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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - MAVEN (Managing Automated Vehicles Enhances Network)

Teaser

Summary

Highly automated vehicles and Cooperative-Intelligent Transport Systems (C-ITS) technology will get more and more present in the near future. By combining both, guidance of such vehicles can considerably improve traffic efficiency and safety, especially in urban areas. To bring the potential of cooperative automated driving at signalized intersections and corridors into reality, the Managing Automated Vehicles Enhances Network (MAVEN) project is exploring the paradigm of a hierarchical self-organizing system: multi-level, top-down guidance of self-organizing dynamic platoons of cooperative automated vehicles. To this end the project is developing a generic multi-level system to guide automated vehicles at signalized intersections. The system supports decentralized management functions at both vehicle and infrastructure level, operating as interacting agents. For these interactions message sets have been developed and submitted to standardization bodies for the development of C-ITS communication standards. Safety is another important objective for MAVEN and therefore it is developing Advanced Driver Assistance Systems (ADAS) techniques to prevent and/or mitigate dangerous situations taking into account Vulnerable Road Users. The systems will also be demonstrated and evaluated using real-world prototypes and traffic simulation studies. Lastly, the project also acknowledges the importance of involving external stakeholders to maximize potential future impact. To this end a roadmap for the introduction of automated vehicles has been produced, while workshops, questionnaires and other dissemination activities will keep involving these stakeholders.

Work performed

The project started by defining its use cases and scenarioâ€™s for which the interaction between vehicles and infrastructure could have a high impact. This led to 16 use cases, which are categorized into three clusters: platoon management, signal optimization and Infrastructure to Vehicle (I2V) interaction. This led to the conclusion that self-organizing platoons with only indirect infrastructure management are optimal. For the negotiation about signal plans a new principle was developed that is based on mutually beneficial information exchange. This negotiation and the safety related collective perception concepts quickly led to the conclusion that new or extended message sets would be required. The work on the use cases also enabled the design of both vehicle and infrastructure architecture. With the prospect of upcoming impact assessment a novel simulation architecture was developed that focussed on combining high realism of the whole system with fast simulation times to enable large-scale validation.

As mentioned before the message sets developed by MAVEN play a vital role in the proper functioning of the use cases. A new Lane Advisory Message (LAM) was developed, while the Collective Perception Message (CPM) was extended to support infrastructure sensors as well. For lane specific speed advice the Signal Phase and Timing (SPaT) and Map messages, a specific profile for interpretation of the message was developed. Lastly, the Cooperative Awareness Message (CAM) was extended to support the platooning and negotiation functionality.

On the vehicle side, the DOMINION framework allows for simulation of vehicles, but can also control a real vehicle. The new concept of multi-layer platoon planning has been implemented into this framework. It splits the automation into two levels. The trajectory planning ensures the vehicle drives an optimal path within the lane on a short horizon. The tactical level takes input from Vehicle to Vehicle or Infrastructure (V2X) communication and platoon algorithms to control the vehicle on a higher level. This is where platooning, lane changes and optimal speed for approaching an intersection are the main targets. For platooning a detailed algorithm based on a state-machine and supported by the new message sets was developed. ADAS systems and data fusion algorithms have been developed and integrated in the vehicle control system to enhance safety. Extended High Definition (HD) maps play an overarching role in the vehicle, enabling the complex scenarios, like urban intersections.

For the research on the infrastructure side several tools were developed to enable the work. These include an extended Local Dynamic Map (LDM) and the simpla tool for simulating automated vehicles in the traffic microsimulation tool SUMO. The research in queue modelling showed that data fusion of information from traditional detectors and automated vehicles resulted in up to 40% reduction for the average error of the queue length estimation. Having better queue model information results in both more efficient traffic control and more accurate speed advice for automated vehicles. Another problem for speed advice is the predictability of adaptive traffic control algorithms. A new stabilization cost function was added for the adaptive control algorithm, which resulted in 25% reduction of average prediction error, while maintaining similar traffic efficiency. A patent was requested and granted for the solution of stabilizing the control by means of a new cost function to the algorithm. Both the actuated and adaptive control strategies developed in MAVEN show automatic formation of green waves when platoons progress through the network and controllers get connected to each other.

To preparation for the demonstrations, infrastructure installations in Helmond and Braunschweig have taken place and the prototype vehicles have been instrumented with the required sensors and other hardware. An extensive test planning with an impact

Final results

The new use cases and the technical systems go beyond the state of the art with MAVEN systems such as multi-level vehicle control, enhanced queue modelling, the patented traffic control algorithm extension and the various new message sets. The MAVEN use cases and message sets are being considered by industrial organizations like ETSI ITS and the Car2Car Communication Consortium. These will result in further positive impacts of the introduction of C-ITS and automated driving technologies to traffic networks and mobility in general for society.

Although the preliminary results are promising, the project will focus on further integration of vehicle and infrastructure systems for real-world testing and demonstration. Furthermore, simulation studies will analyse the impact in detail using different real-world networks and varying parameters such as traffic flow and penetration rate of automated vehicles.