Relevant bibliographies by topics / Connected and Automated Vehicles (CAVs)

Academic literature on the topic 'Connected and Automated Vehicles (CAVs)'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Connected and Automated Vehicles (CAVs)"

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Clements, Lewis M., and Kara M. Kockelman. "Economic Effects of Automated Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2606, no. 1 (January 2017): 106–14. http://dx.doi.org/10.3141/2606-14.

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Connected and fully automated or autonomous vehicles (CAVs) may soon dominate the automotive industry. Once CAVs are sufficiently reliable and affordable, they will penetrate markets and thereby generate economic ripple effects throughout industries. This paper synthesizes and expands on existing analyses of the economic effects of CAVs in the United States across 13 industries and the overall economy. CAVs will soon be central to the automotive industry, with software composing a greater share of vehicle value than previously. The number of vehicles purchased each year may fall because of vehicle sharing, but rising travel distances may increase vehicle sales. The opportunity for heavy-truck drivers to do other work or rest during long drives may lower freight costs and increase capacity. Personal transport may shift toward shared autonomous vehicle fleet use, reducing that of taxis, buses, and other forms of group travel. Fewer collisions and more law-abiding vehicles will lower demand for auto repair, traffic police, medical, insurance, and legal services. CAVs will also lead to new methods for managing travel demand and the repurposing of curbside and off-street parking and will generate major savings from productivity gains during hands-free travel and reduction of pain and suffering costs from crashes. If CAVs eventually capture a large share of the automotive market, they are estimated to have economic impacts of $1.2 trillion or $3,800 per American per year. This paper presents important considerations for CAVs’ overall effects and quantifies those impacts.
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Peng, Huei. "Connected and Automated Vehicles." Mechanical Engineering 138, no. 12 (December 1, 2016): S5—S11. http://dx.doi.org/10.1115/1.2016-dec-2.

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This article focuses on dynamics and control of connected and automated vehicles. The complexity and difficulty can grow significantly from low automation levels to higher levels. The paper briefly highlights three challenges, i.e., sensing, localization, and perception. The Mobility Transformation Center (MTC) is a public/private research and development partnership led by the University of Michigan. MTC aims to develop the foundations for a viable ecosystem of CAVs. A popular alternative to test high-automation-level AVs is the Naturalistic-Field Operational Test (N-FOT). In an N-FOT, a number of equipped vehicles are tested under naturalistic driving conditions over an extended period. In the near future, connected and automated vehicle technologies are expected to be deployed rapidly. While there has been a lot of research in, and attention to, the field of sensing, localization, and perception, this paper aims to point out a few areas related to the field of dynamics and control that are opportunities for further research.
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Hung, Ya-Hsin, Robert W. Proctor, Yunfeng Chen, Jiansong Zhang, and Yiheng Feng. "Drivers’ Knowledge of and Preferences for Connected and Automated Vehicles." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1457–61. http://dx.doi.org/10.1177/1071181322661285.

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Connected and automated vehicles (CAVs) offer many potential advantages, including improved traffic flow, reduction of traffic accidents, and increased freedom for adolescents and adults with restricted mobility. However, successful implementation of CAVs depends on several factors, especially acceptance and preferences by people. Specifically, during the earlier stage of deployment, CAVs will have to share the roads with human-driven vehicles (HDVs), which requires communication between CAVs and HDVs regarding their intentions and future actions. Therefore, as a first step in our research program, we conducted a survey of 182 U.S. drivers to assess their knowledge of CAVs and their thoughts about implementation. We report the survey results, accompanied by our interpretations.
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Jiang, Yangsheng, Bin Zhao, Meng Liu, and Zhihong Yao. "A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment." Journal of Advanced Transportation 2021 (April 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/9945398.

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Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.
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Zhang, Hui, Rongqing Zhang, Chen Chen, Dongliang Duan, Xiang Cheng, and Liuqing Yang. "A Priority-Based Autonomous Intersection Management (AIM) Scheme for Connected Automated Vehicles (CAVs)." Vehicles 3, no. 3 (August 13, 2021): 533–44. http://dx.doi.org/10.3390/vehicles3030032.

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In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs.
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Park, Suyong, Sanghyeon Nam, Gokul S. Sankar, and Kyoungseok Han. "Evaluating the Efficiency of Connected and Automated Buses Platooning in Mixed Traffic Environment." Electronics 11, no. 19 (October 8, 2022): 3231. http://dx.doi.org/10.3390/electronics11193231.

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Due to the battery capacity limitation of battery electric vehicles (BEVs), the importance of minimizing energy consumption has been increasing in recent years. In the mean time, for improving vehicle energy efficiency, platooning has attracted attention of several automakers. Using the connected and automated vehicles (CAVs) technology, platooning can achieve a longer driving range while preserving a closer distance from the preceding vehicle, resulting in the minimization of the aerodynamic force. However, undesired behaviors of human-driven vehicles (HVs) in the platooning group can prohibit the maximization of the energy efficiency. In this paper, we developed a speed planner based on the model predictive control (MPC) to minimize the total platooning energy consumption, and HVs were programmed to maintain a long enough distance from the preceding vehicle to avoid collision. The simulations were performed to determine how HV influences the efficiencies of the platooning group, which is composed of CAVs and HVs together, in several scenarios including the different positions and numbers of the HVs. Test results show that the CAVs planned by our approach reduces energy consumption by about 4% or more than 4% compared to that of the HVs.
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Pribyl, Ondrej. "Effects of Connected and Automated Vehicles in a Cooperative Environment." Journal für Mobilität und Verkehr, no. 6 (November 10, 2020): 21–28. http://dx.doi.org/10.34647/jmv.nr6.id45.

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Cooperative and automated vehicles (CAVs) are often considered a mean to improve quality of life in cities, the traffic flow parameters in particular. This paper provides some evidence based on microscopic traffic simulation on how the effects can really be. Important is that the particular use cases are not built in vehicles only. We focus on so called cooperative environment and advanced traffic control measures.This paper describes the impact of CAVs on a cooperative urban environment, resulting from a European research project - MAVEN. We clearly demonstrate that a proper integration of CAVs into city traffic management can, for example, help with respect to the environmental goals and reduce CO2 emissions by up to 12 % (a combination of GLOSA and signal optimization). On corridors with a green wave, a capacity increase of up to 34% was achieved. Already for lower penetra- tion rates (20% penetration of CAVs), there are significant improvements in traffic performance. For example, platooning leads to a decrease of CO2 emissions of 2,6 % or an impact indicator by 17,7%.
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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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Eziama, Elvin, Faroq Awin, Sabbir Ahmed, Luz Marina Santos-Jaimes, Akinyemi Pelumi, and Danilo Corral-De-Witt. "Detection and Identification of Malicious Cyber-Attacks in Connected and Automated Vehicles’ Real-Time Sensors." Applied Sciences 10, no. 21 (November 4, 2020): 7833. http://dx.doi.org/10.3390/app10217833.

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Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of 7.95%, 9%, 8.77% and 7.33%, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are 5%, 7.9%, 7.54% and 4.1% respectively.
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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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Dissertations / Theses on the topic "Connected and Automated Vehicles (CAVs)"

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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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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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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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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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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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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.
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Almobayedh, Hamad Bader. "Simulation of the Impact of Connected and Automated Vehicles at a Signalized Intersection." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1557207826602638.

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El-Dabaja, Sarah S. "Drivers of "Driverless" Vehicles: A Human Factors Study of Connected and Automated Vehicle Technologies." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1576670482075765.

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Gupta, Shobhit. "Look-Ahead Optimization of a Connected and Automated 48V Mild-Hybrid Electric Vehicle." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478434629481.

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Books on the topic "Connected and Automated Vehicles (CAVs)"

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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 and Automated Vehicles (CAVs)"

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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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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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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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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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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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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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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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Conference papers on the topic "Connected and Automated Vehicles (CAVs)"

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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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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 Copyright Page." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887804.

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"[CAVS 2019 Title Page]." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887823.

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"CAVS 2019 Welcome from the VTS President." In 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS). IEEE, 2019. http://dx.doi.org/10.1109/cavs.2019.8887796.

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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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Reports on the topic "Connected and Automated Vehicles (CAVs)"

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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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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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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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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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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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Li, Lingxi, Yaobin Chen, Renren Tian, Feng Li, Howell Li, and James R. Sturdevant. An Integrated Critical Information Delivery Platform for Smart Segment Dissemination to Road Users. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317440.

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An integrated critical information delivery platform for smart segment dissemination to road users was developed. A statewide baseline milepost geodatabase was created at 0.1-mile resolution with tools, protocols, and interfaces that allow other data sources to be efficiently utilized. A variety of data sources (e.g., INRIX, CARS, Doppler, camera images, connected vehicle data, automated vehicle location) were integrated into existing and new dashboards for stakeholders to monitor roadway conditions and after-action reviews. Additionally, based on these data sources, algorithms were developed and an API was created to identify hazardous road conditions when the location of the end-user mobile device was given. Message delivery schemes were successfully implemented to issue alerts to drivers, which were integrated with two in-vehicle smartphone applications. The performance of the integrated platform was evaluated using both the driving simulator and a number of simulated and on-road tests. The results demonstrated the system was able to disseminate data in real-time using the developed platform.
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