Bibliografie tematiche / Autonomous and connected vehicles

Letteratura scientifica selezionata sul tema "Autonomous and connected vehicles"

Autore: Grafiati

Pubblicato: 7 luglio 2024

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Articoli di riviste sul tema "Autonomous and connected vehicles":

Yu, Bo, Fan Bai e Falko Dressler. "Connected and Autonomous Vehicles". IEEE Internet Computing 22, n. 3 (maggio 2018): 4–5. http://dx.doi.org/10.1109/mic.2018.032501510.

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Uhlemann, Elisabeth. "Autonomous Vehicles Are Connecting... [Connected Vehicles]". IEEE Vehicular Technology Magazine 10, n. 2 (giugno 2015): 22–25. http://dx.doi.org/10.1109/mvt.2015.2414814.

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Eom, Young Hyun, Gyowoong Hwang, Minsu Lee, Young Geun Choi, Sungkuk Cho, R. Young Chul Kim e 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, n. 12 (24 giugno 2020): 4347. http://dx.doi.org/10.3390/app10124347.

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Abstract (sommario):

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.

Quack, Tobias, Michael Bösinger, Frank-Josef Heßeler e Dirk Abel. "Infrastructure-based digital maps for connected autonomous vehicles". at - Automatisierungstechnik 66, n. 2 (23 febbraio 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.

Gao, Kai, Di Yan, Fan Yang, Jin Xie, Li Liu, Ronghua Du e Naixue Xiong. "Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario". Sensors 19, n. 19 (27 settembre 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.

Uhlemann, Elisabeth. "Trusting Autonomous Vehicles [Connected and Automated Vehicles]". IEEE Vehicular Technology Magazine 14, n. 2 (giugno 2019): 121–24. http://dx.doi.org/10.1109/mvt.2019.2905521.

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Shao, Yunli, Mohd Azrin Mohd Zulkefli e Zongxuan Sun. "Vehicle and Powertrain Optimization for Autonomous and Connected Vehicles". Mechanical Engineering 139, n. 09 (1 settembre 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.

Fakhrmoosavi, Fatemeh, Ramin Saedi, Ali Zockaie e 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, n. 10 (10 agosto 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.

Razzaq, Sheeba, Amil Roohani Dar, Munam Ali Shah, Hasan Ali Khattak, Ejaz Ahmed, Ahmed M. El-Sherbeeny, Seongkwan Mark Lee, Khaled Alkhaledi e Hafiz Tayyab Rauf. "Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles". Applied Sciences 12, n. 3 (20 gennaio 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.

Uhlemann, Elisabeth. "Time for Autonomous Vehicles to Connect [Connected Vehicles]". IEEE Vehicular Technology Magazine 13, n. 3 (settembre 2018): 10–13. http://dx.doi.org/10.1109/mvt.2018.2848342.

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Più fonti

Tesi sul tema "Autonomous and connected vehicles":

Wei, Jian. "Hybrid mobile computing for connected autonomous vehicles". Thesis, Aston University, 2018. http://publications.aston.ac.uk/37533/.

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With increasing urbanization and the number of cars on road, there are many global issues on modern transport systems. Autonomous driving and connected vehicles are the most promising technologies to tackle these issues. The so-called integrated technology connected autonomous vehicles (CAV) can provide a wide range of safety applications for safer, greener and more efficient intelligent transport systems (ITS). As computing is an extreme component for CAV systems, various mobile computing models including mobile local computing, mobile edge computing and mobile cloud computing are proposed. However it is believed that none of these models fits all CAV applications, which have highly diverse quality of service (QoS) requirements such as communication delay, data rate, accuracy, reliability and/or computing latency. In this thesis, we are motivated to propose a hybrid mobile computing model with objective of overcoming limitations of individual models and maximizing the performances for CAV applications. In proposed hybrid mobile computing model three basic computing models and/or their combinations are chosen and applied to different CAV applications, which include mobile local computing, mobile edge computing and mobile cloud computing. Different computing models and their combinations are selected according to the QoS requirements of the CAV applications. Following the idea, we first investigate the job offloading and allocation of computing and communication resources at the local hosts and external computing centers with QoS aware and resource awareness. Distributed admission control and resource allocation algorithms are proposed including two baseline non-cooperative algorithms and a matching theory based cooperative algorithm. Experiment results demonstrate the feasibility of the hybrid mobile computing model and show large improvement on the service quality and capacity over existing individual computing models. The matching algorithm also largely outperforms the baseline non-cooperative algorithms. In addition, two specific use cases of the hybrid mobile computing for CAV applications are investigated: object detection with mobile local computing where only local computing resources are used, and movie recommendation with mobile cloud computing where remote cloud resources are used. For object detection, we focus on the challenges of detecting vehicles, pedestrians and cyclists in driving environment and propose three methods to an existing CNN based object detector. Large detection performance improvement is obtained over the KITTI benchmark test dataset. For movie recommendation we propose two recommendation models based on a general framework of integrating machine learning and collaborative filtering approach. The experiment results on Netflix movie dataset show that our models are very effective for cold start items recommendation.

Anantharaman, Gokul Arvind. "Cooperative Collision Avoidance for Connected and Autonomous Vehicles". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543424841946961.

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Garro, Alexandra. "Connected Vehicle Co-Simulation for Autonomous Vehicles in Airsim using Ns-3". DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2332.

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Abstract (sommario):

Vehicle-to-everything (V2X) communications enables vehicles to communicate directly with each other, as well as roadside infrastructure. Vehicle-to-vehicle (V2V) communication, a subset of V2X communication, enables the vehicle to not solely rely on on-board sensors and allows the vehicle to share information directly to any nearby vehicles. Information shared between vehicles may include a vehicle's position, velocity, and direction, as well as other data. As these are safety-critical applications, rigorous security assessments are needed, yet it can be very expensive, dangerous, and complex to test security vulnerabilities of autonomous vehicles. Therefore, we aim to leverage realistic open-sourced simulators to carry out testing for multiple features, such as security attacks as well as cooperative autonomous driving algorithms. Since there is no open-sourced simulator capable of visually and physically simulating a vehicle and accurately representing its network, this thesis aims to combine a vehicle simulator and network simulator in real-time. Specifically, we incorporate Network Simulator 3 (Ns-3) and Unreal Engine's plugin, Airsim. To run this type of simulation accurately requires high computation power and time, and these requirements can cause delays between the two simulators. To handle the delays during simulation, we propose a system using a time step synchronization technique to pair Airsim and Ns-3. We further elaborate on our incorporation of delaying network packets that arrive earlier than the ideal packet delay. Additionally, we validate our system by demonstrating proof-of-concept attacks. Specifically, we simulate a replay attack and a jamming attack on our system, as well as show that a sybil attack is possible.

Obenauf, Austin William. "CONNECTED AND AUTONOMOUS VEHICLES EFFECTS ON EMERGENCY RESPONSE TIMES". UKnowledge, 2019. https://uknowledge.uky.edu/ce_etds/84.

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Emergency response times have been shown to be directly correlated with mortality rates of out-of-hospital patients. Studies have been conducted to show the relationship between time and mortality rates until patients receive the proper treatment. With more cardiac arrests and other life threatening illnesses occurring in the United States, more emergency calls will be required as well. As of today, technological advancements have been made to reduce response times, but human factors still require certain procedures, causing delays in the run time and increasing the rate of mortality. Here we show the results of emergency response times with the market penetration of connected and autonomous vehicles. With connected and autonomous vehicles, the average time emergency vehicles spend on the roadways can be significantly decreased. Safety procedures with human drivers can be eliminated, giving the emergency vehicle a proper right-of-way through virtual emergency lanes and removing the need to slow down and avoid vehicles at intersections or during periods of heavy congestion. Our results show a three minute decrease in response time under full market penetration of the technology, reducing the mortality rate and increasing the potential to save lives.

Sridhar, Srivatsan. "Cooperative Perception in Autonomous Ground Vehicles using a Mobile Robot Testbed". Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/88742.

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With connected and autonomous vehicles, no optimal standard or framework currently exists, outlining the right level of information sharing for cooperative autonomous driving. Cooperative Perception is proposed among vehicles, where every vehicle is transformed into a moving sensor platform that is capable of sharing information collected using its on-board sensors. This helps extend the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. This increase in situational awareness promotes safe driving over a short range and improves traffic flow efficiency over a long range. This thesis proposes a methodology for cooperative perception for autonomous vehicles over a short range. The problem of cooperative perception is broken down into sub-tasks of cooperative relative localization and map merging. Cooperative relative localization is achieved using visual and inertial sensors, where a computer-vision based camera relative pose estimation technique, augmented with position information, is used to provide a pose-fix that is subsequently updated by dead reckoning using an inertial sensor. Prior to map merging, a technique for object localization using a monocular camera is proposed that is based on the Inverse Perspective Mapping technique. A mobile multi-robot testbed was developed to emulate autonomous vehicles and the proposed method was implemented on the testbed to detect pedestrians and also to respond to the perceived hazard. Potential traffic scenarios where cooperative perception could prove crucial were tested and the results are presented in this thesis.
MS

Tamilarasan, Santhosh. "Use of Connected Vehicle Technology for Improving Fuel Economy and Driveability of Autonomous Vehicles". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543787677995516.

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Dabboussi, Abdallah. "Dependability approaches for mobile environment : Application on connected autonomous vehicles". Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA029.

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Les véhicules autonomes et connectés (VAC) doivent avoir une exigence de fiabilité et de sécurité adéquate dans un environnement incertain aux circonstances complexes. La technologie des capteurs, les actionneurs et l'intelligence artificielle (IA) améliorent constamment leurs performances, ce qui permet un développement continu des véhicules autonomes et une automatisation accrue de la tâche de conduite. Les VAC présentent de nombreux avantages dans la vie humaine, tels que l’augmentation de la sécurité routière, la réduction de la pollution et la fourniture d’une mobilité autonome aux non-conducteurs. Cependant, ces composants avancés créent un nouvel ensemble de défis en matière de sécurité et de fiabilité. Il est donc nécessaire d’évaluer ces technologies avant leur mise en œuvre.Nous étudions dans cette thèse la fiabilité du VAC dans son ensemble, en nous concentrant sur les capteurs et le système de communication. Pour cela, une analyse fonctionnelle a été réalisée pour le système VAC. Notre approche scientifique pour l'analyse de la fiabilité du VAC a été structurée avec des méthodes combinant des approches quantitatives et qualitatives (telles que l'analyse fonctionnelle interne et externe, l'analyse préliminaire des risques (APR) et l'analyse des modes de défaillance, de leurs effets et de leur criticité (AMDEC), etc. Afin de prouver nos résultats, une simulation a été réalisée à l'aide de la probabilité d'analyse d'arbre de défaillance (ADD) et elle a été réalisée pour valider l'approche proposée. Les données (taux d'échec) utilisées proviennent d'une base de données professionnelle concernant le type de composants présentés dans le système. À partir de ces données, un modèle probabiliste de dégradation a été proposé. Le calcul de probabilité a été effectué par rapport à un moment d'utilisation de référence. Par la suite, une analyse de sensibilité a été suggérée concernant les paramètres de fiabilité et des propositions de restructuration ont été élaborées pour les composants.CAV fournit des services de communication entre véhicules : véhicules à véhicules (V2V) ou avec infrastructures côté rue : véhicules à infrastructures (V2I). La technologie des “Communications dédiées à courte portée” (DSRC = Dedicated Short Range Communications) utilise plusieurs canaux pour fournir une variété d'applications de sécurité. Les applications de sécurité nécessitent des transmissions appropriées et fiables, tandis que les applications non liées à la sécurité exigent des performances et une vitesse élevée. Aujourd’hui, la diffusion de messages de sécurité de base (Basic safety message, BSM) est l’un des services fondamentaux des véhicules connectés. Pour cela, un modèle analytique destiné à évaluer la fiabilité des services de diffusion V2V relatifs à la sécurité basée sur IEEE 802.11 dans le système DSRC sur autoroute a été proposé. Enfin, une amélioration du modèle proposé a été faite afin d'accroître la fiabilité de la connexion V2V, en tenant compte de nombreux facteurs tels que la portée de transmission, la densité du véhicule, la distance de sécurité sur l'autoroute, le taux d'erreur de paquets, l'influence de bruit et les taux de défaillants pour les équipements de communications.L'évaluation de ces problèmes conduit à une analyse de sensibilité liée aux paramètres de fiabilité, ce qui contribue à davantage d'innovation dans les domaines de l'ingénierie automobile
Connected and Autonomous vehicles (CAV) must have adequate reliability and safety requirements in uncertain environments with complex circumstances. Sensor technology, actuators and artificial intelligence (AI) are constantly and rapidly evolving, thus enabling further development of self-driving vehicles, and increasing the automation of driving. CAV shows many benefits in human life such as increasing road safety, reducing pollution, and providing independent mobility to non-drivers. However, these advanced components create a new set of challenges concerning safety and dependability. Hence, it is necessary to evaluate these technologies before implementation.We study in this thesis the reliability of CAV as a whole, focusing on sensors and the communication system. For that purpose, a functional analysis was done for the CAV system.Our scientific approach for the analyzing the CAV reliability, was structured with methods that combine quantitative and qualitative approaches such as functional analysis for both internal and external, Preliminary Risk Analysis (PRA), and failure modes and effects criticality analysis (FMECA), in addition to other analysis techniques.To prove our results, a simulation was done using the Fault Tree analysis (FTA) probability in order to validate the proposed approach. The data (Failure ratio) used were from a professional database related to the type of components presented in the system. Using this data, a probabilistic model of degradation was proposed. A probability calculation was performed in relation to a reference time of use. Thereafter a sensitivity analysis was suggested concerning the reliability parameters and redesign proposals developed for the components.CAV provide several communication models: vehicles to vehicle (V2V), or with Road Side Infrastructure: vehicle to infrastructure (V2I). Dedicated Short Range Communication (DSRC) employs a multichannel approach to cater for a variety of safety and non-safety applications. Safety applications necessitate appropriate and reliable transmissions, while non-safety applications require performance and high speed. Broadcasting of Basic Safety Messages (BSM) is one of the fundamental services in today’s connected vehicles. For that, an analytical model to evaluate the reliability of IEEE 802.11 based V2V safety-related broadcast services in DSRC system on highway was proposed. Finally, an enhancement on the proposed model was made in order to increase the reliability of the V2V connection, taking into consideration many factors such as transmission range, vehicle density, and safety headway distance on highway, packet error rate, noise influence, and failures rates of communication equipment.Evaluating these problems leads to a sensitivity analysis related to reliability parameters, which helps further innovation in CAV and automobile engineering

Zeng, Tengchan. "Joint Communication, Control, and Learning for Connected and Autonomous Vehicles". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104216.

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Abstract (sommario):

The use of connected and autonomous ground and aerial vehicles is a promising solution to reduce accidents, improve the traffic efficiency, and provide various services ranging from delivery of goods to monitoring. Different from the current connected vehicles and autonomous vehicles, connected and autonomous vehicles (CAVs) combine autonomy and wireless connectivity and use both sensors and communication systems to increase their situational awareness and for their decision-making. However, in order to reap all the benefits of deploying CAVs, one must consider the interconnection between communication, control, and learning mechanisms for the CAV system design. The key goal of this dissertation is, thus, to develop foundational science that can be used for the design, analysis, and optimization of CAV systems while jointly taking into account the synergies among communication, control, and learning systems. First, a joint communication and control system design is developed for non-coordinated CAVs when performing autonomous path tracking. In particular, the maximum time delay requirements are derived to guarantee the stability of the controller when tracking two typical road scenarios (i.e., straight line and circular curve). Tools from optimization theory and risk theory are then used to jointly optimize the control system and power allocation for the communication network so as to maximize the number of vehicular links that meet the controller's delay requirements. Second, the joint control and communication design framework is extended to two coordinated CAVs applications, i.e., CAV platoons and unmanned aerial vehicle (UAV) swarms. Third, a distributed machine learning algorithm, i.e., federated learning (FL), is proposed for a swarm of connected and autonomous UAVs to execute tasks, such as coordinated trajectory planning and cooperative target recognition. In particular, a rigorous convergence analysis for FL is performed to show how wireless factors impact the FL convergence performance, and the design of UAV swarm networks is optimized to reduce the convergence time. Fourth, a new FL framework, called dynamic federated proximal (DFP) algorithm, is proposed for designing the autonomous controller of CAVs while considering the mobility of CAVs, the wireless fading channels, as well as the unbalanced and non independent and identically distributed data across CAVs. To improve the convergence of the proposed DFP algorithm, a contract-theoretic incentive mechanism is also proposed. Fifth, a wireless-enabled asynchronous federated learning (AFL) framework is proposed for urban air mobility (UAM) aircraft to collaboratively learn the turbulence prediction model. In particular, to characterize how UAM aircraft leverage wireless connectivity for AFL, a stochastic geometry based spatial model is developed and the wireless connectivity performance is analyzed. Then, a rigorous convergence analysis is performed for the proposed AFL framework to identify how fast the UAM aircraft converge to using the optimal turbulence prediction model. Sixth, based on the concordance order from stochastic ordering theory, a dependence control mechanism is proposed to improve the overall reliability of wireless networks for CAVs. Finally, to determine the optimal cache placement for CAVs, a novel spatio-temporal caching framework is proposed where the notion of graph motifs, i.e., the spatio-temporal communication patterns in wireless networks, is used. In conclusion, the frameworks presented in this dissertation will provide key fundamental guidelines to design, analyze, and optimize CAV systems.
Doctor of Philosophy
The evolution of transportation systems has always been the key to the progress of human societies. Recently, technology advances in sensing, autonomy, computing, and wireless connectivity ushered in the era of connected and autonomous vehicles (CAVs). In essence, CAVs rely on the data collected from sensors and wireless communication systems to automatically make the operation decision. If designed properly, the deployment of CAVs can improve the safety and the driving experience, increase the fuel efficiency and road capacity, as well as provide various services ranging from delivery of goods to monitoring. To reap all these benefits of deploying CAVs, one must address a number of technique challenges related to the wireless connectivity, autonomy, and autonomous learning for CAV systems. In particular, for CAV connectivity, the challenges include building a low latency and highly reliable network, using proper models for mobile radio channels, and determining the effective content dissemination strategy. At the control level, key considerations include guaranteeing stability and robustness for the controller when faced with measurement errors and wireless imperfections and rapidly adapting the CAV to dynamic environments. Meanwhile, when CAVs use machine learning to complete their tasks (e.g., object detection and environment monitoring), insufficient training data, privacy concerns, communication overhead, and limited energy are among the main challenges. Therefore, this dissertation develops the foundational science needed to design, analyze, and optimize CAVs while jointly taking into account the challenges within the wireless network, controller, and leaning mechanism design. To this end, various frameworks for the joint communication, control, and learning design and wireless network optimizations are proposed for different CAV applications. The results show that, using the proposed frameworks, the performance of CAVs can be optimized with more reliable communication systems, more stable controller, and improved learning mechanism, enabling intelligent transportation systems for the future smart cities.

Ghiasi, Amir. "Connected Autonomous Vehicles: Capacity Analysis, Trajectory Optimization, and Speed Harmonization". Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7295.

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

Alhuttaitawi, Saif. "Intersection coordination for Autonomous Vehicles". Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20936.

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Connected Autonomous Vehicles require intelligent autonomous intersection management for safe and efficient operation. Given the uncertainty in vehicle trajectory, intersection management techniques must consider a safety buffer among the vehicles, which must also account for the network and computational delay, queue and determine the best solution to avoid traffic congestions (smart intersection management), in this paper we model traffic by using Poisson distribution method then add a birth-death processes for each state and combine both two in one queuing system (The Markovian chain) to model the traffic.Also, this paper will compare some autonomous vehicles communication techniques in intersections to draw the best scenario for autonomous vehicle network communication in order to reduce the traffic congestion in an intersection.The Connected Autonomous Vehicles and a normal autonomous vehicle, as well from the third line of the intersection a mix between the both will be provided into the intersection.The last section is about applying the results from the first and second research question into a simulator and compare the simulation results to approve the advantage of using the next generation of transportation technology (The connected autonomous vehicles) over the normal conventional vehicles.

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Libri sul tema "Autonomous and connected vehicles":

Mouftah, Hussein T., Melike Erol-Kantarci e Sameh Sorour, a cura di. Connected and Autonomous Vehicles in Smart Cities. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429329401.

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Murphey, Yi Lu, Ilya Kolmanovsky e Paul Watta, a cura di. AI-enabled Technologies for Autonomous and Connected Vehicles. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-06780-8.

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Hamid, Umar Zakir Abdul, e Fadi Al-Turjman, a cura di. Towards Connected and Autonomous Vehicle Highways. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66042-0.

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Zuev, Sergey, Ruslan Maleev e Aleksandr Chernov. Energy efficiency of electrical equipment systems of autonomous objects. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1740252.

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When considering the main trends in the development of modern autonomous objects (aircraft, combat vehicles, motor vehicles, floating vehicles, agricultural machines, etc.) in recent decades, two key areas can be identified. The first direction is associated with the improvement of traditional designs of autonomous objects (AO) with an internal combustion engine (ICE) or a gas turbine engine (GTD). The second direction is connected with the creation of new types of joint-stock companies, namely electric joint-stock companies( EAO), joint-stock companies with combined power plants (AOKEU). The energy efficiency is largely determined by the power of the generator set and the battery, which is given to the electrical network in various driving modes. Most of the existing methods for calculating power supply systems use the average values of disturbing factors (generator speed, current of electric energy consumers, voltage in the on-board network) when choosing the characteristics of the generator set and the battery. At the same time, it is obvious that when operating a motor vehicle, these parameters change depending on the driving mode. Modern methods of selecting the main parameters and characteristics of the power supply system do not provide for modeling its interaction with the power unit start-up system of a motor vehicle in operation due to the lack of a systematic approach. The choice of a generator set and a battery, as well as the concept of the synthesis of the power supply system is a problem studied in the monograph. For all those interested in electrical engineering and electronics.

Trimble, Tammy E., Stephanie Baker, Jason Wagner, Wendy Wagner, Lisa Loftus-Otway, Brad Mallory, Susanna Gallun et al. Implications of Connected and Automated Driving Systems, Vol. 4: Autonomous Vehicle Action Plan. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25292.

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Trimble, Tammy E., Stephanie Baker, Jason Wagner, Myra Blanoo, Wendy Wagner, Lisa Loftus-Otway, Brad Mallory et al. Implications of Connected and Automated Driving Systems, Vol. 5: Developing the Autonomous Vehicle Action Plan. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25291.

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Van Uytsel, Steven, e Danilo Vasconcellos Vargas, a cura di. Autonomous Vehicles. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9255-3.

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Yan, Jing, Xian Yang, Haiyan Zhao, Xiaoyuan Luo e Xinping Guan. Autonomous Underwater Vehicles. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6096-2.

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Cox, Ingemar J., e Gordon T. Wilfong, a cura di. Autonomous Robot Vehicles. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4613-8997-2.

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Berns, Karsten, e Ewald von Puttkamer. Autonomous Land Vehicles. Wiesbaden: Vieweg+Teubner, 2009. http://dx.doi.org/10.1007/978-3-8348-9334-5.

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Capitoli di libri sul tema "Autonomous and connected vehicles":

Sarvi, Majid, Saeed Asadi e Steven Van Uytsel. "New Fixes for Old Traffic Problems: Connected Transport Systems and AIMES". In Autonomous Vehicles, 185–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_9.

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Guvenc, Levent, Bilin Aksun Guvenc e Mumin Tolga Emirler. "CONNECTED AND AUTONOMOUS VEHICLES". In Internet of Things and Data Analytics Handbook, 581–95. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119173601.ch35.

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Parkes, Stephen, e Ed Ferrari. "Alignment with Concurrent Policy Agendas Promoting Liveability". In Connected and Autonomous Vehicles, 53–66. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-4.

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Parkes, Stephen, e Ed Ferrari. "The Challenges Posed by Cavs for the Built Environment". In Connected and Autonomous Vehicles, 37–51. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-3.

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Parkes, Stephen, e Ed Ferrari. "Responding to the Arrival of Increasingly Connected and Autonomous Vehicles". In Connected and Autonomous Vehicles, 67–81. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-5.

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Parkes, Stephen, e Ed Ferrari. "Recommendations". In Connected and Autonomous Vehicles, 89–90. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-7.

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Parkes, Stephen, e Ed Ferrari. "Conclusions". In Connected and Autonomous Vehicles, 83–88. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-6.

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Parkes, Stephen, e Ed Ferrari. "The Transition to Connected and Autonomous Vehicles". In Connected and Autonomous Vehicles, 25–36. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-2.

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Parkes, Stephen, e Ed Ferrari. "Introduction". In Connected and Autonomous Vehicles, 15–24. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003348832-1.

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Paranjothi, Anirudh, Mohammed Atiquzzaman e Mohammad S. Khan. "Message Dissemination in Connected Vehicles". In Connected and Autonomous Vehicles in Smart Cities, 203–22. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429329401-7.

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Atti di convegni sul tema "Autonomous and connected vehicles":

King, R. "Traffic Management in a Connected or Autonomous Vehicle Environment". In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0063.

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Edwards, T. "Connected and automated vehicles: Concepts of V2x communications and cooperative driving". In Autonomous Passenger Vehicles. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0060.

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"Connected and Autonomous Vehicles". In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). IEEE, 2019. http://dx.doi.org/10.1109/isie.2019.8781104.

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"CAV Connected and Autonomous Vehicles". In 2020 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2020. http://dx.doi.org/10.1109/icit45562.2020.9067119.

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Parent, Michel. "Automated Vehicles: Autonomous or Connected?" In 2013 14th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2013. http://dx.doi.org/10.1109/mdm.2013.105.

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Hu, Chaowei, Yunpeng Wang, Guizhen Yu, Zhangyu Wang, Ao Lei e Zhehua Hu. "Embedding CNN-Based Fast Obstacles Detection for Autonomous Vehicles". In Intelligent and Connected Vehicles Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1622.

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He, Xiangkun, Kaiming Yang, Yulong Liu e Xuewu Ji. "A Novel Direct Yaw Moment Control System for Autonomous Vehicle". In Intelligent and Connected Vehicles Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1594.

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Pacheco, Lucas, Helder Oliveira, Denis Rosario, Eduardo Cerqueira, Leandro Villas e Torsten Braun. "Service Migration for Connected Autonomous Vehicles". In 2020 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2020. http://dx.doi.org/10.1109/iscc50000.2020.9219592.

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Bucaioni, Alessio, John Lundback, Patrizio Pelliccione e Saad Mubeen. "Architecting and Analysing Connected Autonomous Vehicles". In 2020 IEEE International Conference on Software Architecture Companion (ICSA-C). IEEE, 2020. http://dx.doi.org/10.1109/icsa-c50368.2020.00009.

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Pan, Fei, Weiwen Deng, Sumin Zhang, Jinsong Wang e Shanshan Wang. "Trajectory-Tracking Control for Autonomous Driving Considering Its Stability with ESP". In Intelligent and Connected Vehicles Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1639.

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Rapporti di organizzazioni sul tema "Autonomous and connected vehicles":

Abdul Hamid, Umar Zakir. Responder-to-Vehicle Technologies for Connected and Autonomous Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, maggio 2023. http://dx.doi.org/10.4271/epr2023010.

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<div class="section abstract"><div class="htmlview paragraph">Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including connected and automated vehicles (CAVs). It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The benefits of R2V are not limited to fully autonomous vehicles (e.g., SAE Level 4), but can also be used in Level 2 CAV scenarios. However, despite the potential benefits of R2V, discussions on this topic are still limited.</div><div class="htmlview paragraph"><b>Responder-to-Vehicle Technologies for Connected and Autonomous Vehicles</b> aims to provide an overview of R2V technology and its applications for CAV systems, particularly in the context of collision avoidance features. The responder vehicles in question can be autonomous or non-autonomous. It is hoped that it will provide valuable information and knowledge on vehicle connectivity and automation in the current automotive and mobility ecosystem, enabling the development of safer and more reliable autonomous driving technology. The report is intended for both industrial and academic experts and is expected to stimulate further discussions on the development and standardization of R2V technology.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

Tayeb, Shahab, e Matin Pirouz. Securing the Emerging Technologies of Autonomous and Connected Vehicles. Mineta Transportation Institute, aprile 2020. http://dx.doi.org/10.31979/mti.2020.1915.

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Dukarski, Jennifer. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles. SAE International, settembre 2021. http://dx.doi.org/10.4271/epr2021019.

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Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used? What are the regulatory regimes that impact vehicular data use? Is the US close to harmonizing with other nations in the automotive data privacy? And will the risks of hackers lead to the “zombie car apocalypse” or to another avenue for ransomware? This report explores a number of these legal challenges and the unsettled aspects that arise in the world of automotive data

Kwiat, Paul, Eric Chitambar, Andrew Conrad e Samantha Isaac. Autonomous Vehicle-Based Quantum Communication Network. Illinois Center for Transportation, settembre 2022. http://dx.doi.org/10.36501/0197-9191/22-020.

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Quantum communication was demonstrated using autonomous vehicle-to-vehicle (V2V), as well as autonomous vehicle-to-infrastructure (V2I). Supporting critical subsystems including compact size, weight, and power (SWaP) quantum sources; optical systems; and pointing, acquisition, and tracking (PAT) subsystems were designed, developed, and tested. Novel quantum algorithms were created and analyzed, including quantum position verification (QPV) for mobile autonomous vehicles. The results of this research effort can be leveraged in support of future cross-platform, mobile quantum communication networks that provide improved security, more accurate autonomous sensors, and connected quantum computing nodes for next-generation, smart-infrastructure systems.

Peeta, Srinivas, Jian Wang, Yu Wang, Chaojie Wang e Anye Zhou. Cooperative Control Mechanism for Platoon Formation of Connected and Autonomous Vehicles. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317466.

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Benkraouda, Ouafa, Lindsay Braun e Arnab Chakraborty. Policies and Design Guidelines to Plan for Connected and Autonomous Vehicles. Illinois Center for Transportation, agosto 2022. http://dx.doi.org/10.36501/0197-9191/22-012.

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This report chronicles the work undertaken by researchers at the University of Illinois Urbana Champaign to identify policies and design guidelines to plan for connected and autonomous vehicles (CAVs) in mid-sized regions in Illinois. The report starts with the goals of this work followed by a review of existing literature. The review addresses CAV technologies and scenario planning, including academic research articles, policies and guidance documents from federal and state agencies, and recent long-range transportation plans. The review findings are organized into three categories—drivers, levers, and impacts—to facilitate scenario-based planning and included key factors and trends in technology development and adoption (drivers), mechanisms that planners and policymakers may employ to intervene in or prepare for CAV futures (levers), and community-level outcomes of different plausible CAV futures (impacts). Primary research was undertaken first by interviewing practitioners in six mid-sized regions of Illinois to collect inputs about their needs and obstacles to planning for CAVs, as well as to understand their sense of their community’s preparedness for CAVs. The research team then conducted a detailed survey of over 700 residents from the Greater Peoria region to understand their would-be travel behavior and residential location decisions in a CAV future and general attitude toward self-driving cars. These inputs helped identify the key drivers, levers, and impacts to be employed in creating scenarios, a list of selected policies and design, and a framework to select appropriate responses based on the needs and desires of a community. The detailed scenarios are as follows: (1) continuation of the status quo, (2) private multimodal future, and (3) shared multimodal future. The policies and design guidelines are identified for each scenario and are categorized into six sets of action items: general, data and digitization, mobility and traffic, street design, infrastructure, and planning. Specific details of each action item are organized in a format that allows the user to consider each item carefully and to assess its feasibility in a specific region or city. The appendices include background documents related to primary research and, importantly, a handbook for practitioners.

Hovakimyan, Naira, Hunmin Kim, Wenbin Wan e Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, agosto 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.

Razdan, Rahul. Unsettled Issues Regarding Autonomous Vehicles and Open-source Software. SAE International, aprile 2021. http://dx.doi.org/10.4271/epr2021009.

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As automobiles morph from stand-alone mechanical objects to highly connected, autonomous systems with increasing amounts of electronic components. To manage these complex systems, some semblance of in-car decision-making is also being built and networked to a cloud architecture. This cloud can also enable even deeper capabilities within the broader automotive ecosystem. Unsettled Issues Regarding Autonomous Vehicles and Open-source Software introduces the impact of software in advanced automotive applications, the role of open-source communities in accelerating innovation, and the important topic of safety and cybersecurity. As electronic functionality is captured in software and a bigger percentage of that software is open-source code, some critical challenges arise concerning security and validation.

Ahmed, Qadeer, e Vishnu Renganathan. Cybersecurity and Digital Trust Issues in Connected and Automated Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, aprile 2024. http://dx.doi.org/10.4271/epr2024009.

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<div class="section abstract"><div class="htmlview paragraph">Given the rapid advancements in engineering and technology, it is anticipated that connected and automated vehicles (CAVs) will soon become prominent in our daily lives. This development has a vast potential to change the socio-technical perception of public, personal, and freight transportation. The potential benefits to society include reduced driving risks due to human errors, increased mobility, and overall productivity of autonomous vehicle consumers. On the other hand, the potential risks associated with CAV deployment related to technical vulnerabilities are safety and cybersecurity issues that may arise from flawed hardware and software.</div><div class="htmlview paragraph"><b>Cybersecurity and Digital Trust Issues in Connected and Automated Vehicles</b> elaborates on these topics as unsettled cybersecurity and digital trust issues in CAVs and follows with recommendations to fill in the gaps in this evolving field. This report also highlights the importance of establishing robust cybersecurity protocols and fostering digital trust in these vehicles to ensure safe and secure deployment in our modern transportation system.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

Liu, Tong, e Hadi Meidani. Artificial Intelligence for Optimal Truck Platooning: Impact on Autonomous Freight Delivery. Illinois Center for Transportation, agosto 2023. http://dx.doi.org/10.36501/0197-9191/23-017.

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The advancements in autonomous- and connected-vehicle technologies bring drastic changes in freight delivery. Vehicle-to-vehicle and vehicle-to-infrastructure communication has become a reality with the help of autonomous and connected vehicles. One of the most notable changes is the formation of truck platoons. Despite the numerous benefits of truck platooning, such as reduced fuel consumption and increased traffic efficiency, this approach requires a significant amount of computational resources to obtain aerodynamic performance under different scenarios. To overcome this challenge, a data-driven surrogate model was proposed to predict the drag force and fuel-consumption rate of truck platoons. The surrogate model improves computational efficiency, as compared to traditional methods, and provides a valuable tool for evaluating the performance of truck platoons. To demonstrate the benefits of truck platooning, a 161-km (100-mi) corridor in Illinois on I-57 highway was selected to conduct fuel-consumption analysis and delivery-cost analysis for a three-truck platoon. The results showed that the average fuel savings achieved can be up to 10%, depending on the headway between the trucks. The delivery cost of the truck platoon was reduced by 30%, as compared with conventional line-haul delivery. These findings highlighted the importance of truck platooning as a solution for reducing fuel consumption and improving delivery economy in the freight industry.