Scottish data set looks to improve AD systems in poor weather

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Sensors capable of operating in Scotland’s infamous rain, snow and fog are providing data that could help autonomous vehicles see and operate safely in adverse weather. Thanks to the Radiate project led by Heriot-Watt University, a new data set has been published that includes three hours of radar images and 200,000 tagged road actors, including other vehicles and pedestrians, gathered in the country’s often inclement weather.

The data set helps solve a problem that has been facing manufacturers and researchers of autonomous vehicles. Until now, almost all the available, labeled data has been based on sunny, clear days. This meant there was no public data available to help develop autonomous vehicles that can operate safely in adverse weather conditions. It has also relied primarily on data collected from optical sensors, which, much like human vision, don’t work as well during bad weather.

Professor Andrew Wallace and Dr Sen Wang of Herriot Watt have been collecting the data since 2019, when they kitted out a van with light detection and ranging (lidar), radar and stereo cameras, and geopositioning devices.

They drove the car around Edinburgh and the Scottish Highlands to capture urban and rural roads at all times of day and night, purposefully chasing bad weather.

Wallace explained. “Data sets are essential to developing and benchmarking perception systems for autonomous vehicles. We’re many years from driverless cars being on the streets, but autonomous vehicles are already being used in controlled circumstances or piloting areas. We’ve shown that radar can help autonomous vehicles to navigate, map and interpret their environment in bad weather, when vision and lidar can fail.”

The team is based at Heriot-Watt’s Institute of Sensors, Signals and Systems, which has already developed classical and deep learning approaches to interpreting sensory data.

The team says its ultimate goal is to improve perception capability. Wallace concluded, “We need to improve the resolution of the radar, which is naturally fuzzy. If we can combine hi-res optical images with the weather-penetrating capability of enhanced radar that takes us closer to autonomous vehicles being able to see and map better, and ultimately navigate more safely.”

The dataset can be downloaded here: http://pro.hw.ac.uk/radiate/

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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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