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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
MAVEN Deliverable 6.4: Integration Final Report
This document presents the work that has been performed in WP6 after D6.3, and therefore focussing on the integration sprints 3-6. It describes which parts of the system are implemented and how they are put together. To do so, it builds upon the deliverables created so far, esp. D6.3 and all other deliverables of the underlying work packages 3, 4 and 5. Another important aspect for understanding the content of this deliverable is D2.1 [4] for the scenario definition of the whole MAVEN project, and the deliverables D6.1 [5] and D6.2 [6], which give an overview on the existing infrastructure and vehicles used in MAVEN
The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research
Advances in Single-vehicle intelligence of automated driving have encountered
significant challenges because of limited capabilities in perception and
interaction with complex traffic environments. Cooperative Driving
Automation~(CDA) has been considered a pivotal solution to next-generation
automated driving and intelligent transportation. Though CDA has attracted much
attention from both academia and industry, exploration of its potential is
still in its infancy. In industry, companies tend to build their in-house data
collection pipeline and research tools to tailor their needs and protect
intellectual properties. Reinventing the wheels, however, wastes resources and
limits the generalizability of the developed approaches since no standardized
benchmarks exist. On the other hand, in academia, due to the absence of
real-world traffic data and computation resources, researchers often
investigate CDA topics in simplified and mostly simulated environments,
restricting the possibility of scaling the research outputs to real-world
scenarios. Therefore, there is an urgent need to establish an open-source
ecosystem~(OSE) to address the demands of different communities for CDA
research, particularly in the early exploratory research stages, and provide
the bridge to ensure an integrated development and testing pipeline that
diverse communities can share. In this paper, we introduce the OpenCDA research
ecosystem, a unified OSE integrated with a model zoo, a suite of driving
simulators at various resolutions, large-scale real-world and simulated
datasets, complete development toolkits for benchmark training/testing, and a
scenario database/generator. We also demonstrate the effectiveness of OpenCDA
OSE through example use cases, including cooperative 3D LiDAR detection,
cooperative merge, cooperative camera-based map prediction, and adversarial
scenario generation
Advanced Sensing and Control for Connected and Automated Vehicles
Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs
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Road transportation is fundamental for the movement of individuals and goods, also contributing to economic development. A significant contributor to urban road congestion is poor intersection control using conventional traffic signals. In this work, we present a decentralized multi-agent system mechanism for road intersection management for connected autonomous vehicles, including the coordination of platoon formations. We propose a reservation-based mechanism able to maximize the overall vehicle throughput at intersections. The study introduces i) auctions as an alternative to the First-Come-First-Serve policy for assigning reservations to vehicles and ii) a method for resolving disputes between conflicting reservations. The results demonstrate the benefits of using platooning for improving throughput and the average delay in intersection control. The distributed nature of the approach increases scalability by shifting the majority of the computing burden from the intersection manager to the driving agents.info:eu-repo/semantics/publishedVersio
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