Relevant bibliographies by topics / Connected automated vehicles (CAV)

Academic literature on the topic 'Connected automated vehicles (CAV)'

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Published: 10 December 2022
Last updated: 28 January 2023

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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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Dissertations / Theses on the topic "Connected automated vehicles (CAV)"

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Kero, Chanelle. "A Literature Review of Connected and Automated Vehicles : Attack Vectors Due to Level of Automation." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80322.

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The manufacturing of connected and automated vehicles (CAVs) is happening and they are aiming at providing an efficient, safe, and seamless driving experience. This is done by offering automated driving together with wireless communication to and from various objects in the surrounding environment. How automated the vehicle is can be classified from level 0 (no automation at all) to level 5 (fully automated). There is many potential attack vectors of CAVs for attackers to take advantage of and these attack vectors may change depending on what level of automation the vehicle have. There are some known vulnerabilities of CAVs where the security has been breached, but what is seemed to be lacking in the academia in the field of CAVs is a place where the majority of information regarding known attack vectors and cyber-attacks on those is collected. In addition to this the attack vectors may be analyzed for each level of automation the vehicles may have. This research is a systematic literature review (SLR) with three stages (planning, conducting, and report) based on literature review methodology presented by Kitchenham (2004). These stages aim at planning the review, finding articles, extracting information from the found articles, and finally analyzing the result of them. The literature review resulted in information regarding identified cyberattacks and attack vectors the attackers may use as a path to exploit vulnerabilities of a CAV. In total 24 types of attack vectors were identified. Some attack vectors like vehicle communication types, vehicle applications, CAN bus protocol, and broadcasted messages were highlighted the most by the authors. When the attack vectors were analyzed together with the standard of ‘Levels of Driving Automation’ it became clear that there are more vulnerabilities to consider the higher level of automation the vehicle have. The contributions of this research are hence (1) a broad summary of attack vectors of CAVs and (2) a summary of these attack vectors for every level of driving automation. This had not been done before and was found to be lacking in the academia.
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Narasimhan, Ramakrishnan Akshra. "Design and Evaluation of Perception System Algorithms for Semi-Autonomous Vehicles." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595256912692618.

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Monteuuis, Jean-Philippe. "Resilience by design & failures forecasting for a connected autonomous vehicle." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT003.

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Les véhicules autonomes dotés d'un niveau d'automatisation 5 conduiront de manière autonome dans tous les scénarios routiers tels que les autoroutes, les routes enneigées, les zones urbaines ou les embouteillages. L'intégration de la communication V2X, en tant que nouvelle source de perception du véhicule, pourrait supprimer les limitations de la perception locale en communiquant avec un piéton caché par un obstacle ou en détectant à l'avance la présence d'un véhicule caché par un brouillard épais. Cependant, cette communication V2X peut constituer une nouvelle source d'attaques menaçant la perception du véhicule. Les contre-mesures actuelles ne sont pas conçues pour toutes les architectures de véhicules autonomes, car elles requièrent l'assistance du conducteur ou fonctionnent avec un ensemble spécifique de capteurs. La thèse vise donc à proposer une architecture de perception générique et résiliante aux défaillances pour tous les types de véhicules connectés et autonomes. Dans cette thèse, nous proposons une architecture de perception générique nommée GPA avec son algorithme de perception résiliante aux défaillances (FRPA). Nous proposons une nouvelle méthode d'analyse de menaces et d'évaluation des risques nommée SARA, qui identifie et évalue le risque d'attaques ciblant les véhicules connectés et automatisés de niveau 5. Pour identifier où et comment ces attaques ont lieu, nous proposons un modèle d'attaquant et un modèle d'objectifs de sécurité pour tous les systèmes de perception automobile. Nous avons implémenté deux modules de notre algorithme FRPA: un module classification des défaillances basé sur une méthode de Machine Learning et un module de corrélation V2X-Capteur en considérant trois sources d'information: radar, camera et V2X. Nous avons mis en évidence plusieurs nouvelles attaques dans le cycle de perception et soulevé le besoin de nouvelles contre-mesures de sécurité centrées sur l'intégrité physique des infrastructures routières et sur les algorithmes de perception fiables. De plus, nos contre-mesures basées sur l'apprentissage automatique et la corrélation entre capteurs sont très précises pour détecter et classifier les défaillances de perception (score de précision supérieur à 90 %). Enfin, les idées développées dans la thèse ont abouti à 10 brevets déposés et à plusieurs publications
Autonomous vehicles with an automation level 5 will drive autonomously in any road scenarios such as highways, snowy roads, urban areas, or traffic jams. The integration of V2X communication, as a new source of perception for the vehicle could remove the limitations of local perception by communicating with an occluded pedestrian or by detecting in advance the presence of a vehicle under a heavy mist. However, this V2X communication may be a new source of attacks threatening the vehicle perception. Current countermeasures are not designed for all autonomous vehicles because these countermeasures require the driver assistance or work with a specific set of sensors. Therefore, the thesis aims to propose a generic failure resilient perception architecture for all types of connected and autonomous vehicles supporting different kinds of sensors. In this thesis, we propose a generic perception architecture named GPA with its failure resilient perception algorithm (FRPA). We propose a new threat analysis and risk assessment method named SARA that identifies and assess the risk of attacks targeting connected and automated vehicles with an automation level 5. To identify where and how these attacks occur, we propose an attacker and a security goal model for all automotive perception systems. We implemented two modules of our failures resilient perception algorithm (FRPA): a Machine Learning based Failure Classifier and a V2X-Sensor Correlation Module considering three kinds of source: camera, radar, and V2X. We highlighted several new attacks in the perception pipeline and raise the need for new security countermeasures such as the physical integrity of road infrastructures and trustworthy perception algorithms. Besides, our countermeasures based on machine learning and sensor correlation showed very accurate results to detect and classifies perception failures (over 90% accuracy score). Finally, the ideas developed in the thesis resulted in 10 filled patents and several publications
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Liu, Peng. "Distributed Model Predictive Control for Cooperative Highway Driving." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500564857136091.

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Goel, Shlok. "Research, Design, and Implementation of Virtual and Experimental Environment for CAV System Design, Calibration, Validation and Verification." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595368946630713.

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Kim, Bumsik. "Modeling Automated Vehicles and Connected Automated Vehicles on Highways." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103012.

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The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.
Doctor of Philosophy
The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.
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Mangette, Clayton John. "Perception and Planning of Connected and Automated Vehicles." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98812.

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Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments.
Master of Science
Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.
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Mohammadian, Saeed. "Freeway traffic flow dynamics and safety: A behavioural continuum framework." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/227209/1/Saeed_Mohammadian_Thesis.pdf.

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Congestion and rear-end crashes are two undesirable phenomena of freeway traffic flows, which are interrelated and highly affected by human psychological factors. Since congestion is an everyday problem, and crashes are rare events, congestion management and crash risk prevention strategies are often implemented through separate research directions. However, overwhelming evidence has underscored the inter-relation between rear-end crashes and freeway traffic flow dynamics in recent decades. This dissertation develops novel mathematical models for freeway traffic flow dynamics and safety to integrate them into a unifiable framework. The outcomes of this PhD can enable moving towards faster and safer roads.
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Ghiasi, Amir. "Connected Autonomous Vehicles: Capacity Analysis, Trajectory Optimization, and Speed Harmonization." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7295.

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Emerging connected and autonomous vehicle technologies (CAV) provide an opportunity to improve highway capacity and reduce adverse impacts of stop-and-go traffic. To realize the potential benefits of CAV technologies, this study provides insightful methodological and managerial tools in microscopic and macroscopic traffic scales. In the macroscopic scale, this dissertation proposes an analytical method to formulate highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). The proposed analytical mixed traffic highway capacity model is based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. In the microscopic scale, this study develops innovative control strategies to smooth highway traffic using CAV technologies. First, it formulates a simplified traffic smoothing model for guiding movements of CAVs on a general one-lane highway segment. The proposed simplified model is able to control the overall smoothness of a platoon of CAVs and approximately optimize traffic performance in terms of fuel efficiency and driving comfort. The elegant theoretical properties for the general objective function and the associated constraints provides an efficient analytical algorithm for solving this problem to the exact optimum. Numerical examples reveal that this exact algorithm has an efficient computational performance and a satisfactory solution quality. This trajectory-based traffic smoothing concept is then extended to develop a joint trajectory and signal optimization problem. This problem simultaneously solves the optimal CAV trajectory function shape and the signal timing plan to minimize travel time delay and fuel consumption. The proposed algorithm simplifies the vehicle trajectory and fuel consumption functions that leads to an efficient optimization model that provides exact solutions. Numerical experiments reveal that this algorithm is applicable to any signalized crossing points including intersections and work-zones. Further, the model is tested with various traffic conditions and roadway geometries. These control approaches are then extended to a mixed traffic environment with HVs, connected vehicles (CVs), and CAVs by proposing a CAV-based speed harmonization algorithm. This algorithm develops an innovative traffic prediction model to estimate the real-time status of downstream traffic using traffic sensor data and information provided by CVs and CAVs. With this prediction, the algorithm controls the upstream CAVs so that they smoothly hedge against the backward deceleration waves and gradually merge into the downstream traffic with a reasonable speed. This model addresses the full spectrum of CV and CAV market penetration rates and various traffic conditions. Numerical experiments are performed to assess the algorithm performance with different traffic conditions and CV and CAV market penetration rates. The results show significant improvements in damping traffic oscillations and reducing fuel consumption.
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Harper, Corey David. "Transitioning to a Connected and Automated Vehicle Environment: Opportunities for Improving Transportation." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1007.

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Over the past few years automotive and technology companies have made significant advances in what has been traditionally a completely human function: driving. Crash avoidance features such as lane departure warning and forward collision warning are becoming increasingly more common and cheaper to obtain, even on non-luxury vehicles. Technology companies and auto manufacturers have announced plans to have self-driving vehicles ready for public use as early as 2020. The mass adoption of automated vehicles (AVs) could significantly change surface transportation as we know it today. This thesis is intended to provide a technical analysis of the potential impacts of AVs on current light-duty vehicle miles traveled (VMT) and parking decisions, the economic desirability of widespread deployment of partially automated technologies, and methods for existing roadways to transition to connected and automated vehicle (CAV) transportation, so that policymakers can make more informed decisions during the transition to CAVs. This work takes a look at AVs from a point in time where vehicles are equipped with driver assistance systems (Level 1) to a point in time where AVs are driverless (Level 5) and can self-park. The results of this work indicate that the fleet-wide adoption of partially automated crash avoidance technologies could provide net-benefit of about $4 billion at current system effectiveness and could provide an annual net-benefit up to $202 billion if all relevant crashes could be prevented. About 25% of all crashes could be addressed by the crash avoidance technologies examined in this dissertation. Over time, as technologies become more effective and cheaper due to economies of scale, greater benefits than the $4 billion could be realized. As automated technologies become more advanced and widespread, existing roadways will need to be able to accommodate these vehicles. This work investigates the effects of a dedicated truck platoon lane on congestion on the Pennsylvania Turnpike and provides a method for existing roadways and highways to determine viable platoon demonstration sites. The initial results suggest that there are several sections of turnpike that could serve as commercial truck platoon demonstration site while still providing a high LOS to all other vehicles. Once AVs can safely and legally drive unoccupied, vehicles will no longer be limited to their driver’s destination and can search for cheaper parking in more distant parking locations. This work simulates a fleet of privately owned vehicles (POVs) in search of cheaper parking in Seattle, using a rectangular grid throughout the study area. Model results indicate that we are not likely to see significant increase in vehicle miles traveled (VMT) and energy use from cars moving from downtown parking lots to cheaper parking in distance locations but at higher penetration rates, parking lot revenues could likely decline to the point where operating a lot is unsustainable economically, if no parking demand management policies are implemented. Driverless vehicles also promise to increase mobility for those in underserved populations. This work estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. Three demand wedges were established in order to conduct a first-order bounding analysis. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. AV technology holds much promise in providing a more accessible and safe transportation system. This thesis can help policymakers and stakeholders maximize the benefits and minimize the challenges.
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Books on the topic "Connected automated vehicles (CAV)"

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Zmud, Johanna, Ginger Goodin, Maarit Moran, Nidhi Kalra, and Eric Thorn. Strategies to Advance Automated and Connected Vehicles. Washington, D.C.: Transportation Research Board, 2017. http://dx.doi.org/10.17226/24873.

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Ricci, Andrea. Socioeconomic Impacts of Automated and Connected Vehicles. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25359.

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Guda, Alexander, ed. Networked Control Systems for Connected and Automated Vehicles. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-11051-1.

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Guda, Alexander, ed. Networked Control Systems for Connected and Automated Vehicles. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-11058-0.

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Fisher, Donald L., William J. Horrey, John D. Lee, and Michael A. Regan, eds. Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles. Boca Raton, FL : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/b21974.

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Hamilton, Booz Allen, and New Jersey Institute of Technology. Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25366.

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Turnbull, Katherine F. Automated and Connected Vehicles: Summary of the 9th University Transportation Centers Spotlight Conference. Washington, D.C.: Transportation Research Board, 2016. http://dx.doi.org/10.17226/23621.

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Zmud, Johanna, Ginger Goodin, Maarit Moran, Nidhi Kalra, and Eric Thorn. Advancing Automated and Connected Vehicles: Policy and Planning Strategies for State and Local Transportation Agencies. Washington, D.C.: Transportation Research Board, 2017. http://dx.doi.org/10.17226/24872.

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Zmud, Johanna, Tom Williams, Maren Outwater, Mark Bradley, Nidhi Kalra, and Shelley Row. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25332.

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Zmud, Johanna, Tom Williams, Maren Outwater, Mark Bradley, Nidhi Kalra, and Shelley Row. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25319.

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Book chapters on the topic "Connected automated vehicles (CAV)"

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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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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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Kurzhanskiy, A. A., F. Borrelli, and P. Varaiya. "Connected and Automated Vehicles." In Encyclopedia of Systems and Control, 1–11. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_100119-1.

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Kurzhanskiy, A. A., F. Borrelli, and P. Varaiya. "Connected and Automated Vehicles." In Encyclopedia of Systems and Control, 240–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100119.

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Klein, Lawrence A. "Automated vehicles." In ITS Sensors and Architectures for Traffic Management and Connected Vehicles, 253–92. Boca Raton : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315206905-11.

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Zünd, Daniel, and Luís M. A. Bettencourt. "Street View Imaging for Automated Assessments of Urban Infrastructure and Services." In Urban Informatics, 29–40. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_4.

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AbstractMany forms of ambient data in cities are starting to become available that allows tracking of short-term urban operations, such as traffic management, trash collections, inspections, or non-emergency maintenance requests. However, arguably the greatest promise of urban analytics is to set up measurable objectives and track progress toward systemic development goals connected to human development and sustainability over the longer term. The challenge for such an approach is the connection between new technological capabilities, such as sensing and machine learning and local knowledge, and operations of residents and city governments. Here, we describe an emerging project for the long-term monitoring of sustainable development in fast-growing towns in the Galapagos Islands through the convergence of these methods. We demonstrate how collaborative mapping and the capture of 360-degree street views can produce a general basis for a broad set of quantitative analytics, when such actions are coupled to mapping and deep-learning characterizations of urban environments. We map and assess the precision of urban assets via automatic object classification and characterize their abundance and spatial heterogeneity. We also discuss how these methods, as they continue to improve, can provide the means to perform an ambient census of urban assets (buildings, vehicles, services) and environmental conditions.
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Younis, Mohamed, Sookyoung Lee, Wassila Lalouani, Dayuan Tan, and Sanket Gupte. "Dynamic Road Management in the Era of CAV." In Connected and Autonomous Vehicles in Smart Cities, 133–72. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429329401-5.

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Noy, Ian Y. "Connected Vehicles in a Connected World." In Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles, 421–40. Boca Raton, FL : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/b21974-19.

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Brost, Mascha, Özcan Deniz, Ines Österle, Christian Ulrich, Murat Senzeybek, Robert Hahn, and Stephan Schmid. "Energy Consumption of Connected and Automated Vehicles." In Electric, Hybrid, and Fuel Cell Vehicles, 201–24. New York, NY: Springer New York, 2021. http://dx.doi.org/10.1007/978-1-0716-1492-1_1098.

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Petit, Jonathan, and William Whyte. "Future Threats to Connected and Automated Vehicles." In Road Vehicle Automation 8, 83–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80063-5_8.

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Conference papers on the topic "Connected automated vehicles (CAV)"

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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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Oh, Sanghoon, Linjun Zhang, Eric Tseng, Wayne Williams, Helen Kourous, and Gabor Orosz. "Safe Decision and Control of Connected Automated Vehicles for an Unprotected Left Turn." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3335.

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Abstract The unprotected left turn of a connected automated vehicle (CAV) is investigated when it has a potential conflict with a connected human-driven vehicle (CHV) approaching in the opposite lane. A control architecture is proposed that includes interactions between the decision making, motion planning, and control levels. By utilizing the road context and information received via vehicle-to-everything (V2X) communication, a reduced state space representation is determined which allows the CAV to evaluate safety in a fast and efficient manner Using a temporal metric, a safety evaluation algorithm is developed which determines the safety of the decision making at controller level. To evaluate the algorithms, data collected with real vehicles is utilized.
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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.
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Wang, Xiaoyang, Ioannis Mavromatis, Andrea Tassi, Raul Santos-Rodriguez, and Robert J. Piechocki. "Location Anomalies Detection for Connected and Autonomous Vehicles." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887778.

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Zhong, Zijia, and Earl E. Lee. "Alternative Intersection Designs with Connected and Automated Vehicle." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887763.

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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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Reports on the topic "Connected automated vehicles (CAV)"

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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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Yang, Xianfeng Terry. Vehicle Sensor Data (VSD) Based Traffic Control in Connected Automated Vehicle (CAV) Environment. Transportation Research and Education Center (TREC), 2018. http://dx.doi.org/10.15760/trec.212.

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Coyner, Kelley, and Jason Bittner. Automated Vehicles and Infrastructure Enablers. SAE International, March 2022. http://dx.doi.org/10.4271/epr2022008.

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Manufacturers and developers of automated vehicles (AVs) often maintain that no new infrastructure enablers are needed to achieve full AV deployment aside from existing infrastructure investments (e.g., connected traffic signals, designated stops, booking software, mobile applications, separated lanes). These groups hold that a state-of-good-repair and clean lane markings are sufficient; however, much of the US receives poor grades when it comes to these features. What do infrastructure owners and operators need to know about what constitutes effective lane markings or what to prioritize in terms of safety and mobility? How do policy considerations effect these choices? Automated Vehicles and Infrastructure Enablers the first in a series on AVs and infrastructure—considers ways in which infrastructure can speed or delay deployment, mitigate hazards, and capture benefits related to AV roll-out. Some of these benefits include accessibility, safety, reduced climate impacts, and integrated supply chain logistics.
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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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Coyner, Kelley, and Jason Bittner. Infrastructure Enablers and Automated Vehicles: Trucking. SAE International, July 2022. http://dx.doi.org/10.4271/epr2022017.

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While automated trucking developers have established regular commercial shipments, operations and testing remain limited largely to limited-access highways like interstates. This infrastructure provides a platform or operating environment that is highly structured, with generally good road conditions and visible lane markings. To date, these deployments have not included routine movements from hub to hub, whether on or off these limited-access facilities. Benefits such as safety, fuel efficiency, staffing for long-haul trips, and a strengthened supply chain turn enable broader deployment which can enable movement from one transportation system to another. Infrastructure Enablers and Automated Vehicles: Trucking focuses on unresolved issues between the automated vehicle industry and infrastructure owners and operators that stand in the way of using infrastructure—both physical and digital—to extend use cases for automated trucking to more operational design domains (ODDs). The report also examines opportunities and recommendations related the integration of automated trucking across transportation networks and the supply chain. The topics include road conditions and lane marking visibility, work zone navigation, transfer hubs, and facility design, as well as connected and electric charging infrastructure.
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Pulugurtha, Srinivas S., and Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.1944.

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Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers' responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants' response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives.
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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.
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Mailhiot, Christian, William Mark Severa, Christopher D. Moen, and Troy Jones. Workshop on Advanced Computing for Connected and Automated Vehicles. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1592572.

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Huang, Ke, and Xianfeng Yang. Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization. Mineta Transportation Institute, August 2020. http://dx.doi.org/10.31979/mti.2020.1924.

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Stephens, T. S., Jeff Gonder, Yuche Chen, Z. Lin, C. Liu, and D. Gohlke. Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1334242.

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You might also be interested in the extended bibliographies on the topic 'Connected automated vehicles (CAV)' for particular source types:
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