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Autonomous vehicles

Autonomous, or self-driving, vehicles have been hard to miss in the news recently, whether this be Tesla's partially automated 'Auto Pilot' feature, or the fully driverless 'pods' that arrived on the streets of Milton Keynes in October 2016.

As the technology becomes more familiar, people are becoming increasingly confident that individual vehicles will be able to drive and navigate themselves on roads and around people. But a single self-driving car is of limited value, it needs to work as part of an existing transport system – therefore conversations are now moving towards ‘how will they actually work in a city network’. Currently one pod can move one person (maybe two if sharing), but it needs a trained safety driver to be in the vehicle, plus traffic cameras to monitor its every move and to make sure it does what is expected.

This is expensive, so to make autonomous urban transport more efficient - while maintaining safety - we need to share this supervision between the pods and external systems (cameras and humans). We aim to achieve this by using Swarm Intelligence (what bees or ants do when part of a colony) to enable real-time, collaborative supervision of pods – meaning individual pods are locally supervised, not only by cameras or humans, but by neighbouring pods in the Swarm colony.


Multi-objective Optimisation of Pod Fleet

Technology inspired by ‘Mother Nature’ is being developed to help manage fleets of driverless pods. The concept is based on fusing together existing information from other vehicles in the fleet to allow each pod to locally decide the most appropriate action for the group as a whole – similar to how insects and birds currently behave. This means that pods can highlight any unexpected behaviour to a supervisor, as well as giving local authorities the chance to take advantage of ‘platooning’, where vehicles follow each other when possible to minimise the number or individual vehicle movements. The technology also makes the system automatically adapt its behaviour to meet demand so that pods can be optimally distributed within a city to the areas where they are most likely.



Co-simulation of Autonomous Vehicles

This project will use WMG’s ‘3xD simulator for Intelligent Vehicles’ in order to conduct remote co-simulations between a simulated pod and a physical pod, located at RDMs Urban Development Lab).


  • Kampert, Erik, Schettler, Christoph, Woodman, Roger, Jennings, Paul A., Higgins, Matthew D., 2020. Millimeter-wave communication for a last-mile autonomous transport vehicle. IEEE Access, 8, pp. 8386-8392. DOI: 10.1109/ACCESS.2020.2965003
  • Woodman, Roger, Lu, Ke, Higgins, Matthew D., Brewerton, Simon, Jennings, Paul A., Birrell, Stewart A., 2019. Gap acceptance study of pedestrians crossing between platooning autonomous vehicles in a virtual environment. Transportation Research Part F: Traffic Psychology and Behaviour, 67, pp. 1-14. DOI: 10.1016/j.trf.2019.09.017
  • Woodman, Roger, Hill, William D., Birrell, Stewart A., Higgins, Matthew D., 2019. An evolutionary approach to the optimisation of autonomous pod distribution for application in an urban transportation service. 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy, 23-26 Oct 2019, Published in 2019 23rd International Conference on Mechatronics Technology (ICMT), pp. 1-6. DOI: 10.1109/ICMECT.2019.8932138
  • Woodman, Roger, Lu, Ke, Higgins, Matthew D., Brewerton, Simon, Jennings, Paul. A., Birrell, Stewart A., 2019. A human factors approach to defining requirements for low-speed autonomous vehicles to enable intelligent platooning. 2019 IEEE Intelligent Vehicles Symposium (IV), 9-12 Jun 2019, Paris, France, pp. 2371-2376. DOI: 10.1109/IVS.2019.8814128



  • WMG, University of Warwick
  • RDM Group
  • Milton Keynes Council

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