Smart cities have attracted significant attention in the context of urban development policies for a sustainable cities future. Infrastructure and use of new technologies are vital to make a city truly “smart”. Mobility is recognised as a crucial element to support the functioning of the urban area for improved quality of services and life. Current transportation networks become overloaded resulting to both increased levels of congestion and of pollution. This is a funded joint PhD project between Cranfield University, UK (CU) and Université de Technologie de Compiègne, France (UTC) which will investigate the link between Multi-Vehicle Systems (MVS) and interactive smart mobility infrastructure layer. Smart mobility infrastructure facilities in UTC and CU support a realistic problem investigation.

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In the last decade the concept of smart cities has attracted significant attention in the context of urban development policies for a sustainable cities future. Infrastructure and use of new technologies are vital elements to make a city truly “smart”. Mobility is recognised as a crucial element to support the functioning of the urban area for improved quality of services and life. Current transportation networks become overloaded resulting to both increased levels of congestion and of pollution.

We are at an era strongly encouraging the utilisation of smart mobility with efficient vehicle operation, cooperative systems, innovation and optimization of city resource allocation with intelligent transportation systems to addressing the following objectives:

  1. reduce pollution; 
  2. reduce traffic congestion; 
  3. improve transfer speed (planning); 
  4. reduce transfer costs; 
  5. reduce noise pollution; 
  6. improve people safety.

This research will lead to a proposed solution for control architecture combining a global component (vehicle-vehicle interaction and interaction with supervisory infrastructure) to enable improved mobility in cities. The work will investigate the link between Multi-Vehicle Systems (MVS) and interactive smart mobility infrastructure layer. The work falls within the remit of efficient and smart mobility within future smart cities. Current smart mobility infrastructure facilities in UTC and CU support a realistic problem investigation (i.e. provision of data for the synthetic framework).

This PhD project is collaborative research between Heudiasyc (UMR UTC/CNRS, France) and Cranfield University (United Kingdom). The successful candidate will spend time both in France (between the SyRI team of Heydiasyc Laboratory), and in the UK (in the Autonomous and Cyber-Physical Systems Centre), 18 months each. The research team comprises complementary research excellence in control of complex systems (single and multi-robot/vehicle), Artificial Intelligence, Multi-Agent Systems, from UTC; and Systems Autonomy, Autonomous vehicle mobility, Intelligent systems, Real-time Decision making, from CU.

This thesis topic deals with the energy optimization of road traffic in cities using an optimal coordination of Multi-Vehicle Systems (MVS) within the existing infrastructure in the city. More specifically, this thesis aims at the coordination of MVS in city bottlenecks, essentially the intersections and roundabouts (e.g. considered as dynamic traversability obstacles for the vehicles). In these strategic nodes for traffic management of cities, vehicles are often required to brake / accelerate, or even come to a complete stop and then restart. All of these situations are extremely energy consuming for vehicles and correspond to one of the major causes of traffic jams (which is turn a major cause of energy consumption for these transport systems within city mobility). It is worth mentioning that these sudden changes in speed are also a major source of passenger discomfort.

The PhD candidate will investigate an overall control architecture combining a global level layer to reduce congestion in cities (which will be modeled in the form of a stochastic system), and a lower-level layer (corresponding to vehicle-vehicle (VV) or vehicles-infrastructure (VI) interactions directly in the strategic nodes of cities). The interactions between a macro-model (which will be modeled by a macro-model seeing the city as distinct arteries to be traversed) and a micro-model consisting of the interactions between VV and VI to cross intersections or roundabouts will be studied in a systematic way, in order to lead to more sustainable and safer traffic management/control.

This research will propose an efficient control architecture (decision-making under uncertainty / cooperative planning / low-level control) which can address high-level objectives emanating, in particular, from a global supervisory layer. The architecture targeted in this thesis will thus make it possible to optimize the overall energy consumed by vehicle formations at city level traversing their journey plan. At the low-level level, the vehicles interact locally in a distributed manner, or in an appropriate fleet, to respect both the supervisory layer macro-instructions (high-level) and ensure the comfort and safety of the maneuvers performed. Thus, according to these macro-instructions, the low-level aims primarily to plan a safe trajectory in a collaborative manner with neighboring vehicles, and which in addition consider the uncertainties of the environment (perception, delay of communication V2V, V2I, etc.). Fault tolerance will be considered in the VI layer, with sensor configuration from an interactive infrastructure point of view investigated, to support resilience in the framework.

Available ordnance survey maps and also data from Cranfield University’s, previous, MUEAVI (Multi User Environment for Autonomous Vehicle Innovation)-based projects, in particular on perception of vehicle movement seen from the available MUEAVI sensors (e.g. cameras, lidars), as well as data from previous VV (inter-vehicle) experiments at UTC will support data analysis and the baseline for the macro- model consideration.

At a glance

  • Application deadline24 Oct 2021
  • Award type(s)PhD
  • Start date31 Jan 2022
  • Duration of award3 years
  • EligibilityUK, EU
  • Reference numberSATM237

Supervisor

1st Supervisor: Dr Argyrios Zolotas
2nd Supervisor: Prof Lounis Adouane

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in engineering or a related discipline, a Master degree is a plus.

This project would suit candidates with the following:

  • Good mathematical/analytical background.
  • Programming skills (C++ and/or Java, Matlab/Simulink, Python and/or ROS).
  • Knowledge on mobile robot control and/or multi-agent systems, appreciation of systems and control, good familiarity with machine learning techniques.

Funding

To be eligible for this funding in full, applicants must be a UK/EU national.
 
Sponsored by Cranfield and UTC cotutelle studentship; this is a fully funded opportunity. The successful candidate will spend half the time in France and half the time in the UK.

About the sponsor

Sponsored by EPSRC, Cranfield University and SAAB UK, this studentship will provide a bursary of up to £18,000 (tax free) plus fees* for three years.            

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.


How to apply

For further information please contact:
 
  
If you are eligible to apply for this studentship, please complete the online application form.

In your application include: CV, a 1-page motivation letter, recommendation letter or the referree name (at least 1), degree results.

In addition to the online application you also need to forward the above documents by email to: a.zolotas@cranfield.ac.uk and Lounis.Adouane@utc.fr using subject 'Applied for the UTC/CU joint PhD - (include your application ID here)'.