Ford harnesses on and offboard data to enhance driver safety

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Ford has developed a concept dubbed RoadSafe, intended to alert drivers as they approach higher risk locations, while also sharing information with relevant local authorities.

According to the company, RoadSafe utilizes algorithms to process data from sources including connected vehicles, roadside sensors and accident reports, to pinpoint where there is a higher chance of traffic incidents occurring. This information can then be displayed on a map that identifies the level of risk and could also be used to warn drivers of hotspots.

“There are areas in every city where the chance of an incident is higher, whether it’s due to a poorly placed sign, an unrepaired pothole or junctions built to accommodate far less traffic than we have today. Ford can pinpoint the areas of concern, so drivers could be made more aware of them and authorities can address them,” said Jon Scott, City Insights project lead, Ford Mobility Europe.

The digital tool is the culmination of four years of research by Ford, including most recently a 20-month government-funded project conducted together with Oxfordshire county council in the UK, Loughborough University and AI sensor specialist Vivacity Labs, with support from Transport for London and backing from Innovate UK.

The research began with an analysis of Greater London to highlight road safety hotspots and to identify potential causes and safety mitigations. The research then expanded to Oxfordshire, with more than 200 passenger and commercial vehicles voluntarily connected in the two locations. The data enabled the team to develop a ‘Road Segment Risk Rating Heat Map’, accessible via a dashboard, which identifies stretches of road that are of particular concern.

The dashboard includes various layers of data, including historic accident data and a ‘Risk Prediction’ rating algorithm for each road segment based on a range of data inputs, calculated using advanced data analytics techniques. The ‘Road Segment Risk Prediction’ rating uses colors to show where incidents are more likely to happen, with red having the highest risk level and yellow the lowest.

To gather the data, the connected vehicles record driving events, including braking, steering and accelerating, while Vivacity’s road-side sensors track the movements of different modes of transportation. The sensors employ machine-learning algorithms to detect near-miss incidents and analyze movement patterns of vulnerable road-users such as cyclists and pedestrians, as well as non-connected vehicles. All data shared by the sensors is anonymized with video feeds discarded at source, enabling safer roads without intruding on privacy.

Combining vehicle and sensor data can, Ford says, help identify a wide variety of hazards such as places where vehicles pass too close to cyclists; a poorly located bus stop causing traffic to become congested; and badly designed infrastructure such as a roundabouts (traffic circles) and junctions causing confusion and near-misses.

In the future, Ford hopes the technology could also benefit passengers riding in autonomous vehicles. Combining the onboard sensors of the vehicle with a digital tool could help them anticipate hazardous situations even earlier and therefore adapt their operation accordingly.

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Lawrence has been covering engineering subjects – with a focus on motorsport technology – since 2007 and has edited and contributed to a variety of international titles. Currently, he is responsible for content across UKI Media & Events' portfolio of websites while also writing for the company's print titles.

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