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Details of Grant 

EPSRC Reference: EP/S015493/1
Title: Resilient Path Coordination in Connected Vehicle Systems
Principal Investigator: Prorok, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science and Technology
Organisation: University of Cambridge
Scheme: New Investigator Award
Starts: 03 January 2019 Ends: 02 January 2021 Value (£): 236,186
EPSRC Research Topic Classifications:
Artificial Intelligence Robotics & Autonomy
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Sep 2018 EPSRC ICT Prioritisation Panel September 2018 Announced
Summary on Grant Application Form
The deployment of connected and autonomous vehicles presents us with transformational opportunities for road transport. As the standardization of inter-vehicular communications progresses, vehicles will soon be wirelessly connected, enabling coordinated driving strategies. By enabling vehicles to jointly agree on optimal maneuvers and navigation strategies, coordinated driving promises to improve overall traffic throughput, road capacity, and passenger safety. However, coordinated driving in connected and autonomous vehicle systems suffers from one key limitation: all vehicles in the system are assumed to be cooperative. This is an issue, since automated vehicle control systems are susceptible to numerous failure conditions, ranging from internally triggered faults (e.g., hardware, software, or communications failures) to externally triggered faults (e.g., environmental disturbances or malicious tampering). When cooperation breaks down due to these faults, coordinated driving strategies lead to negative unanticipated consequences, compromising passenger safety and traffic fluidity.

The goal of this project is to develop a resilient coordinated driving method for multi-vehicle systems that are potentially non-cooperative and unreliable. The issue of providing resilience in the face of non-cooperation (e.g., faulty, byzantine or adversarial agents) has received considerable attention within the domain of distributed network control. However, it is not until very recently that we have started to tackle the question of how to deal with failures and misbehavior when connected agents (vehicles or robots) are mobile. Although preliminary results are promising, the methods deal with control (without planning), and cannot handle discrete workspace constraints (i.e., lane topographies) nor kinodynamic constraints (i.e., car-like motion primitives). Consequently, they do not lend themselves to the problem of coordinated driving. There lies a gap between what we know about resilient network control, and what we know about resilient path coordination. The main contribution of this research programme is to fill this gap by providing methods for resilient planning of trajectories for car-like vehicles with lane constraints.

Our methodology is based on a juxtaposition of centralized and decentralized path planning: we will leverage decentralized planning to guarantee collision-free paths at all times and in all circumstances, and couple this with centralized planning that optimizes global objectives whenever possible. The aim is to develop an adaptive algorithm that slides between the distinct modes as a function of real-time factors that define the level of cooperation in the multi-vehicle system. Such sliding mode architectures have yet to be established within the context of connected multi-vehicle systems, where vehicles cannot be assumed to be cooperative at all times. The proposed research will build upon the expertise of the PI in the field of resilient control, inter-vehicular coordination, and optimization.

The implications of this research are expected to contribute to the theory of multi-vehicle path planning and control, with direct applications to connected and autonomous vehicles. This will ultimately contribute to the improvement of future road transport systems, addressing both safety as well as efficiency.

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Organisation Website: http://www.cam.ac.uk