Ford Sensor Technology Applied to Motorcycles for New Mobility Insights

December 11, 2015 • News, Road, Transport

Improves Medical Care for Rural Africa

Ford is now equipping motorcycles with sensor kits inspired by its OpenXC hardware platform to map areas of community health care services in rural West Africa. Capturing data on additional transportation types is helping researchers detect patterns and develop new mobility solutions

Researchers have used OpenXC technology to collect real-time data from vehicles to better understand driving habits and help create new, novel features for today’s car

OpenXC has been used in several experiments tied to Ford Smart Mobility, the company’s plan to take connectivity, mobility, autonomous vehicles, the customer experience and data and analytics to the next level. Learnings could be expanded to other transportation modes – helping ambulance and emergency services providers improve efficiency across the world

DEARBORN, Mich., Dec. 11, 2015 – Ford is expanding its use of sensor technology to motorcycles, helping researchers and programmers better understand how cars, bikes and other modes of transportation together can create new mobility solutions and make people’s lives better – including improving healthcare in rural West Africa.

“OpenXC started as a project to make a car send a tweet five years ago, but has since become a platform, or an ‘Internet of mobility’ that allows us to use data to better understand how people move around the world,” said Ken Washington, Ford vice president, Research and Advanced Engineering. “Now, the same open innovation mentality behind OpenXC has inspired our team to create a sensor kit for bicycles and motorcycles to learn how other transportation options might best serve people in urban, suburban and rural areas, including improving their health.”

Ford’s open-source hardware and software kit provides real-time access to vehicle data, such as sensors, GPS receiver and vehicle speed. Ford has been using OpenXC to support some of its Ford Smart Mobility experiments for more than a year.

The company is gathering and analyzing vehicle data collected by OpenXC as part of Ford Smart Mobility, its plan to take connectivity, mobility, autonomous vehicles, the customer experience, and data and analytics to the next level.

Data-driven motorcycles

The broad insights learned from vehicle data, including how people drive and use their cars, first inspired Ford researchers to create a sensor kit for bicycles to collect additional data. Now, the company is rolling out the new sensor kit to motorcycles helping Riders for Health.

The medical services group collects GPS data and mapping coordinates to reach people who need medical care – vaccines, medications and live-saving hospital care – in rural West Africa.

Ford helped Riders for Health improve its maintenance systems and vehicle fleet logistics, equipping Ford Ranger pickups with OpenXC technology. This allows Riders for Health to track stops, timing and routes for their work in The Gambia.

The data collected also is being used to create maps of remote regions – a first.

After learning more than half of the group’s fleet of service vehicles are motorcycles that cannot capture the right level of data, Ford created a new sensor kit that will be upfitted on 50 motorcycles early next year.

“Our goal is to understand what mobility means to people who don’t have access to their own vehicles,” said Arthur Zysk, Ford research analyst who leads the project. “Ford’s commitment to smart mobility innovation is driving real, measurable change.”

Longer-term lessons and applications from this project could be used to help ambulance and emergency services providers improve efficiency across the world, including in rural areas.

Urban mobility learnings

Engineers at Ford’s Research and Innovation Center Palo Alto have been using sensor kits that gather information from bicycles and other common forms of transportation in urban areas.

The devices gather information such as wheel speed, acceleration and altitude, as well as traffic patterns, pedestrian data and road conditions, which is difficult to obtain from vehicle sensors.

Researchers continue exploring how bike and vehicle data can be analyzed together to gain greater understanding of how different transportation modes might best meet future mobility needs.