Ford expands sensors to motorcycle

Ford is expanding its use of sensor technology to motorcycles to allow researchers and programmers to better understand how cars, bikes and other modes of transportation can create new mobility solutions.

‘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 analysing 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.

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.

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 analysed together to gain greater understanding of how different transportation modes might best meet future mobility needs.