LAMBDA-V: artificial intelligence starts with human data

Intelligent Transport Magazine

January 2019

Andy Graham, LAMBDA-V Project Manager, details how collecting the informal rules of driving could help highly autonomous vehicles learn about how human drivers really use UK roads.

 

LAMBDA-V: artificial intelligence starts with human data

Think of a regular journey you make from home – maybe to the nearest motorway, or the school run, or the supermarket. Now think of all the unusual parts of that journey where you drive using your personal experience of the route. Maybe there’s a part where the road narrows and you have to let people through by flashing your lights, or there’s a big pothole that you avoid by easing over to the other side of the road, or maybe there’s a set of junctions so close together that you must get in the right lane at the first junction to be able to turn right at the second.

One might think that highly autonomous vehicles (AVs) will simply use artificial intelligence to solve all these examples of ambient behaviour, but there are three challenges to this thinking…

Firstly, AVs will need to function alongside human-driven vehicles for many years. The initial few AVs in circulation will need to know what to expect from the majority of other vehicles – humans don’t follow algorithms and don’t always follow the Highway Code. Modelling mixed-autonomy streams of traffic requires better knowledge of how human drivers behave now, and how AVs might behave alongside them.

Secondly, if AVs are to be adopted quickly and in large numbers, the early autonomous products from Original Equipment Manufacturers (OEMs) will need to drive in a similar way to current human drivers, at least during the transition period.

Finally, highway authorities want to know the impact of AVs on traffic and how they can manage it efficiently and safely.

To explore the feasibility of developing solutions to these three challenges, a group of UK companies is collaborating in an Innovate UK-funded project called LAMBDA-V (Learning through AMBient Driving styles for Autonomous-Vehicles). Leading LAMBDA-V is a company called CloudMade, which brings expertise in machine learning and human driver profiling. Joining CloudMade is a unique mix of collaborators: Trakm8, which collects big data from existing vehicles; Aimsun, a provider of mobility modelling software for simulating the impact of AVs and conventional traffic; White Willow Consulting, which provides project support and links to other connected vehicle initiatives; and a UK Highway Authority, Birmingham City Council. The team aims to explore the possibility of building profiles and rules for AVs that can also be used in network modelling for local authorities.

Read the full article for more detail.