Associate appointed to lead KTP between Aurrigo and Aston University to develop machine vision solution, improving operational safety and performance of autonomous vehicles.
Developing and implementing a sophisticated machine vision solution for autonomous vehicles in the Low-Speed Autonomous Transport Systems (L-SATS) sector – that is the subject of a Knowledge Transfer Partnership (KTP) between Aurrigo, a division of the Richmond Design and Marketing Group Ltd. (RDM), and Aston University’s College of Engineering and Physical Sciences.
The RDM Group are leaders in the UK L-SATS sector and introduced the first-ever driverless vehicle in 2017. Self driving cars have the potential to radically change transport and mobility: this KTP will accelerate the development of intelligent systems which have perception and autonomous decision capabilities. This will enable driverless cars to navigate in the presence of static and dynamic obstacles (e.g. road signs, pedestrians, cyclists, etc.) with a comprehensive understanding of the immediate environment, while following higher-level directions.
The objective is to use deep-learning techniques to enable safe interaction in mixed environments to achieve full autonomous (Level 5) vehicle capability which means vehicles will run outside confined vehicle free spaces, e.g. airports and closed campuses. This will require the development of an “Avoidance Dynamic Path Planning” capability, a critical innovation to achieve growth for RDM.
Controlling an agent from high-dimensional sensory inputs is a major challenge in any adaptive application. This project will exploit, for the academics, previous successes in deep reinforcement learning for traffic settings and apply it in an iterative manner between: exploration of the action space; optimisation for model update, and evaluation to verify the model’s performance.
James Heaton, recently appointed KTP Associate, is “relishing the technical challenges of this application”. He will be working with the team at Aurrigo and the academic team from Aston University including Dr George Vogiatzis and Dr Diego Faria.
With a Bachelor’s degree in Mechanical Engineering, a final year specialisation in robotics, and a recently completed Master’s in Artificial Intelligence James Heaton was the perfect fit for the KTP Associate role on the project. Over to him:
“In my first couple of months I have been working with a Far East Airport and Regulators to create a simulation environment for their airport. 3D laser scans were taken of the runway and surrounding buildings which I have turned into a rendered world using our processing pipeline linked to a game development platform. Using CAD models of the vehicles, we can place them in the world and have them drive around using a sophisticated physics engine.
Aurrigo has a number of interesting applications for their autonomous vehicles…Whilst [they] are constantly reported on in today’s media, these are almost always self-driving cars on public roads, and not much is known to the public about how we can use artificial intelligence and autonomous vehicles to improve the efficiency of something like baggage handling”.
Nick Ridler, engineering manager at RDM Group, commented, “The expertise of Aston’s academics and their specialist labs at Aston University are key, allowing us to run detection and recognition of obstacle scenarios. This data will be used in our decision-making algorithms which will choose the optimal solution based on the environment“.
Jose Freedman, Knowledge Transfer Adviser on the project from KTN added “This is an exceptionally exciting and challenging KTP project which will lead to very significant benefits for the company, academic team and the Associate. I am delighted to be supporting them”.
The 2 year project received funding from Innovate UK and promises to deliver significant developments for the sector.
Want to find out more about collaborating with an academic team to drive innovation? More information here.
Interested in working as a KTP Associate? See some of the latest vacancies here.
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