With more than three-quarters of the project timeline having passed, we are proud to say that we are on track to achieve the project objectives within the allocated project time allowance, even with the current additional challenges of COVID-19 self-isolation restrictions.
We have developed automotive-grade indoor parking maps required for autonomous vehicles to localise and navigate within a multi-storey or underground parking facility, using a map-based approach to scale globally. This approach has been provisionally agreed by the ISO Technical Committee for Automated Valet Parking systems. Parkopedia has created indoor maps for a number of facilities around the world and is continuing to grow its indoor map inventory to supply to its customers. The associated localisation algorithms, targeting a minimal sensor set of cameras, ultrasonic sensors and inertial measurement units, have also been developed to make best use of these maps.
We have also tested the self-parking technology in a variety of locations. Great care is taken to account for parking ramps, as by necessity, the low concrete walls are at their closest to the car at this moment. These ramps are considered to be the point of greatest risk as the localisation methods have to work extra hard when changing height since they are usually only exercised on flat surfaces and the speed control algorithms need to account for gravitational acceleration of the car down the slope, and slowing it on the up-slope. After a lot of testing in simulation and hundreds of hours of in-car testing, we are pleased to have overcome this challenge, which you can see in action below.
Safety documents to cover the testing thus far have been published and a final document to cover demonstrations with large numbers of people is the last item outstanding. We have secured initial agreement for further trials and a subsequent demonstration in a different car park to showcase the functionality in a different environment.
The outstanding work items now exclusively relate to integration with the map and localisation algorithm, but we are confident of completing the project on time with our objective achieved. We are looking forward to the day this feature is available in a production vehicle!
Brian leads our engineering teams as CTO covering product, platform and infrastructure, data science, computer vision and robotics. Prior to this he led the R&D effort to develop Indoor Maps, which also included the successfully completed multi-million pound AVP Project.
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