. The policies and design guidelines are identified for each scenario and are categorized into six sets of action items: general, data and digitization, mobility and traffic, street design, infrastructure, and planning. Specific details of each action item are organized in a format that allows the user to consider each item carefully and to assess its feasibility in a specific region or city. The appendices include background documents related to primary research and, importantly, a handbook for practitioners.