212 research outputs found

    Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout

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    Roundabouts in conjunction with other traffic scenarios, e.g., intersections, merging roadways, speed reduction zones, can induce congestion in a transportation network due to driver responses to various disturbances. Research efforts have shown that smoothing traffic flow and eliminating stop-and-go driving can both improve fuel efficiency of the vehicles and the throughput of a roundabout. In this paper, we validate an optimal control framework developed earlier in a multi-lane roundabout scenario using the University of Delaware's scaled smart city (UDSSC). We first provide conditions where the solution is optimal. Then, we demonstrate the feasibility of the solution using experiments at UDSSC, and show that the optimal solution completely eliminates stop-and-go driving while preserving safety.Comment: 6 Pages, 4 Figures, 1 tabl

    Enabling Mixed Autonomy Traffic Control

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    We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable and affordable platform for experimental control. This paper introduces an extensible open-source hardware and software platform, enabling a team of 100 vehicles to execute several different vehicular control algorithms as a collaborative fleet, composed of three different makes and models, which drove 22752 miles in a combined 1022 hours, over 5 days in Nashville, TN in November 2022

    Advances in the Hierarchical Emergent Behaviors (HEB) approach to autonomous vehicles

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    Widespread deployment of autonomous vehicles (AVs) presents formidable challenges in terms on handling scalability and complexity, particularly regarding vehicular reaction in the face of unforeseen corner cases. Hierarchical Emergent Behaviors (HEB) is a scalable architecture based on the concepts of emergent behaviors and hierarchical decomposition. It relies on a few simple but powerful rules to govern local vehicular interactions. Rather than requiring prescriptive programming of every possible scenario, HEB’s approach relies on global behaviors induced by the application of these local, well-understood rules. Our first two papers on HEB focused on a primal set of rules applied at the first hierarchical level. On the path to systematize a solid design methodology, this paper proposes additional rules for the second level, studies through simulations the resultant richer set of emergent behaviors, and discusses the communica-tion mechanisms between the different levels.Peer ReviewedPostprint (author's final draft

    Autonomous Cars and Tort Liability: Why the Market Will Drive Autonomous Cars Out of the Marketplace

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    Autonomous Cars and Tort Liability: Why the Market Will Drive Autonomous Cars Out of the Marketplace

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    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)

    The Driverless City

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    Autonomous Vehicles (AVs) are poised to become the next revolution in mobility. Marketers and engineers enthusiastically promise numerous benefits that AVs will deliver in a future without human drivers: huge reductions in accidents, parking spots, congestion, even the elimination of the loathsome commute among many others. But there are as many, if not more potential ways that the AV revolution can also go wrong: worsening traffic and congestion, urban sprawl, and eroding public transit, for example. How will Autonomous Vehicles shape cities in the future? The Driverless City is not one city: it is many. AVs could be a boon or a debacle. They could even be both at the same time. An extensive literature review revealed a broad cone of possibilities: a myriad different impacts that driverless vehicles could have on different aspects of a city. After synthesizing these into ten main areas of impact, key scenarios are expounded with supplemental foresight. This top-down approach is followed by a bottom-up research workshop where non-expert participants from the general public weighed in on the synthesis and scenarios, and expressed their own thoughts and concerns about what The Driverless City could be. Then, a group of experts helped narrow the cone of possibility into much tighter cones of probability using the Delphi research method. These forecasts and projections shine a spotlight on the key considerations that city planners, urban designers, policy makers and other decision-makers should be taking now to promote desirable outcomes for their city, and curtail undesirable ones

    Vulnerable road users and connected autonomous vehicles interaction: a survey

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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.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version
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