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F1 fan crowdsourcing winners re-imagine what telemetry might look like

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F1 fan crowdsourcing winners re-imagine what telemetry might look like
Jul 31, 2015, 11:17 AM

The winners have been announced of the first challenge of the F1's crowdsourcing challenge the F1 Connectivity Innovation Prize, which asked contes...

The winners have been announced of the first challenge of the F1's crowdsourcing challenge the F1 Connectivity Innovation Prize, which asked contestants to get involved in something that goes right to the heart of how an F1 team operates at the race track.

They were asked to design a new approach for displaying critical race car telemetry.

Screen Shot 2015-07-30 at 21.18.28

The opening challenge, the first of two in this seasons competition, was set by the Mercedes team. It asked users to imagine the new display for all the data collected via on-board sensors to enable engineers at the race track and at the team's factory in the UK to make faster decisions through real-time visualisation of data.

The F1 CIP is an initiative backed by F1 Management, Mercedes AMG Petronas F1 team and Tata Communications. The top prize is a cheque for $50,000 and a trophy, to be presented to the winner at the end of the season in November. The five runners-up of both challenges will be awarded VIP trips to Abu Dhabi GP, the last round of the F1 championship.

The three winning entries are:

Screen Shot 2015-07-30 at 21.06.00

Winning entry 1: team entrants Ravi Sawhney and Svetlana Sawhney, UK

Idea: Databricks (click to enlarge) is an application that combines simple, yet effective visualisation of data with machine learning that would enable the team to make more timely and accurate decisions. A databrick displays variables such as speed in a range of graphical, brick-like formats. In addition to visualising data, a databrick can learn from what has happened in the past for a driver on an exact position on the track in similar conditions.

By comparing what it is seeing in real-time with what has happened in the past, it can alert race engineers to unusual patterns, enabling predictive decision making. Visually, databricks live inside a canvas where they can be moved through a simple touch-based interface to allow the team to access insights as quickly as possible.

Screen Shot 2015-07-30 at 20.56.49

Winning entry 2: team entrants Marco EinÖder and Leire Apraiz, Spain

Idea: This Graphic User Interface system (click to enlarge) uses a modular approach to display key variables such as tyre degradation and temperature, to help enhance the accuracy of decision making during races.

It uses high-impact colours and two different block types – a square core block for key variables, and a series of rectangular blocks to display other information to give the main data points additional context. Race engineers are able to place the rectangular contextual blocks anywhere around the main square block, making the system scalable and customisable.

The solution taps into race engineers’ photographic memory by placing functionally relevant data together, such as information relating to tyres, driver’s actions, speed and mechanical systems.

Screen Shot 2015-07-30 at 20.56.12

Winning entry 3: individual entrant Paul Clarke, Australia

Idea: Signals and Streams (click to enlarge) is a creative combination of traditional web technologies and state-of-the-art protocols, which brings consolidated data insights to the team in real time. The incoming data, Signals, are distributed to race engineers the instant they are received.

To help the team make sense of the wealth of data they receive, they would be provided with an artificial analyst called ARROW. Powered by machine learning, ARROW guides accurate decision making in a data-saturated, high pressure environment such as F1.

To help the team communicate regarding the Signals they receive, the solution is equipped with an instant communication feature called Streams. The combination of these tools enables the team to consume the huge amounts of data they gather during each race and react as effectively as possible.

Paddy Lowe, Mercedes F1 Technical chief said, “We’ve been hugely impressed by the response to the challenge we set to consider how to derive clear advantage from the considerable volume of data we generate at the track – and importantly, to provide this competitive insight both in real-time and to a high standard of accuracy.

"The winning entries are all commendable in their originality as well as their viability, and represent the best of a very impressive array of submissions.”
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Series Formula 1
Tags innovation