Learning through AMBient Driving styles for Autonomous-Vehicles. LAMBDA-V

"Our vision is for the potential benefits in safety and capacity of highly automated vehicles (CAVs) to be achieved more quickly, by using data on 'real world' behaviour of human driven vehicles to define and rules for new automated ones that improve on human safety and driving capability. It may be feasible to build these from data from existing vehicles, based not just on road laws how humans drive vehicles in specific circumstances. These could be 'tuned' by modelling how CAVS and other vehicles then behave in a mixed fleet. This will help tailor early CAV behaviour to match that of human drivers, improving confidence for early adopters.

We want to understand the feasibility of processing existing massive datasets, to understand the parameters needed for modelling human drivers and how to extend them to make vehicle rules, improving current technology and modelling impact to balance comfort, capacity and safety. This could ensure CAV behaviour meets needs of regulators and customers.

We focus on innovatively exploring a full end to end data chain and business model in a mixed fleet environment. This integrates vehicle maker and road operator perspectives on CAV behaviour and examines how to develop privacy law compliant datasets for other CAV projects. It brings together those who develop CAV and modelling software with data from massive mixed fleets of anonymised drivers across the UK, rather than small fleets of specialised vehicles in one location.

Led by CloudMade, bringing expertise in machine learning and human driver behaviour modelling, the partners include Birmingham City Council as a highway authority with legal powers and duties, TSS from road operations and Trakm8 and the AA who will provide anonymised sample data from many thousands of AA member's vehicles equipped with the innovative Car Genie device. Our key output will be identifying potential product improvements for all partners to make data, modelling and rules generate new sales.

The benefits if the idea is feasible would be reduced unforeseen impacts on traffic, patents on rules for CAVS, an improved understanding of early mixed fleet operation of human and automated vehicles and how to make early level self driving vehicles attractive to users. It will help highways authoritiesand vehicle makers alike understand how to deploy CAVs on a variety of real world roads.

It is a 1-year feasibility study delivering technology innovation and business change needed for exploiting the idea globally."