Collision-Aware Communication for Intersection Management of Automated Vehicles

Intersection management of automated vehicles relies on wireless communication, whereby communication resources should be allocated to vehicles while maintaining safety. We present a collision-aware resource allocation (CARA) strategy for coordination of automated and connected vehicles by a centralized intersection manager. The proposed strategy is based on a self-triggered approach and proactively reduces the risk of channel congestion by only assigning communication resources to vehicles that are in critical configurations, i.e., when there is a risk for a future collision. Compared with collision-agnostic communication strategies, typically considered for automated intersection management, the CARA strategy aims to bridge the gap between control, sensing, and communication. It is shown to significantly reduce the required amount of communication (albeit with a slight increase in the control cost), without compromising safety. Furthermore, control cost can be reduced by allowing more frequent communication, which we demonstrate through a trade-off analysis between control performance and communication load. Hence, CARA can operate in communication-limited scenarios, but also be modified for scenarios where the control cost is of primary interest

Vehicle Based Intersection Management with Intelligent Agents

Signal-based intersection management will change when vehicles with intelligent capability are available in the future. Intelligent agents embedded in vehicle software will be responsible for vehicle control and route guidance. Intersection management can be achieved through the collaboration of these agents, without a centralized control infrastructure. This research focuses on the use of distributed multi-agent systems to provide microscopic adaptive control which might reduce traffic delay and chances of collisions at intersections. A hypothesized Mobile Ad-hoc Network provides communication links to connect the agents.Intelligent Agents, Adaptive Intersection Control

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs