Journal articles on the topic 'Connected and Automated Vehicles (CAVs)'
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1
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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Shevchenko, Olga. "Connected Automated Driving: Civil Liability Regulation in the European Union." Teisė 114 (April 5, 2020): 85–102. http://dx.doi.org/10.15388/teise.2020.114.5.
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The aim of this article is to provide with the option of civil liability regulation of connected autonomous vehicles (CAVs) and autonomous vehicles (AVs) at the European Union level in the light of introduction of Connected Automated Driving (CAD) on the common market.
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Wang, Yunze, Ranran Xu, and Ke Zhang. "A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics." Sustainability 14, no. 17 (September 3, 2022): 11010. http://dx.doi.org/10.3390/su141711010.
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Autonomous driving technology and vehicle-to-vehicle communication technology make the hybrid driving of connected and automated vehicles (CAVs) and regular vehicles (RVs) a long-existing phenomenon in the coming future. Among the existing studies, IDM models are mostly used to study the performance of homogeneous traffic flow. To explore the stability of mixed traffic flow, an extended intelligent driver model (IDM) based car-following model was proposed for mixed traffic flow (MTF) with both CAVs and RVs, considering the headway, the speed and acceleration of multiple front vehicles, as well as the response characteristics of RV drivers. Through the linear stability analysis, the criterion for the stability of MTFs was derived, and the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF are discussed. Based on the above theoretical model, a numerical simulation was conducted in two typical scenarios of starting and braking. The results showed that, at the microscopic scale, the vehicle in the Cooperative Adaptive Cruise Control (CACC) mode could significantly decelerate in response to the interference from other vehicles in the same traffic environment. At the macroscopic scale, as the penetration rate of CAVs increased, the overall acceleration fluctuation of the traffic flow decreased. At the same penetration rate of CAVs, the higher density of CAVs coincided with the higher stability of the MTF. When the penetration rate of CAVs was 50%, the degree of distribution had the greatest impact on the MTF. When the penetration rate of CAVs exceeded 70%, the degree of distribution had little impact on the MTF. This research can provide basic theoretical support for the management and control of MTF in the future.
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Sobanjo, John O. "Civil Infrastructure Management Models for the Connected and Automated Vehicles Technology." Infrastructures 4, no. 3 (August 7, 2019): 49. http://dx.doi.org/10.3390/infrastructures4030049.
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The new concept of Connected and Automated Vehicles (CAVs) necessitates a need to review the approach of managing the existing civil infrastructure system (highways, bridges, sign structures, etc.). This paper provides a basic introduction to the CAV concept, assesses the infrastructure requirements for CAVs, and identifies the appropriateness of the existing infrastructure, and needs, in terms of the condition assessment and deterioration modeling. With focus on the Vehicle-to-Infrastructure (V2I) requirements for CAVs, the main elements required on the infrastructure are the Roadside Units (RSUs), which are primarily for communication; they are similar to non-structural transportation assets, such as traffic signals, signs, etc. The ongoing pertinent efforts of agencies and the private industry are reviewed, including the V2I Deployment Coalition (American Association of State Transportation Officials (AASHTO), the Institute of Transportation Engineers (ITE), and the Intelligent Transportation Society of America (ITS America)). Current methods of transportation asset management, particularly, of non-structural elements, are also reviewed. Two reliability-based models were developed and demonstrated for the deterioration of RSUs, including the age replacement model, and a combined survivor function considering the vulnerability of the CAV elements to natural hazards, such as the hurricanes. The paper also discusses the implications of the CAV technology on traffic models, particularly, how it affects user costs’ computations.
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Lee, Geonil, and Jae-il Jung. "Decentralized Platoon Join-in-Middle Protocol Considering Communication Delay for Connected and Automated Vehicle." Sensors 21, no. 21 (October 27, 2021): 7126. http://dx.doi.org/10.3390/s21217126.
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Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.
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Utriainen, Roni, and Markus Pöllänen. "The Needed Features of Connected and Automated Vehicles to Prevent Passenger Car Crashes Caused by Driving Errors." Future Transportation 1, no. 2 (September 3, 2021): 370–86. http://dx.doi.org/10.3390/futuretransp1020021.
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Connected and automated vehicles (CAVs) can enhance traffic safety considerably. However, as CAVs are currently under development, the safety impact cannot be assessed directly. In this study, driver-managed passenger car crashes with fatalities in Finland were investigated qualitatively to evaluate the needed features of the CAVs to avoid these crashes. The focus was on single-car crashes and collisions between passenger cars, in which the immediate risk factor was a driving error (n = 48). Most of the analysed crashes (33 of 48) were due to loss of control with typically adverse weather or road conditions. To avoid these crashes, a CAV should be able to adjust its speed according to the conditions. In 13 of 48 crashes, the car was under control prior to the crash. A reliable capability to recognize other road users is an important CAV feature, because observational errors were common in these cases. In addition, communication between the vehicles could assist in avoiding intersection crashes and crashes caused by a sudden change in weather conditions. This study increases knowledge on crashes related to driving errors and the needed features of CAVs to avoid these crashes. In particular, CAVs’ feature to adjust the speed is important, because cases of loss of control in adverse weather or road conditions were typical events.
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Molinaro, Antonella, Claudia Campolo, Jérôme Härri, Christian Esteve Rothenberg, and Alexey Vinel. "5G-V2X Communications and Networking for Connected and Autonomous Vehicles." Future Internet 12, no. 7 (July 8, 2020): 116. http://dx.doi.org/10.3390/fi12070116.
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In the last decade, there has been a surge in interest in connected and automated vehicles (CAVs) and related enabling technologies in the fields of communication, automation, computing, sensing, and positioning [...]
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Ding, Fan, Jiwan Jiang, Yang Zhou, Ran Yi, and Huachun Tan. "Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon." Sustainability 12, no. 23 (November 28, 2020): 9955. http://dx.doi.org/10.3390/su12239955.
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With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.
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Wu, Yuanyuan, and Feng Zhu. "Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?" Sustainability 13, no. 16 (August 23, 2021): 9482. http://dx.doi.org/10.3390/su13169482.
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The concept of signal-free management at road junctions is tailored for Connected and Automated Vehicles (CAVs), in which the conventional signal control is replaced by various right-of-way assignment policies. First-Come-First-Served (FCFS) is the most commonly used policy. In most proposed strategies, although the traffic signals are replaced, the organization of vehicle trajectory remains the same as that of traffic lights. As a naturally signal-free strategy, roundabout has not received enough attention. A key motivation of this study is to theoretically compare the performance of signalized intersection (I-Signal), intersection using FCFS policy (I-FCFS), roundabout using the typical major-minor priority pattern (R-MM), and roundabout adopting FCFS policy (R-FCFS) under pure CAVs environment. Queueing theory is applied to derive the theoretical formulas of the capacity and average delay of each strategy. M/G/1 model is used to model the three signal-free strategies, while M/M/1/setup model is used to capture the red-and-green light switch nature of signal control. The critical safety time gaps are the main variables and are assumed to be generally distributed in the theoretical derivation. Analytically, I-Signal has the largest capacity benefiting from the ability to separate conflict points in groups, but in some cases it will have higher delay. Among the other three signal-free strategies, R-FCFS has the highest capacity and the least average control delay, indicating that the optimization of signal-free management of CAVs based on roundabout setting is worthy of further study.
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Raptis, George E., Christina Katsini, Christos Alexakos, Athanasios Kalogeras, and Dimitrios Serpanos. "CAVeCTIR: Matching Cyber Threat Intelligence Reports on Connected and Autonomous Vehicles Using Machine Learning." Applied Sciences 12, no. 22 (November 16, 2022): 11631. http://dx.doi.org/10.3390/app122211631.
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Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract malicious activities that jeopardize security and safety dimensions. The cybersecurity systems of CAVs detect such activities, collect and analyze related information during and after the activity, and use cyber threat intelligence (CTI) to organize this information. Considering that CTI collected from various malicious activities may share common characteristics, it is critical to provide the cybersecurity stakeholders with quick and automatic ways of analysis and interrelation. This aims to help them perform more accurate and effective forensic investigations. To this end, we present CAVeCTIR, a novel approach that finds similarities between CTI reports that describe malicious activities detected on CAVs. CAVeCTIR uses advanced machine learning techniques and provides a quick, automated, and effective solution for clustering similar malicious activities. We applied CAVeCTIR in a series of experiments investigating almost 3000 malicious activities in simulation, real-world, and hybrid CAV environments, covering seven critical cyber-attack scenarios. The results showed that the DBSCAN algorithm identified seven no-overlapping core clusters characterized by high density. The results indicated that cybersecurity stakeholders could take advantage of CAVeCTIR by adopting the same or similar methods to analyze newly detected malicious activity, speed up the attack attribution process, and perform a more accurate forensics investigation.
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Jin, Yuting, Zhihong Yao, Jiazhe Han, Lu Hu, and Yangsheng Jiang. "Variable Cell Transmission Model for Mixed Traffic Flow with Connected Automated Vehicles and Human-Driven Vehicles." Journal of Advanced Transportation 2022 (April 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/6342857.
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The current research on the mixed traffic flow characteristics of human-driven vehicles (HDVs) and connected automated vehicles (CAVs) mainly focuses on the micro-level. To study the characteristics of the mixed traffic flow from the medium and macro level, this paper proposes a variable cell transmission model (VCTM). First, the fundamental diagram is introduced based on the phenomena of hysteresis of traffic flow. Second, the VCTM with different market penetration rates (MPR) of CAVs is proposed based on the classical cell transmission model (CTM). Then, the effectiveness of VCTM is verified by micro-simulation based on the intelligent driver model (IDM). Finally, some congestion indexes are selected to discuss the characteristics of mixed traffic flow based on the VCTM with an expressway simulation. The results show that the traffic capacity and congestion dissipation capacity gradually are increased with the increase of MPR of CAVs. The homogeneous CAVs traffic flow capacity can reach 1.41 times that of the homogeneous HDVs traffic flow, and the congestion dissipation time can be reduced by 25%. The larger MPR is, the greater the improvement effect is. In addition, compared with CTM, VCTM can reflect the delay, queuing, and dissipation of mixed traffic flow more accurately, which is helpful to capture the evolution mechanism of mixed traffic flow in the future.
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Tumminello, Maria Luisa, Elżbieta Macioszek, Anna Granà, and Tullio Giuffrè. "Simulation-Based Analysis of “What-If” Scenarios with Connected and Automated Vehicles Navigating Roundabouts." Sensors 22, no. 17 (September 3, 2022): 6670. http://dx.doi.org/10.3390/s22176670.
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Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.
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Zhao, Zhouqiao, Guoyuan Wu, and Matthew Barth. "Corridor-Wise Eco-Friendly Cooperative Ramp Management System for Connected and Automated Vehicles." Sustainability 13, no. 15 (July 31, 2021): 8557. http://dx.doi.org/10.3390/su13158557.
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Safety, mobility, and environmental sustainability are three fundamental issues that our transportation system has been confronting for decades. Intelligent transportation systems (ITS) aim to address these problems by leveraging disruptive technologies, such as connected and automated vehicles (CAVs). The cooperative potential of CAVs enable more efficient maneuvers and operation of a group of vehicles, or even the entire traffic system. In addition, CAVs may couple with other emerging technologies such as electrification to boost overall system performance and to further mitigate the aforementioned issues. In this study, we propose a hierarchical eco-friendly cooperative ramp management system, where macroscopically, a stratified ramp metering algorithm, is deployed to coordinate all of the ramp inflow rates along a corridor according to the real-time traffic condition; microscopically, a model predictive control (MPC)-based algorithm is designed for the detailed speed control of individual CAVs. Using the shared information from CAVs, the proposed ramp management system can smooth traffic flow, improve system mobility, and decrease the energy consumption of the network. Moreover, traffic simulation has been conducted using PTV VISSIM under various congestion levels for vehicles with different powertrain types, i.e., an internal combustion engine and an electric motor. Compared to conventional ramp metering, the proposed ramp management system may improve mobility by 48.6–56.7% and save energy by 24.0–35.1%. Compared to no control scenarios, savings in travel time and energy consumption are in the ranges of 79.4–89.1% and 0.8–2.5%, respectively.
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Amini, Mohammad Reza, Yiheng Feng, Zhen Yang, Ilya Kolmanovsky, and Jing Sun. "Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (August 20, 2020): 17–29. http://dx.doi.org/10.1177/0361198120941508.
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Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.
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Karbasi, Amirhosein, and Steve O’Hern. "Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic." Future Transportation 2, no. 1 (January 4, 2022): 24–40. http://dx.doi.org/10.3390/futuretransp2010002.
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Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.
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Li, Chenghao, Zhiqun Hu, Zhaoming Lu, and Xiangming Wen. "Cooperative Intersection with Misperception in Partially Connected and Automated Traffic." Sensors 21, no. 15 (July 23, 2021): 5003. http://dx.doi.org/10.3390/s21155003.
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The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.
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Zhao, Bin, Yalan Lin, Huijun Hao, and Zhihong Yao. "Fuel Consumption and Traffic Emissions Evaluation of Mixed Traffic Flow with Connected Automated Vehicles at Multiple Traffic Scenarios." Journal of Advanced Transportation 2022 (January 12, 2022): 1–14. http://dx.doi.org/10.1155/2022/6345404.
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To analyze the impact of different proportions of connected automated vehicles (CAVs) on fuel consumption and traffic emissions, this paper studies fuel consumption and traffic emissions of mixed traffic flow with CAVs at different traffic scenarios. Firstly, the car-following modes and proportional relationship of vehicles in the mixed traffic flow are analyzed. On this basis, different car-following models are applied to capture the corresponding car-following modes. Then, Virginia Tech microscopic (VT-micro) model is adopted to calculate the instantaneous fuel consumption and traffic emissions. Finally, based on three typical traffic scenarios, a basic segment with bottleneck zone, ramp of the freeway, and signalized intersection, a simulation platform is built based on Python and SUMO to obtain vehicle trajectory data, and the fuel consumption and traffic emissions in different scenarios are obtained. The results show that (1) In different traffic scenarios, the application of CAVs can reduce fuel consumption and traffic emissions. The higher the penetration rate, the more significant the reduction in fuel consumption and traffic emissions. (2) In the three typical traffic scenarios, the advantages of CAVs are more evident in the signalized intersection. When the penetration rate of CAVs is 100%, the fuel consumption and traffic emissions reduction ratio is as high as 32%. It is noteworthy that the application of CAVs in urban transportation will significantly reduce fuel consumption and traffic emissions.
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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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Zou, Yun, and Xiaobo Qu. "On the impact of connected automated vehicles in freeway work zones: a cooperative cellular automata model based approach." Journal of Intelligent and Connected Vehicles 1, no. 1 (February 5, 2018): 1–14. http://dx.doi.org/10.1108/jicv-11-2017-0001.
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Purpose Freeway work zones have been traffic bottlenecks that lead to a series of problems, including long travel time, high-speed variation, driver’s dissatisfaction and traffic congestion. This research aims to develop a collaborative component of connected and automated vehicles (CAVs) to alleviate negative effects caused by work zones. Design/methodology/approach The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations. Findings Simulation results show that, with the proposed component and penetration of CAVs, the average performances (travel time, safety and emission) can all be improved and the stochasticity of performances will be minimized too. Originality/value To the best of the authors’ knowledge, this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance.
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Bakibillah, A. S. M., Md Abdus Samad Kamal, Chee Pin Tan, Susilawati Susilawati, Tomohisa Hayakawa, and Jun-ichi Imura. "Bi-Level Coordinated Merging of Connected and Automated Vehicles at Roundabouts." Sensors 21, no. 19 (September 30, 2021): 6533. http://dx.doi.org/10.3390/s21196533.
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Traditional uncoordinated traffic flows in a roundabout can lead to severe traffic congestion, travel delay, and the increased fuel consumption of vehicles. An interesting way to mitigate this would be through cooperative control of connected and automated vehicles (CAVs). In this paper, we propose a novel solution, which is a roundabout control system (RCS), for CAVs to attain smooth and safe traffic flows. The RCS is essentially a bi-level framework, consisting of higher and lower levels of control, where in the higher level, vehicles in the entry lane approaching the roundabout will be made to form clusters based on traffic flow volume, and in the lower level, the vehicles’ optimal sequences and roundabout merging times are calculated by solving a combinatorial optimization problem using a receding horizon control (RHC) approach. The proposed RCS aims to minimize the total time taken for all approaching vehicles to enter the roundabout, whilst minimally affecting the movement of circulating vehicles. Our developed strategy ensures fast optimization, and can be implemented in real-time. Using microscopic simulations, we demonstrate the effectiveness of the RCS, and compare it to the current traditional roundabout system (TRS) for various traffic flow scenarios. From the results, we can conclude that the proposed RCS produces significant improvement in traffic flow performance, in particular for the average velocity, average fuel consumption, and average travel time in the roundabout.
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Ahmed, Shofiq, Kakan Dey, and Ryan Fries. "Evaluation of Transportation System Resilience in the Presence of Connected and Automated Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 9 (May 9, 2019): 562–74. http://dx.doi.org/10.1177/0361198119848702.
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Large-scale natural disasters challenge the resilience of the surface transportation system. The objective of this research was to develop a resilience model of the surface transportation system with a mixed-traffic environment and considering varying Connected and Automated Vehicle (CAV) penetration scenarios. As deployment of CAVs is expected to improve traffic operations, a resilience model was developed in this research to evaluate the resilience performance of a transportation system with several CAV penetration levels (0%, 25%, 50%, 75%, and 100%) for a given budget and recovery time. The proposed resilience quantification model was applied on a roadway network considering several disaster scenarios. The network capacity in relation to trips at any phase of disaster was compared with the pre-disaster trips to determine the system resilience. The capacity variation and the travel time variation were also estimated. The analysis showed that the resilience of the transportation system improved with CAVs in relation to travel time and capacity improvement. Link travel times were significantly improved by higher CAV penetration rate. The findings also suggested that higher penetration of CAVs (i.e., 50% or more) increased the recovery costs. For example, the recovery costs needed for medium and large-scale disasters were 50% and 90% higher, respectively, compared with the recovery costs for a small-scale disaster. These higher costs were primarily for the repair and replacement of intelligent infrastructure required to support the operation of CAVs.
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Shi, Yanjun, Zhiheng Yuan, Hao Yu, Yijia Guo, and Yuhan Qi. "A Graph-Based Optimal On-Ramp Merging of Connected Vehicles on the Highway." Machines 9, no. 11 (November 16, 2021): 290. http://dx.doi.org/10.3390/machines9110290.
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Connected and automated vehicles (CAVs) are a very promising alternative for reducing fuel consumption and improving traffic efficiency when vehicles merge at on-ramps. In this study, we propose a graph-based method to coordinate CAVs to merge at the highway ramp. First, the optimized vehicles were divided into groups to pass the merging point. Then we built a directed graph model for each group of vehicles, where each path of the graph corresponds to one of all possible merging sequences. The improved shortest path algorithm is proposed to find the optimal merging sequence for minimizing total fuel consumption. The results of the simulation showed that the proposed graph-based method reduced fuel consumption and ensured high traffic efficiency; moreover, the vehicles can form a platoon after passing the merge point.
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Wu, Renfei, Linheng Li, Wenqi Lu, Yikang Rui, and Bin Ran. "Improving the Safe Operation of Platoon Lane Changing for Connected Automated Vehicles: A Novel Field-Driven Approach." Applied Sciences 11, no. 16 (August 8, 2021): 7287. http://dx.doi.org/10.3390/app11167287.
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Connected and automated vehicles (CAVs) platoons have been widely researched because of their efficiency advantages. However, most studies mainly focus on the stability control of platoon and there is a lack of in-depth consideration of platoon lane changing. In order to make up for this vacancy, this study focused on the dynamic gap in the platoon lane changing process. First, an intra-platoon potential field of vehicles in the platoon was established by combining the repulsive force under vehicle safety and the gravity inside the platoon, which can effectively characterize the risk distribution around vehicles. Second, the platoon lane changing process was designed and critical distances of platoon vehicles under different conflict situations were analyzed. Based on this, this study proposed a critical distance model of platoon lane changing. Furthermore, we also found that the critical distances for platoon lane changing were within an interval with upper and lower bounds, which was different from the minimum distance of non-platoon vehicles. Finally, experiments were conducted and the results showed that the proposed model could effectively represent the relationship between the distance between vehicles in the platoon and the motion state of the surrounding vehicles. Moreover, the proposed method could also be applied to the lane-changing maneuver of a self-organizing platoon at a strategic level in a CAVs system.
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Li, Duo, and Peter Wagner. "A Novel Approach for Mixed Manual/Connected Automated Freeway Traffic Management." Sensors 20, no. 6 (March 22, 2020): 1757. http://dx.doi.org/10.3390/s20061757.
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Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates.
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Zhang, Yuanhao, and Jiabao Zhao. "A Novel Coordination Mechanism for Connected and Automated Vehicles in the Multi-Intersection Road Network." Energies 15, no. 14 (July 16, 2022): 5168. http://dx.doi.org/10.3390/en15145168.
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In recent years, connected automated vehicles (CAVs) have attracted much attention, and the coordination strategy of CAVs in isolated intersections has been widely discussed. However, these algorithms for isolated intersections cannot be directly applied in a multi-intersection road network (MiRN). The coordination strategy in the MiRN requires further investigation. This paper proposes a two-tier strategy for CAV coordination in the MiRN. First, we analyze the coordination problem in isolated intersections and formulate it as a mixed-integer programming problem. Then, for the MiRN, we propose a consensus prediction method to estimate the travel time for CAVs with different paths. Finally, a novel coordination approach is given, showing how to determine the optimal path for CAVs. The experimental results demonstrate the efficiency of the proposed strategy under various traffic flow rates. Compared with the fixed signal time assignment method and the actuated signal time assignment method, our method reduces the average travel time by about 74–83% under different flow rates. We also evaluate the impact of parameters on the strategy’s performance and provide some suggestions for setting these parameters.
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An, Hongil, and Jae-il Jung. "Design of a Cooperative Lane Change Protocol for a Connected and Automated Vehicle Based on an Estimation of the Communication Delay." Sensors 18, no. 10 (October 17, 2018): 3499. http://dx.doi.org/10.3390/s18103499.
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Connected and automated vehicles (CAVs) have recently attracted a great deal of attention. Various studies have been conducted to improve vehicle and traffic safety through vehicle to vehicle (V2V) communication. In the field of CAVs, lane change research is considered a very challenging subject. This paper presents a cooperative lane change protocol, considering the impact of V2V communication delay. When creating a path for a lane change in the local path planning module, V2V communication delay occurs. Each vehicle was represented, in our study, by an oriented bounding box (OBB) to determine the risk of collision. We set up a highway driving simulation environment and verified the improved protocol by implementing a longitudinal and lateral controller.
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Chen, Na, Meng Wang, Tom Alkim, and Bart van Arem. "A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays." Journal of Advanced Transportation 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/9852721.
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Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
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Zhong, Zijia, Joyoung Lee, and Liuhui Zhao. "Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicles in Mixed Traffic Conditions." Journal of Advanced Transportation 2021 (January 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/8816540.
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Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.
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Zhou, Zhaoming, Jianbo Yuan, Shengmin Zhou, Qiong Long, Jianrong Cai, and Lei Zhang. "Characteristics Analysis and Equilibrium Optimization of Mixed Traffic Flow considering Connected Automated and Human-Driven Vehicles." Journal of Advanced Transportation 2022 (August 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/3866042.
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Considering the impact of informatization condition, vehicles on the road network are divided into connected automated vehicles (CAVs) and human-driven vehicles (HDVs), which follow the principle of system optimization and stochastic user equilibrium, respectively. Taking the road network reserve capacity maximization model under the condition of road capacity constraint as the upper-level programming and the traffic assignment model under heterogeneous flow environment as the lower level programming, then a bilevel programming model is constructed. Among them, the nonuniform demand growth multiplier is adopted for each OD pair to reflect the inconsistency of traffic demand structure growth, and the calculation of link capacity is related to the market penetration of CAVs. The incremental method, method of successive averages, and simulated annealing algorithm are used to solve the model, and the effects of different market penetration on road network capacity, travel time, and saturation are analyzed through a numerical example. The relevant data under different weights are normalized and the optimal deployment scheme of CAVs and HDVs in different periods is obtained by comprehensive evaluation. Meanwhile, the mixed equilibrium flow state is explored under the premise of given market penetration to verify the feasibility of the model and algorithm.
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Ahmed, Hafiz Usman, Ying Huang, Pan Lu, and Raj Bridgelall. "Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview." Smart Cities 5, no. 1 (March 17, 2022): 382–404. http://dx.doi.org/10.3390/smartcities5010022.
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The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
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Li, Ye, Yuntao Shi, Jaeyoung Lee, Chen Yuan, and Baojie Wang. "Safety Effects of Connected and Automated Vehicle-Based Variable Speed Limit Control near Freeway Bottlenecks considering Driver’s Heterogeneity." Journal of Advanced Transportation 2022 (January 30, 2022): 1–16. http://dx.doi.org/10.1155/2022/7996623.
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A control strategy using variable speed limit (VSL) is a proven solution to reduce freeway collision risks and improve safety. However, the heterogeneity of human drivers restricts the effectiveness of traditional VSL controls, which may be made up by recent advanced technologies of connected and automated vehicles (CAVs). This study aims to propose a CAV-based VSL control system to address the limitations caused by human drivers on VSL control’s effectiveness. First, the heterogeneity of human drivers is analyzed, and its impact on the safety performance of VSL is examined. Specifically, a microscopic simulation platform is established, and two vehicle dynamic models developed for CAVs and human-driven vehicles (HDVs) are incorporated into the simulation platform. Based on a widely utilized surrogate safety measurement, time-to-collision, its derivative metrics are applied to evaluate collision risks, and the total travel time is used to assess operational efficiency. Extensive simulations are conducted to examine the performance of the proposed CAV-VSL system. The results indicate the following: (1) the heterogeneity of human drivers negatively affects the performance of the VSL; (2) the performance of the proposed control system in a mixed flow can be improved by advanced wireless communication technology; (3) CAVs are able to implement the VSL control strategy effectively resulting in the proactive reduction of the heterogeneity.
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Liu, Shaojie, and Wei David Fan. "Evaluating the Performance of Connected and Automated Vehicles in Fixed Signal-Controlled Conventional Intersections and Superstreets with Platooning-Based Trajectory Planning." Journal of Advanced Transportation 2022 (June 2, 2022): 1–18. http://dx.doi.org/10.1155/2022/6093217.
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Connected and autonomous vehicles (CAVs) are emerging technology that attracts the interests of many transportation professionals and computational scientists. Several recent studies have investigated different model frameworks of CAVs in different transportation environments, such as on freeways and at conventional intersections. Nevertheless, few efforts have been made to investigate the performances of CAVs at innovative intersections, and the lack of knowledge can result in an inaccurate prediction of CAVs performances in the existing transportation network. This research intends to mitigate this research gap by studying the traffic delay and fuel consumption of CAVs in the environment of the superstreet and its equivalent conventional intersection through simulation-based experiments. A real-world superstreet in Leeland, NC, is selected and used. A conventional intersection with equivalent road designs is established in the simulation platform to make a comparison with the selected superstreet. This research develops both platooning and trajectory planning modeling frameworks to examine the implications of CAVs with different capabilities. The Intelligent Driver Model (IDM) is selected and applied to model the CAV behaviors, while Wiedemann 99 (W99) is used to model Human-Driven Vehicles (HDVs). The simulation results demonstrate the efficiency of both platooning and trajectory planning, respectively. Different effects of CAVs in the superstreet and its equivalent conventional intersection are observed. The findings from this research can provide an important reference for transportation planners and policymakers in predicting the influence of CAVs on the existing transportation infrastructure.
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Wu, Yunxia, Yalan Lin, Rong Hu, Zilan Wang, Bin Zhao, and Zhihong Yao. "Modeling and Simulation of Traffic Congestion for Mixed Traffic Flow with Connected Automated Vehicles: A Cell Transmission Model Approach." Journal of Advanced Transportation 2022 (June 27, 2022): 1–20. http://dx.doi.org/10.1155/2022/8348726.
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Connected automated vehicles (CAVs) can significantly shorten the headway of car following, thereby effectively improving the traffic capacity and injecting new power to alleviate traffic congestion. To investigate the congestion characteristics of mixed traffic flow with CAVs and human-driven vehicles (HDVs), this paper proposes a cell transmission model to capture and simulate traffic congestion for mixed traffic flow. Firstly, the Newell, adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC) models are adopted to capture the car-following behavior of different vehicles. Secondly, the fundamental diagram under different penetration rates of CAVs is derived based on car-following models. Then, the cell transmission model (CTM) of mixed traffic flow is developed based on the classical CTM and fundamental diagram of mixed traffic flow. Finally, two simulation methods, mixed traffic flow CTM and micro-simulation, are designed to verify the effectiveness of the proposed model. Moreover, taking the moving bottleneck on the expressway as an example, the congestion characteristics of mixed traffic flow are analyzed using multiple indexes, such as average travel speed, congestion delay, and congestion scale. The results show the following: (i) CAVs can significantly alleviate traffic congestion, (ii) the duration of the bottleneck is positively correlated with the degree of traffic congestion, and (iii) The traffic congestion assessment results under different model parameters slightly differ, but the impact is negligible.
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Raju, Narayana, and Haneen Farah. "Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles." Journal of Advanced Transportation 2021 (September 24, 2021): 1–29. http://dx.doi.org/10.1155/2021/2444363.
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Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.
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Min, Haitao, Xiaoyong Xiong, Fang Yang, Weiyi Sun, Yuanbin Yu, and Pengyu Wang. "An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning." Machines 11, no. 2 (January 26, 2023): 168. http://dx.doi.org/10.3390/machines11020168.
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The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the vehicle trajectory is conducive to improving the energy efficiency of the vehicle. Therefore, an energy-efficient driving method for CAVs based on reinforcement learning is proposed in this paper. Firstly, a set of vehicle trajectory prediction models based on long and short-term memory (LSTM) neural networks are developed, which integrate driving intention prediction and lane change time prediction to improve the prediction accuracy of surrounding vehicle trajectories. Secondly, an energy-efficient driving model is built based on Proximity Policy Optimization (PPO) reinforcement learning. The model takes the current states and predicted trajectories of surrounding vehicles as input information, and outputs energy-saving control variables while taking into account various constraints, such as safety, comfort, and travel efficiency. Finally, the method is tested by simulation on the NGSIM dataset, and the results show that the proposed method can save energy consumption by 9–22%.
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Sakaguchi, Yuta, A. S. M. Bakibillah, Md Abdus Samad Kamal, and Kou Yamada. "A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways." Sensors 23, no. 2 (January 5, 2023): 611. http://dx.doi.org/10.3390/s23020611.
Full textAbstract:
Uncoordinated driving behavior is one of the main reasons for bottlenecks on freeways. This paper presents a novel cyber-physical framework for optimal coordination of connected and automated vehicles (CAVs) on multi-lane freeways. We consider that all vehicles are connected to a cloud-based computing framework, where a traffic coordination system optimizes the target trajectories of individual vehicles for smooth and safe lane changing or merging. In the proposed framework, the vehicles are coordinated into groups or platoons, and their trajectories are successively optimized in a receding horizon control (RHC) approach. Optimization of the traffic coordination system aims to provide sufficient gaps when a lane change is necessary while minimizing the speed deviation and acceleration of all vehicles. The coordination information is then provided to individual vehicles equipped with local controllers, and each vehicle decides its control acceleration to follow the target trajectories while ensuring a safe distance. Our proposed method guarantees fast optimization and can be used in real-time. The proposed coordination system was evaluated using microscopic traffic simulations and benchmarked with the traditional driving (human-based) system. The results show significant improvement in fuel economy, average velocity, and travel time for various traffic volumes.
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Deng, Zhiyun, Yanjun Shi, Qiaomei Han, Lingling Lv, and Weiming Shen. "A Conflict Duration Graph-Based Coordination Method for Connected and Automated Vehicles at Signal-Free Intersections." Applied Sciences 10, no. 18 (September 8, 2020): 6223. http://dx.doi.org/10.3390/app10186223.
Full textAbstract:
Previous studies on Connected and Automated Vehicles (CAVs) demonstrated the potential to coordinate the behaviors of multiple connected vehicles for traffic improvements. In this paper, we first propose a Conflict Duration Graph-based (CDG-based) coordination framework to resolve collisions and improve the traffic capacity of signal-free intersections. Secondly, a Speed Control-based Intersection Coordination Model (SICM) is developed to identify complex constraints in multi-vehicle collision scenarios. Thirdly, a geometric Translation-based Intersection Coordination Algorithm (TICA) is proposed to calculate the ideal location of time blocks in CDGs and then obtain the near-optimal design speed in the form of combinatorial optimization. Twelve groups of test scenarios with different traffic volumes were designed and tested on a MATLAB-based simulation platform. Simulation results showed that the proposed method can resolve all the collisions and instruct the vehicles to pass signal-free intersections collaboratively without stopping in low to medium level of congestion.
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Alanazi, Fayez, Ping Yi, and Gehawi El. "Improving the performance of unsignalized t-intersections within CAVs mixed traffic." Journal of Applied Engineering Science 20, no. 2 (2022): 464–76. http://dx.doi.org/10.5937/jaes0-34023.
Full textAbstract:
The rapid growth in population and the increase in the number of vehicles on the road have resulted in severe traffic congestion over the last two decades. However, intersections, where different flows intersect, are among the major cause of traffic congestion besides bottlenecks. Past decades have seen major technological advancements in road vehicles aimed at making vehicles traveling securely and comfortably. Current connected and automated vehicles (CAV) are packed with lane-keeping assistance and adaptive cruise control to ensure that vehicles do not collide and reduce traffic congestion. In this research, we developed a control algorithm that utilizes CAVs to help generate additional usable gaps for the minor road vehicles to enter the intersection without affecting the mainline traffic flow. Simulation results showed that the delay and queue length of the minor road approach is minimized without causing a significant delay to the mainline. The minor road delay was reduced by 72% when the percentage of CAVs on the major road is 70% compared to the benchmark with no CAVs on the major road.
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Shay, Elizabeth, Asad J. Khattak, and Behram Wali. "Walkability in the Connected and Automated Vehicle Era: A U.S. Perspective on Research Needs." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 35 (July 13, 2018): 118–28. http://dx.doi.org/10.1177/0361198118787630.
Full textAbstract:
Walkability and walking activity are of interest to planners, engineers, and health practitioners for their potential to improve safety, promote environmental and public health, and increase social equity. Connected and automated vehicles (CAVs) will reshape the built environment, mobility, and safety in ways we cannot know with certainty—but which we may anticipate will change the meaning of “walkability.” The CAV era may provide economic, environmental, and social benefits, while potentially disrupting the status quo. This paper considers the concept of walkability in light of the approaching transition to CAVs, considering literature in engineering, information technology, built environment, land use, and public health, to support a discussion on research needs. To add depth, we subject a collection of research papers and technical reports to text analytics.
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Wang, Chengmei, and Yuchuan Du. "Lane-Changing Strategy Based on a Novel Sliding Mode Control Approach for Connected Automated Vehicles." Applied Sciences 12, no. 21 (October 30, 2022): 11000. http://dx.doi.org/10.3390/app122111000.
Full textAbstract:
Safe and efficient autonomous lane changing is a key step of connected automated vehicles(CAVs), which can greatly reduce the traffic accident rate and relieve the traffic pressure. Aiming at the requirements of the smoothness and efficiency of the lane-changing trajectory of CAVs, it is necessary to design the lane changing controller to integrate the sensing, decision-making, and control tasks in the driving process. Firstly, based on the vehicle dynamics model, this paper proposes a vehicle lane-changing control strategy based on NNTSMC method (neural network enhanced non-singular fast terminal sliding mode control). The designed lane-changing controller can well realize the designed path tracking, and both lateral position and yaw angle can well track the expected value. This method enables the vehicle to control the front wheel steering angle intelligently, and the lateral acceleration during steering changes in the small scope, which ensures the steering stability of the vehicle. In this study, an improved adaptive RBF neural network with bounded mapping is designed to estimate the upper bound of the total disturbance of the system, which effectively reduces the chattering phenomenon of the control force. The Lyapunov function constructed in this study proves that the designed controller can ensure the stability of the controlled system. Finally, a comparative experiment is performed by the MATLAB/Simulink-CarSim co-simulation. Compared with SMC and TSMC (non-singular fast terminal sliding mode control), the proposed method has a performance improvement of at least 58.0% and 34.1%, respectively. The effectiveness and superiority of the proposed control method were confirmed by the experiments on the co-simulation platform.
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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.
Full textAbstract:
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%.