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Real-Life Driving Data to Improve Autonomous Vehicles

1st November 2018

Real-Life Driving Data to Improve Autonomous Vehicles


A new partnership aims to use human driver data to improve the performance and acceptability of connected and autonomous vehicles (CAVs). ‘Learning through AMBient Driving styles for Autonomous-Vehicles’ (LAMBDA-V), is a one-year feasibility study into how human driver behaviour can be analysed and used to accelerate the adoption of CAVs.

For example, the likelihood of a human driver swerving to avoid a pothole, or how and when they apply the brakes when entering a 30mph zone. This data can help better inform the decision-making of CAVs.

It is part of the UK Government’s £22m funding from the Centre for Connected and Autonomous Vehicles (CCAV) for projects to develop autonomous vehicles.

The lead partner is CloudMade, bringing expertise in machine learning and human driver behaviour modelling. The other partners include Trakm8, which will collate and analyse anonymised sample data from thousands of vehicles. The other consortium partners are Birmingham City Council as the highway authority with legal powers and duties; and smart mobility software expert Aimsun.

James Brown, CTO of CloudMade, said: "Understanding human behaviour and modelling this behaviour is one of the key elements in humanising Autonomous Vehicles and enabling personalisation of the vehicle."

"CloudMade, with its extensive experience and expertise in machine learning and human behaviour profiling, is uniquely positioned to utilise these capabilities in this programme. The CCAV grant will enable us to accelerate the development of solutions that learn individual driver behaviour and derive the necessary rule-sets and approaches to modelling and adapting to this during the drive."

LAMBDA-V is a one-year study on 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 the needs of both regulators and customers.

The project will 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 CAVs 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.

New rules for safer and more efficient driving behaviour may be built from data from existing vehicles, based not just on road laws but on how humans drive vehicles in specific circumstances. These could be 'tuned' by modelling how CAVs and other vehicles then behave in a mixed fleet, which will help to tailor early CAV behaviour to match that of human drivers and thereby improve confidence for early adopters.

The key output will be identifying potential product improvements for all partners to make data, modelling and rules generate new sales. The benefits would include: 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; and improved understanding for highways authorities and vehicle makers regarding how to deploy CAVs on a variety of real-world roads.

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