136 research outputs found

    MethOds and tools for comprehensive impact Assessment of the CCAM solutions for passengers and goods. D1.1: CCAM solutions review and gaps

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    Review of the state-of-the-art on Cooperative, Connected and Automated mobility use cases, scenarios, business models, Key Performance Indicators, impact evaluation methods, technologies, and user needs (for organisations & citizens)

    Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

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    The paper deals with traffic simulation within roundabouts when both “connected and automated vehicles” (CAVs) and human-driven cars are present. The aim is to present the past, current and future research on CAVs running into roundabouts within the Cooperative, Connected and Automated Mobility (CCAM) framework. Both microscopic traffic simulations and virtual reality simulations by dynamic driving simulators will be considered. The paper is divided into five parts. At first, the literature is analysed using the Systematic Literature Review (SLR) methodology based on Scopus database. Secondly, the influence of CAVs on roundabout-specific design features and configuration is analysed. Gap-acceptance models used to define the capacity of the roundabout, one of its most important key performance indicators, are also presented. Third, the most common simulation software are described and analysed in terms of traffic demand implementation. Then the communication approaches and path management algorithms are studied. An example is proposed on the integration of microscopic traffic simulations and dynamic driving simulators virtual reality simulations. Finally, car following models suitable for roundabout traffic are discussed. There is still a gap between simulations and actual experience. There are reasonable doubts on how modelling and optimizing CAVs’ behaviour into roundabouts in view of CCAM. It seems that Cooperative, Connected and Automated Vehicles (CCAVs), more than simply Connected and Automated Vehicles (CAVs), could optimise traffic flow, safety and driving comfort within the roundabout. A very promising technology for traffic simulation within the roundabout seems the one based on dynamic driving simulators

    Autonomous Shuttle Transit: An Exploratory Case Study and the Future Impact on TSU Campus

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    By 2040 the third-largest city in the United States, Houston, Texas, a top global city for traffic congestion, will become a significant metropolis with future growth possibilities of 11 million people passing Chicago (HGAC, 2018). For this purpose, Houston and surrounding growing populations will contribute to gridlock traffic, with highway expansions increasing ozone and inefficient transit systems with longer commutes in underserved, sidelined communities. Above all, historically, persons of color, notably Black Indigenous Persons of Color (BIPOC) in Black and Brown marginalized communities, are deprived of transportation accessibility. Undoubtedly, Driverless Shuttle (DS) rideshare platforms reflect that higher-income whites are admittedly more likely to hold discriminatory attitudes toward fellow passengers of different classes and races (Middleton & Zhao, 2019). At the same time, Environmental Justice (EJ) studies have shown that Black and Brown low-income disenfranchised communities are more exposed to inefficient transit systems. They are characterized by unequal treatment and accessibility to the bus than affluent White commuters (Bullard, Johnson, and Torres, 2004). As a result, systemic racism, an unfair burden of environmental injustice, has plagued the Greater Third Ward transit-dependent population. For this purpose, Houston\u27s Metropolitan Transit Authority (METRO) riddle inequities have shaped public transportation for every minoritized BIPOC within the community (Spieler, 2020). Most importantly, Blacks are twice as likely to experience inferior transportation access as their more affluent counterparts (Sisson, 2019; Bullard, Johnson, and Torres, 2004, p.2). According to Harvard Law (2021), Bullard states, In 1990, Dumping in Dixie: Race, Class and Environmental Quality assuredly documented that environmental vulnerability mapped closely with Jim Crow segregation. This why racial redlining discriminatory zoning, and inefficient land use practices, (Bullard, 2021, p. 245; Bullard, 1990) target Houston\u27s Black and Brown neighborhoods, hindering economic and social advancement in employment, education, and health care (Bullard, 2021, p. 245; Bullard, 1990; Freemark, 2020; Talbott, 2020). The problem of injustice was examined by longitudinal data where an Autonomous Vehicle bus pilot associated with the built environment in this study highlighted 1. Transportation inequality along the TSU Campus Tiger Walk is related to bus stops. 2. Distance between three designated bus stop locations. 3. Safety and critical driving functions fully driverless for an entire trip. 4. First/last mile driverless shuttle connectivity interacting with Metro buses and Light Rail in Houston\u27s Greater Third Ward neighborhood. The methods of research incorporated qualitative and quantitative analysis. The study used a driverless shuttle to compare racial and social economics between bus stops at Texas Southern University, a historically black university, during an Autonomous Vehicle (AV) Shuttle pilot study. For this purpose, Autonomous Shuttle Transit, an additional mode of mobility, will connect Houston\u27s Greater Third Ward transit-dependent population to Metro’s bus and light rail networks. In addition to bus stops along the TSU Campus Tiger Walk. This study made a similar theoretical comparison of the Tiger Tram to AV two years before the TSU Shuttle pilot. The results pointed to a link between income and transit-dependent populations using a driverless shuttle under specific conditions. A Google map determined the half-mile distance along the TSU Campus Tiger Walk. The driverless shuttle and socioeconomics of Political Science, Administrative Justice, and Psychology undergraduate classes were used to measure transportation equity horizontally. A regression analysis was carried out to determine if the socioeconomic factors had statistical significance. Also, linear regression modeling was used to determine which sociodemographic variables strongly predict the transport mode used. The findings revealed that Blacks, people with disabilities, and the TSU AV shuttle working with metro buses were statistically significant at a 95% confidence level. Also, a predictor of respondents walking, and biking will use the Autonomous Shuttle as an additional mode of transportation. Also, the data analysis results indicate a significant negative correlation between the driverless shuttle time intervals along the TSU Tiger Walk and the Metro bus service. This correlation implies that higher percentages of respondents will walk further from the TSU campus Tiger Walk central location to the bus stop connecting Third Ward’s transit-dependent residents to the Metro Light rail. Likewise, in the Third Ward community, low-income transit-dependent populations in the Cuney Homes are disproportionately exposed to inadequate transit access than any other area in the neighborhood. The results also support the Environmental Justice (EJ) claim that minorities and low-income transit-dependent populations are closer to bus stops and farther away from the light rail. Although the results showed that race, income, and disability variations are likely to predict that TSU’s transit-dependent population will use the TSU Autonomous Shuttle connecting the Third Ward community. Comparing the social demographic indicators along the TSU Tiger Walk and the Third Ward area shows that deed restrictions do not address EJ concerns associated with bus stops and transportation modes. The conclusion indicates that despite several decades of EJ policies and transit regulations, institutional racism in the Third Ward neighborhood is embedded. Over the decades, African Americans and other people of color have been disproportionately exposed to transit injustice because they are concentrated in neighborhoods with less transit accessibility. However, the TSU Campus Tiger Walk still has fewer efficient transit options than other Third Ward census tracts that map closer to bus stops with higher income

    Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

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    Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

    Advances in Automated Driving Systems

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    Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

    UAS Flight Path Planning and Collision Avoidance Based on Markov Decision Process

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    The growing interest and trend for deploying unmanned aircraft systems (UAS) in civil applications require robust traffic management approaches that can safely integrate the unmanned platforms into the airspace. Although there have been significant advances in autonomous navigation, especially in the ground vehicles domain, there are still challenges to address for navigation in a dynamic 3D environment that airspace presents. An integrated approach that facilitates semi-autonomous operations in dynamic environments and also allows for operators to stay in the loop for intervention may provide a workable and practical solution for safe UAS integration in the airspace. This thesis research proposes a new path planning method for UAS flying in a dynamic 3D environment shared by multiple aerial vehicles posing potential conflict risks. This capability is referred to as de-confliction in drone traffic management. It primarily targets applications such as UAM [1] where multiple flying manned and/or unmanned aircraft may be present. A new multi-staged algorithm is designed that combines AFP method and Harmonic functions with AKF and MDP for dynamic path planning. It starts with the prediction of aircraft traffic density in the area and then generates the UAS flight path in a way to minimize the risk of encounters and potential conflicts. Hardware-in-the-loop simulations of the algorithm in various scenarios are presented, with an RGB-D camera and Pixhawk Autopilot to track the target. Numerical simulations show satisfactory results in various scenarios for path planning that considerably reduces the risk of conflict with other static and dynamic obstacles. A comparison with the potential field is provided that illustrates the robust and fast of the MDP algorithm

    A framework for the synergistic integration of fully autonomous ground vehicles with smart city

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    Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This paper proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities
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