Статті в журналах: "Autonomous and connected vehicles"

1

Yu, Bo, Fan Bai, and Falko Dressler. "Connected and Autonomous Vehicles." IEEE Internet Computing 22, no. 3 (May 2018): 4–5. http://dx.doi.org/10.1109/mic.2018.032501510.

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2

Uhlemann, Elisabeth. "Autonomous Vehicles Are Connecting... [Connected Vehicles]." IEEE Vehicular Technology Magazine 10, no. 2 (June 2015): 22–25. http://dx.doi.org/10.1109/mvt.2015.2414814.

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3

Eom, Young Hyun, Gyowoong Hwang, Minsu Lee, Young Geun Choi, Sungkuk Cho, R. Young Chul Kim, and Byungkook Jeon. "Topological Sequence Recognition Mechanism of Dynamic Connected Cars Using the Connected Mobile Virtual Fence (CMVF) System for Connected Car Technology." Applied Sciences 10, no. 12 (June 24, 2020): 4347. http://dx.doi.org/10.3390/app10124347.

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To prevent traffic accidents, even autonomous vehicles, as well as connected cars, need to know the driving situation of other vehicles in the vicinity. In particular, in emergency situations, messages’ transmission among vehicles can face many problems such as the broadcast storm, message flooding, or message contention. Therefore, this paper proposes a topological sequence recognition mechanism that calculates the driving direction of vehicles, the geographical location and relative position associated with the driving direction, and the relative safety distance for each vehicle in connected subgroups of connected cars using the Connected Mobile Virtual Fence (CMVF) system. Thus, the proposed mechanism can alleviate issues with message dissemination as a vehicle will know the driving situations of other nearby vehicles. In addition, the proposed mechanism is found to be very effective, particularly in preventing secondary accidents due to traffic accidents in front of the vehicle so that emergency messages can be disseminated to the trailing vehicles. Finally, it is expected that the proposed mechanism will be reflected in the technology of connected cars and autonomous vehicles.

4

Quack, Tobias, Michael Bösinger, Frank-Josef Heßeler, and Dirk Abel. "Infrastructure-based digital maps for connected autonomous vehicles." at - Automatisierungstechnik 66, no. 2 (February 23, 2018): 183–91. http://dx.doi.org/10.1515/auto-2017-0100.

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Abstract One major key to autonomous driving is reliable knowledge about the vehicle's surroundings. In complex situations like urban intersections, the vehicle's on-board sensors are often unable to detect and classify all features of the environment. Therefore, high-precision digital maps are widely used to provide the vehicle with additional information. In this article, we introduce a system which makes use of a mobile edge computing architecture (MEC) for computing digital maps on infrastructure-based, distributed computers. In cooperation with the mobile network operator Vodafone an LTE test field is implemented at the Aldenhoven Testing Center (ATC). The proving ground thus combines an urban crossing with the MEC capabilities of the LTE test field so that the developed methods can be tested in a realistic scenario. In the near future the LTE test field will be equipped with the new 5G mobile standard allowing for fast and reliable exchange of map and sensor data between vehicles and infrastructure.

5

Gao, Kai, Di Yan, Fan Yang, Jin Xie, Li Liu, Ronghua Du, and Naixue Xiong. "Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario." Sensors 19, no. 19 (September 27, 2019): 4199. http://dx.doi.org/10.3390/s19194199.

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Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.

6

Uhlemann, Elisabeth. "Trusting Autonomous Vehicles [Connected and Automated Vehicles]." IEEE Vehicular Technology Magazine 14, no. 2 (June 2019): 121–24. http://dx.doi.org/10.1109/mvt.2019.2905521.

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7

Shao, Yunli, Mohd Azrin Mohd Zulkefli, and Zongxuan Sun. "Vehicle and Powertrain Optimization for Autonomous and Connected Vehicles." Mechanical Engineering 139, no. 09 (September 1, 2017): S19—S23. http://dx.doi.org/10.1115/1.2017-sep-6.

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This article discusses the potential of using autonomous and connected vehicle (CV) technologies to save energy. It also focuses on the potential energy savings of internal combustion engine-based vehicles (ICVs) and hybrid electric vehicles (HEVs). An example of vehicle and powertrain co-optimization for HEV eco-approaching and departure is also given. CV technologies are gaining increasing attention around the world. Vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication enable real-time access to traffic information that was not available before, including preceding vehicles’ location, speed, pedal position, traffic signal phasing and timing (SPaT). The example shown in this article demonstrates the potential benefits from vehicle and powertrain co-optimization by investigating an eco-approaching and departure application. More research in this area can offer more mature solutions to implement such optimization in a real-production vehicle.

8

Fakhrmoosavi, Fatemeh, Ramin Saedi, Ali Zockaie, and Alireza Talebpour. "Impacts of Connected and Autonomous Vehicles on Traffic Flow with Heterogeneous Drivers Spatially Distributed over Large-Scale Networks." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (August 10, 2020): 817–30. http://dx.doi.org/10.1177/0361198120940997.

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Connected and automated vehicle technologies are expected to significantly contribute in improving mobility and safety. As connected and autonomous vehicles have not been used in practice at large scale, there are still some uncertainties in relation to their applications. Therefore, researchers utilize traffic simulation tools to model the presence of these vehicles. There are several studies on the impacts of vehicle connectivity and automation at the segment level. However, only a few studies have investigated these impacts on traffic flow at the network level. Most of these studies consider a uniform distribution of connected or autonomous vehicles over the network. They also fail to consider the interactions between heterogeneous drivers, with and without connectivity, and autonomous vehicles at the network level. Therefore, this study aims to realistically observe the impacts of these emerging technologies on traffic flow at the network level by incorporating adaptive fundamental diagrams in a mesoscopic simulation tool. The adaptive fundamental diagram concept considers spatially and temporally varying distributions of different vehicle types with heterogeneous drivers. Furthermore, this study considers the intersection capacity variations and fundamental diagram adjustments for arterial links resulting from the presence of different vehicle types and driver classes. The proposed methodology is applied to a large-scale network of Chicago. The results compare network fundamental diagrams and hysteresis loop areas for different proportions of connected and autonomous vehicles. In addition to quantifying impacts of connected and autonomous vehicles, the results demonstrate the impacts of various factors associated with these vehicles on traffic flow at the network level.

9

Razzaq, Sheeba, Amil Roohani Dar, Munam Ali Shah, Hasan Ali Khattak, Ejaz Ahmed, Ahmed M. El-Sherbeeny, Seongkwan Mark Lee, Khaled Alkhaledi, and Hafiz Tayyab Rauf. "Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles." Applied Sciences 12, no. 3 (January 20, 2022): 1049. http://dx.doi.org/10.3390/app12031049.

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According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.

10

Uhlemann, Elisabeth. "Time for Autonomous Vehicles to Connect [Connected Vehicles]." IEEE Vehicular Technology Magazine 13, no. 3 (September 2018): 10–13. http://dx.doi.org/10.1109/mvt.2018.2848342.

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Alkheir, Ala Abu, Moayad Aloqaily, and Hussein T. Mouftah. "Connected and Autonomous Electric Vehicles (CAEVs)." IT Professional 20, no. 6 (November 1, 2018): 54–61. http://dx.doi.org/10.1109/mitp.2018.2876977.

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12

Yang, Qing, Honggang Wang, Weisong Shi, Ye Liu, Dinh Thai Hoang, Antonella Molinaro, and Ryokichi Onishi. "Guest Editorial: Connected And Autonomous Vehicles." IEEE Network 37, no. 4 (July 2023): 180–82. http://dx.doi.org/10.1109/mnet.2023.10293233.

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13

J. Mennie, James. "Semi-Autonomous Vehicles & Connected Vehicles Can Save Lives Now!" Muma Business Review 3 (2019): 207–12. http://dx.doi.org/10.28945/4421.

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Autonomous Vehicles have made rapid advances, yet remain elusive for mass adoption. Technology remains in beta phase, and fully autonomous vehicles are not expected until the 2030’s as numerous companies have spent billions of dollars racing towards a safe and reliable solution. Almost 40,000 Americans lose their lives in traffic fatalities every year. The technology for Connected Vehicles and Semi-Autonomous Vehicles is here and ready to be implemented. Why hasn’t this happened yet?

14

S. Raj, Jennifer. "Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors." March 2021 3, no. 2 (July 17, 2021): 93–106. http://dx.doi.org/10.36548/jucct.2021.2.003.

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The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.

15

Uhlemann, Elisabeth. "Legislation Supports Autonomous Vehicles But Not Connected Ones [Connected and Automated Vehicles]." IEEE Vehicular Technology Magazine 17, no. 2 (June 2022): 112–15. http://dx.doi.org/10.1109/mvt.2022.3159987.

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16

Pipicelli, Michele, Alfredo Gimelli, Bernardo Sessa, Francesco De Nola, Gianluca Toscano, and Gabriele Di Blasio. "Architecture and Potential of Connected and Autonomous Vehicles." Vehicles 6, no. 1 (January 29, 2024): 275–304. http://dx.doi.org/10.3390/vehicles6010012.

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The transport sector is under an intensive renovation process. Innovative concepts such as shared and intermodal mobility, mobility as a service, and connected and autonomous vehicles (CAVs) will contribute to the transition toward carbon neutrality and are foreseen as crucial parts of future mobility systems, as demonstrated by worldwide efforts in research and industry communities. The main driver of CAVs development is road safety, but other benefits, such as comfort and energy saving, are not to be neglected. CAVs analysis and development usually focus on Information and Communication Technology (ICT) research themes and less on the entire vehicle system. Many studies on specific aspects of CAVs are available in the literature, including advanced powertrain control strategies and their effects on vehicle efficiency. However, most studies neglect the additional power consumption due to the autonomous driving system. This work aims to assess uncertain CAVs’ efficiency improvements and offers an overview of their architecture. In particular, a combination of the literature survey and proper statistical methods are proposed to provide a comprehensive overview of CAVs. The CAV layout, data processing, and management to be used in energy management strategies are discussed. The data gathered are used to define statistical distribution relative to the efficiency improvement, number of sensors, computing units and their power requirements. Those distributions have been employed within a Monte Carlo method simulation to evaluate the effect on vehicle energy consumption and energy saving, using optimal driving behaviour, and considering the power consumption from additional CAV hardware. The results show that the assumption that CAV technologies will reduce energy consumption compared to the reference vehicle, should not be taken for granted. In 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while the results are more promising for heavy-duty vehicles.

17

Zhang, Yuheng, Luning Liu, Zhaoming Lu, Luhan Wang, and Xiangming Wen. "Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles." IEEE Access 8 (2020): 124486–502. http://dx.doi.org/10.1109/access.2020.3002825.

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18

Wang, Zhuwei, Yuehui Guo, Yu Gao, Chao Fang, Meng Li, and Yang Sun. "Fog-Based Distributed Networked Control for Connected Autonomous Vehicles." Wireless Communications and Mobile Computing 2020 (November 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/8855655.

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With the rapid developments of wireless communication and increasing number of connected vehicles, Vehicular Ad Hoc Networks (VANETs) enable cyberinteractions in the physical transportation system. Future networks require real-time control capability to support delay-sensitive application such as connected autonomous vehicles. In recent years, fog computing becomes an emerging technology to deal with the insufficiency in traditional cloud computing. In this paper, a fog-based distributed network control design is proposed toward connected and automated vehicle application. The proposed architecture combines VANETs with the new fog paradigm to enhance the connectivity and collaboration among distributed vehicles. A case study of connected cruise control (CCC) is introduced to demonstrate the efficiency of the proposed architecture and control design. Finally, we discuss some future research directions and open issues to be addressed.

19

Lobato, Wellington, Paulo Mendes, Denis Rosário, Eduardo Cerqueira, and Leandro A. Villas. "Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles." Future Internet 15, no. 2 (January 22, 2023): 41. http://dx.doi.org/10.3390/fi15020041.

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Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.

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Mudhivarthi, Bhavesh Raju, Prabhat Thakur, and Ghanshyam Singh. "Aspects of Cyber Security in Autonomous and Connected Vehicles." Applied Sciences 13, no. 5 (February 26, 2023): 3014. http://dx.doi.org/10.3390/app13053014.

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An automobile is a computer on wheels after the integration of electronics. This handshake of electronics and mechanical systems makes a vehicle smart, and comfortable; driver assistance for achieving this involves data exchange and surroundings sensing. Devices such as sensors, telematics, protocols, etc., are responsible for data exchange and data sensing. This process contains some loopholes that are the preliminary sources for the attacker to attack the vulnerable devices to control the vehicle. This article provides a review of possible attacks and defenses on autonomous and connected vehicles. The attacker’s area of autonomous and connected vehicles is classified into three categories that are safety system attacks, connectivity attacks, and diagnostics attacks, and provided all possible defenses for those attacks. In addition, we provided an analysis of the domain to understand the scenarios in this domain, recommendations, and future scope in this area for further work.

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El Ganaoui-Mourlan, Ouafae, Stephane Camp, Charles Verhas, Nicolas Pollet, Benjamin Ortega, and Baptiste Robic. "Traffic Manager Development for a Roundabout Crossed by Autonomous and Connected Vehicles Using V2I Architecture." Sustainability 15, no. 12 (June 7, 2023): 9247. http://dx.doi.org/10.3390/su15129247.

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Connected Autonomous Vehicle (CAV) is considered as a proposal toward sustainable mobility. In order to succeed in a sustainable mobility solution, “CAV” or more precisely “CAV Transport System” should prove to be low energy, safe, and allow better performances than human-driven vehicles. This paper will propose a system architecture for a sustainable CAV Transport System on a standard scenario: crossing a roundabout. Nowadays, roundabouts are very common and practical crossing alternatives to improve the traffic flow and increase safety. This study aims to simulate and analyze the behavior of connected autonomous vehicles crossing a roundabout using a V2I (vehicle-to-infrastructure) architecture. The vehicles are exchanging information with a so-called central signaling unit. All vehicles are exchanging their position, speed, and target destination. The central signaling unit has a global view of the system compared to each ego vehicle (has more local than global information); thus, can safely and efficiently manage the traffic of the vehicles in the roundabout using a standard signaling block strategy. This strategy of decision of the central signaling unit (CSU) is performed by dividing the roundabout into several zones/blocks which can be booked by only one vehicle at a time. A solver, reproducing a vehicle’s behavior and dynamics, computes the trajectory and velocity of each vehicle depending on its surroundings. Finally, a graphical representation is used and implemented to facilitate the analysis and visualization of the roundabout crossing. The vehicle flow performance of the developed traffic control model is compared with SUMO.

22

Jafaripournimchahi, Ammar, Yingfeng Cai, Hai Wang, Lu Sun, and Jiancheng Weng. "Integrated-Hybrid Framework for Connected and Autonomous Vehicles Microscopic Traffic Flow Modelling." Journal of Advanced Transportation 2022 (April 26, 2022): 1–16. http://dx.doi.org/10.1155/2022/2253697.

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In this study, a novel traffic flow modeling framework is proposed considering the impact of driving system and vehicle mechanical behavior as two different units on the traffic flow. To precisely model the behavior of Connected and Autonomous (CA) vehicles, three submodels are proposed as a novel microscopic traffic flow framework, named Integrated-Hybrid (IH) model. Focusing on the realization of the car following behavior of CA vehicles, the driving system (vehicle control system) and the vehicle mechanical system are modeled separately and linked by throttle and brake actuators model. The IH model constitutes the key part of the Full Velocity Difference (FVD) model considering the mechanical capability of vehicles and dynamic collision avoidance strategies to ensure the safety of following distance between two consecutive vehicles. Linear stability conditions are derived for each model and developing methodology for each submodel is discussed. Our simulations revealed that the IH model successfully generates velocity and acceleration profiles during car following maneuvers and throttle angle/brake information in connected vehicles environment can effectively improve traffic flow stability. The vehicles’ departure and arrival process while passing through a signal-lane with a traffic light considering the anticipation driving behavior and throttle angle/brake information of direct leading vehicle was explored. Our numerical results demonstrated that the IH model can capture the velocity fluctuations, delay times, and kinematic waves efficiently in traffic flow.

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Jafaripournimchahi, Ammar, Yingfeng Cai, Hai Wang, Lu Sun, and Jiancheng Weng. "Integrated-Hybrid Framework for Connected and Autonomous Vehicles Microscopic Traffic Flow Modelling." Journal of Advanced Transportation 2022 (April 26, 2022): 1–16. http://dx.doi.org/10.1155/2022/2253697.

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In this study, a novel traffic flow modeling framework is proposed considering the impact of driving system and vehicle mechanical behavior as two different units on the traffic flow. To precisely model the behavior of Connected and Autonomous (CA) vehicles, three submodels are proposed as a novel microscopic traffic flow framework, named Integrated-Hybrid (IH) model. Focusing on the realization of the car following behavior of CA vehicles, the driving system (vehicle control system) and the vehicle mechanical system are modeled separately and linked by throttle and brake actuators model. The IH model constitutes the key part of the Full Velocity Difference (FVD) model considering the mechanical capability of vehicles and dynamic collision avoidance strategies to ensure the safety of following distance between two consecutive vehicles. Linear stability conditions are derived for each model and developing methodology for each submodel is discussed. Our simulations revealed that the IH model successfully generates velocity and acceleration profiles during car following maneuvers and throttle angle/brake information in connected vehicles environment can effectively improve traffic flow stability. The vehicles’ departure and arrival process while passing through a signal-lane with a traffic light considering the anticipation driving behavior and throttle angle/brake information of direct leading vehicle was explored. Our numerical results demonstrated that the IH model can capture the velocity fluctuations, delay times, and kinematic waves efficiently in traffic flow.

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Sjoberg, Katrin. "Resilience and Recovery [Connected and Autonomous Vehicles]." IEEE Vehicular Technology Magazine 16, no. 1 (March 2021): 93–96. http://dx.doi.org/10.1109/mvt.2020.3044123.

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Safi, Mostafa, Seyed Mehran Dibaji, and Mohammad Pirani. "Resilient coordinated movement of connected autonomous vehicles." European Journal of Control 64 (March 2022): 100613. http://dx.doi.org/10.1016/j.ejcon.2021.12.008.

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26

Liu, Changliu, Chung-Wei Lin, Shinichi Shiraishi, and Masayoshi Tomizuka. "Distributed Conflict Resolution for Connected Autonomous Vehicles." IEEE Transactions on Intelligent Vehicles 3, no. 1 (March 2018): 18–29. http://dx.doi.org/10.1109/tiv.2017.2788209.

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Andersen, Hans, Xiaotong Shen, You Hong Eng, Daniela Rus, and Marcelo H. Ang. "Connected Cooperative Control of Autonomous Vehicles During Unexpected Road Situations." Mechanical Engineering 139, no. 12 (December 1, 2017): S3—S7. http://dx.doi.org/10.1115/1.2017-dec-7.

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This article discusses how connected cooperative control of autonomous vehicles (AVs) can help in providing safe and comfortable mobility during unexpected road situations. Driving AVs in urban areas poses a big challenge due to the complexity of the traffic rules as well as unexpected scenarios involved. In these situations, an inter-vehicle communication system can be of great help. Cooperation between multiple AVs is possible with the development of vehicular communication. In particular, state estimation can be improved with multiple sources of information gathered from different vehicles. Cooperative state estimation can also improve robustness against communication failure. With future trajectories shared among nearby vehicles, the motion can be coordinated to make navigation safer and smoother for AVs. For vehicular communication, the IEEE 802.11p standard has been designed to allow information exchange between high-speed cars, and between vehicles and roadside infrastructure. Other wireless communication technologies, such as 3G, 4G, and WiFi, are also suggested.

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Cao, Hang, and Máté Zöldy. "An Investigation of Autonomous Vehicle Roundabout Situation." Periodica Polytechnica Transportation Engineering 48, no. 3 (August 4, 2019): 236–41. http://dx.doi.org/10.3311/pptr.13762.

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The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing.

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Kim, Hoe Kyoung. "The Environmental Benefits of an Automatic Idling Control System of Connected and Autonomous Vehicles (CAVs)." Applied Sciences 14, no. 11 (May 21, 2024): 4338. http://dx.doi.org/10.3390/app14114338.

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The transportation sector is regarded as the main culprit in greenhouse gas emission in the urban network, particularly idling vehicles waiting at signalized intersections. Although autonomous vehicles can be a promising technology to tackle vehicle idling, their environmental benefits receive little attention compared with their safety and mobility issues. This study investigated the environmental benefits of autonomous vehicles equipped with an automatic idling control function based on the queue discharge time and traffic signal information transmitted from the traffic signal controller via V2I communication using microscopic mobility and emission simulation models, VISSIM and MOVES, in Haeundae-gu in Busan, Korea. This study found that the function contributes to a significant reduction in CO2 emissions by 23.6% for all-inclusive emission and 94.3% for idling emission, respectively. Moreover, total reduced idling time accounts for 47.6% of the total travel time and 94.3% of the total idling time, respectively. Consequently, the autonomous vehicles equipped with automatic vehicle idling control function under C-ITS can play an important role in reducing greenhouse gas emissions and fuel consumption as well in the urban network.

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Liu, Pengfei, and Wei Fan. "Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment." Promet - Traffic&Transportation 33, no. 5 (October 8, 2021): 767–74. http://dx.doi.org/10.7307/ptt.v33i5.3779.

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Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

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Szeto, Matthew, Edward Andert, Aviral Shrivastava, Martin Reisslein, Chung-Wei Lin, and Christ Richmond. "B-AWARE: Blockage Aware RSU Scheduling for 5G Enabled Autonomous Vehicles." ACM Transactions on Embedded Computing Systems 22, no. 5s (September 9, 2023): 1–23. http://dx.doi.org/10.1145/3609133.

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5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers from a high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages. These blockages are detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Many modern CAVs motion planning algorithms routinely use other vehicle’s near-future path plans, either by explicit communication among vehicles, or by prediction. In this paper, we make use of the knowledge of other vehicle’s near future path plans to further improve the RSU association mechanism for CAVs. We solve the RSU association algorithm by converting it to a shortest path problem with the objective to maximize the total communication bandwidth. We evaluate our approach, titled B-AWARE, in simulation using Simulation of Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements. Simulations show B-AWARE results in a 1.05× improvement of the potential datarate in the average case and 1.28× in the best case vs. the state-of-the-art. But more impressively, B-AWARE reduces the time spent with no connection by 42% in the average case and 60% in the best case as compared to the state-of-the-art methods. This is a result of B-AWARE reducing nearly 100% of blockage occurrences.

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Lu, Qiang, and Kyoung-Dae Kim. "A Genetic Algorithm Approach for Expedited Crossing of Emergency Vehicles in Connected and Autonomous Intersection Traffic." Journal of Advanced Transportation 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/7318917.

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This paper proposes an intersection control algorithm which aims to determine an efficient vehicle-passing sequence that allows the emergency vehicle to cross an intersection as soon as possible while the travel times of other vehicles are minimally affected. When there are no emergency vehicles within the intersection area, the vehicles are controlled by the DICA that we proposed in our earlier work. When there are emergency vehicles entering the communication range, we prioritize emergency vehicles through optimal ordering of vehicles. Since the number of possible vehicle-passing sequences increases rapidly with the number of vehicles, finding an efficient sequence of vehicles in a short time is the main challenge of the study. A genetic algorithm is proposed to solve the optimization problem which finds the optimal vehicle sequence that gives the emergency vehicles the highest priority. The efficiency of the proposed approach for expedited crossing of emergency vehicles is validated through comparisons with DICA and a reactive traffic light algorithm through extensive simulations. The results show that the proposed genetic algorithm is able to decrease the travel times of emergency vehicles significantly in light and medium traffic volumes without causing any noticeable performance degradation of normal vehicles.

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Reyes-Muñoz, Angélica, and Juan Guerrero-Ibáñez. "Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey." Sensors 22, no. 12 (June 18, 2022): 4614. http://dx.doi.org/10.3390/s22124614.

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Abdelaal, Mohamed, and Steffen Schön. "Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles." Algorithms 13, no. 3 (February 28, 2020): 52. http://dx.doi.org/10.3390/a13030052.

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This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bézier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bézier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.

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R, SathisKumar. "Enhanced Autonomous Speed Control System for Integrated Cars." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 11, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33627.

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The Automated Vehicle Speed Control System (AVSCS) is an embedded framework that utilizes specialized hardware and software components to automatically regulate a vehicle's speed. This system is designed to be implemented in a variety of vehicles, including cars, trucks, and autonomous vehicles, to enhance safe and efficient driving. The AVSCS comprises several essential elements: sensors, a processor or microcontroller, a control calculation algorithm, and a user interface. The sensors gather real-time data about the vehicle's current speed, weather conditions, and surrounding road environment, including wheel speed, GPS, radar, LIDAR, and cameras. The microcontroller or processor processes the sensor data and employs control algorithms, such as PID (Proportional-Integral-Derivative) controllers or advanced Model Predictive Control (MPC) techniques, to calculate the optimal vehicle speed. This information is then used to adjust the throttle or braking mechanisms accordingly. Additionally, the user interface allows drivers to customize the system or set their desired speed preferences, providing a seamless and personalized driving experience. The integration of the AVSCS into various vehicle types can significantly increase security, streamline traffic,, and reduce energy consumption, making it a crucial component of the connected and autonomous vehicle ecosystem. Keywords: vehicle speed regulator, GPS, radar, LIDAR, and cameras

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Wu, Jiaxin, Yibing Wang, Zhao Zhang, Yiqing Wen, Liangxia Zhong, and Pengjun Zheng. "A Cooperative Merging Control Method for Freeway Ramps in Connected and Autonomous Driving." Sustainability 14, no. 18 (September 6, 2022): 11120. http://dx.doi.org/10.3390/su141811120.

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The highway on-ramp merging area is one of the major sections that form traffic bottlenecks. In a connected vehicle environment, V2V and V2I technologies enable real-time exchange of information, including position, speed, and acceleration. To improve the efficiency of vehicle merging at the on-ramp, this study proposes a cooperative merging control strategy for network-connected autonomous vehicles. First, the central controller designs the merging sequence and safety space for vehicles passing through the confluence point. Then, a trajectory optimization model was constructed based on vehicle longitudinal dynamics, and the PMP algorithm was used to determine the optimal control input. Finally, all vehicles follow the optimal trajectory so that the ramp vehicles merge smoothly into the mainline. Simulations verify that the proposed algorithm performs better than FIFO, with 13.2% energy savings, 41.4% increase in average speed, and 50.4% reduction in travel time over the uncontrolled merging scenario. The method is further applied to different traffic flow conditions and the results show that it can significantly improve traffic safety and mobility, while effectively reducing vehicle energy consumption. However, the traffic operation improvement is not satisfactory under low traffic demand.

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Mckenzie, Ross, and John Mcphee. "Research and Educational Programs for Connected and Autonomous Vehicles at the University of Waterloo." Mechanical Engineering 139, no. 12 (December 1, 2017): S21—S23. http://dx.doi.org/10.1115/1.2017-dec-11.

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This article presents an overview of the research and educational programs for connected and autonomous vehicles at the University of Waterloo (UWaterloo). UWaterloo is Canada’s largest engineering school, with 9,500 engineering students and 309 engineering faculty. The University of Waterloo Centre for Automotive Research (WatCAR) for faculty, staff and students is contributing to the development of in-vehicle systems education programs for connected and autonomous vehicles (CAVs) at Waterloo. Over 130 Waterloo faculty, 110 from engineering, are engaged in WatCAR’s automotive and transportation systems research programs. The school’s CAV efforts leverage WatCAR research expertise from five areas: (1) Connected and Autonomous; (2) Software and Data; (3) Lightweighting and Fabrication; (4) Structure and Safety; and (5) Advanced Powertrain and Emissions. Foundational and operational artificial intelligence expertise from the University of Waterloo Artificial Intelligence Institute complements the autonomous driving efforts, in disciplines that include neural networks, pattern analysis and machine learning.

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Wu, Biao, Zhixiong Ma, Xichan Zhu, and Yu Lin. "Research on the Vehicle-Behavior Boundary of Intersection Traffic Based on Naturalistic Driving Data Study." Applied Sciences 14, no. 8 (April 18, 2024): 3432. http://dx.doi.org/10.3390/app14083432.

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With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in intersection areas has become a significant challenge due to the rapid advancement of autonomous vehicle technology. Autonomous vehicles in intersections with strong-structure and weak-rule characteristics pose a potential hazard in complex traffic situations. Studying the driving behavior of vehicles in intersections is of great significance due to the complex traffic environment, frequent traffic signals, and traffic violations, which can optimize the vehicle driving behavior and improve the safety and efficiency of intersection traffic. By using naturalistic driving data from the DAIR V2X-Seq dataset and general vehicle dynamic parameters, it is possible to obtain the joint-probability-density distribution of the bivariate dynamic parameters of a vehicle. This distribution represents the driving characteristics of vehicles in intersection traffic. The three vehicle dynamic parameters that have an impact on vehicles driving through the intersection area are velocity, angular velocity, and acceleration. The driving behavior characteristics of human-driven vehicles (HVs) and autonomous vehicles (AVs) were analyzed using the multivariate kernel density estimation (MKDE) method to establish the vehicle-behavior boundary. The assessment of the boundary model showed that it accurately characterizes the driving characteristics of HVs and AVs. This boundary can be used to improve the safety detection of intersection areas, enhancing the performance of autonomous vehicles and optimizing intersection traffic.

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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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Savitha, P. B., Madhu S, and Arjun S. "Cyber Security Issues in Connected Autonomous Vehicle." International Journal of Research Publication and Reviews 4, no. 3 (March 2023): 929–36. http://dx.doi.org/10.55248/gengpi.2023.32358.

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41

Bang, Sookyuk, and Soyoung Ahn. "Analysis and Control of Heterogeneous Connected and Autonomous Vehicles using a Spring-Mass-Damper System." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (June 20, 2020): 309–18. http://dx.doi.org/10.1177/0361198120927696.

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This study analyzes the behavior of heterogeneous connected and autonomous vehicles (CAVs) and proposes the best vehicle sequence for optimal platoon throughput and platoon formation. A spring-mass-damper (SMD) system is adopted for control of CAVs, and the control parameters are formulated in relation to the physical capabilities of vehicles. To gain insight, we consider three types of vehicle: passenger cars, mini-vans, and heavy-duty vehicles. For each type, we investigate the maximum platoon throughput and the clustering time, defined as the time to reach the target equilibrium state. We further investigate different sequences of vehicle types in a platoon to identify the optimal vehicle order that maximizes the throughput and minimizes clustering time. Findings suggest that the highest performance vehicle (in relation to acceleration capability) should be placed as the leader of a platoon and that the number of passenger cars behind heavy vehicles (e.g., semi-trailers) should be minimized in the platoon. In addition, we examine how the proportions of lower performance vehicles affect throughput and clustering times. The result suggests that the higher the proportions, the lower the throughput and the longer the clustering time. The lowest performance vehicle had the greatest effect.

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Liang, Xiao (Joyce), S. Ilgin Guler, and Vikash V. Gayah. "Signal Timing Optimization with Connected Vehicle Technology: Platooning to Improve Computational Efficiency." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (July 16, 2018): 81–92. http://dx.doi.org/10.1177/0361198118786842.

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This paper develops a real-time traffic signal optimization algorithm in the presence of connected and autonomous vehicles (CAVs). The proposed algorithm leverages information from connected vehicles (CVs) arriving at an intersection to identify naturally occurring platoons that consist of both CVs and non-CVs. Signal timings are then selected to optimize the sequence at which these platoons are allowed to discharge through the intersection to minimize total vehicle delay. Longitudinal trajectory guidance that explicitly accounts for vehicle acceleration and deceleration behavior is provided to the lead autonomous vehicle (AV) in any platoon to minimize the total number of stopping maneuvers performed by all vehicles. Simulation tests reveal that the proposed platoon-based algorithm provides superior computational savings (over 95%) compared with a previously developed algorithm that focuses on optimizing departure sequences of individual vehicles, with negligible changes in operational performance. The computational savings allow the platoon-based algorithm to accommodate intersections with four multi-lane approaches and left turns, whereas large computational costs limited the previous vehicle-based algorithm to only two single-lane approaches without conflicting left turns. Additional simulation tests of the platoon-based algorithm on these more realistic intersection configurations show that intersection performance increases as the penetration rate of CAVs in the vehicle fleet increases. However, the marginal benefits decrease rapidly after the fleet is composed of 40% CAVs.

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Sjoberg, Katrin. "Automotive Industry Faces Challenges [Connected and Autonomous Vehicles]." IEEE Vehicular Technology Magazine 15, no. 3 (September 2020): 109–12. http://dx.doi.org/10.1109/mvt.2020.3005604.

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44

Kopelias, Pantelis, Elissavet Demiridi, Konstantinos Vogiatzis, Alexandros Skabardonis, and Vassiliki Zafiropoulou. "Connected & autonomous vehicles – Environmental impacts – A review." Science of The Total Environment 712 (April 2020): 135237. http://dx.doi.org/10.1016/j.scitotenv.2019.135237.

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Levin, Michael W., and Alireza Khani. "Dynamic transit lanes for connected and autonomous vehicles." Public Transport 10, no. 3 (August 22, 2018): 399–426. http://dx.doi.org/10.1007/s12469-018-0186-2.

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46

Atagoziev, Maksat, Ece Güran Schmidt, and Klaus Werner Schmidt. "Lane change scheduling for connected and autonomous vehicles." Transportation Research Part C: Emerging Technologies 147 (February 2023): 103985. http://dx.doi.org/10.1016/j.trc.2022.103985.

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47

Chah, Badreddine, Alexandre Lombard, Anis Bkakria, Reda Yaich, Abdeljalil Abbas-Turki, and Stéphane Galland. "Privacy Threat Analysis for connected and autonomous vehicles." Procedia Computer Science 210 (2022): 36–44. http://dx.doi.org/10.1016/j.procs.2022.10.117.

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48

Wu, Jingkai, Yafei Wang, Lin Wang, Zhaokun Shen, and Chengliang Yin. "Consensus-Based Platoon Forming for Connected Autonomous Vehicles." IFAC-PapersOnLine 51, no. 31 (2018): 801–6. http://dx.doi.org/10.1016/j.ifacol.2018.10.127.

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Faghihian, Hamed, and Arman Sargolzaei. "Energy Efficiency of Connected Autonomous Vehicles: A Review." Electronics 12, no. 19 (September 29, 2023): 4086. http://dx.doi.org/10.3390/electronics12194086.

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Connected autonomous vehicles (CAVs) have emerged as a promising solution for enhancing transportation efficiency. However, the increased adoption of CAVs is expected to lead to a rise in transportation demand and, subsequently, higher energy consumption. In this context, electric CAVs (E-CAVs) present a significant opportunity to shape the future of efficient transportation systems. While conventional CAVs possess the potential to reduce fuel consumption, E-CAVs offer similar prospects but through distinct approaches. Notably, the control of acceleration and regenerative brakes in E-CAVs stands out as an area of immense potential for increasing efficiency, leveraging various control methods in conjunction with the cooperative and perception capabilities inherent in CAVs. To bridge this knowledge gap, this paper conducts a comprehensive survey of energy efficiency methods employed in conventional CAVs while also exploring energy efficiency strategies specifically tailored for E-CAVs.

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Fang, Yukun, Haigen Min, Xia Wu, Wuqi Wang, Xiangmo Zhao, Beatriz Martinez-Pastor, and Rui Teixeira. "Anomaly diagnosis of connected autonomous vehicles: A survey." Information Fusion 105 (May 2024): 102223. http://dx.doi.org/10.1016/j.inffus.2024.102223.

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