Researchers at Oxford University's Mobile Robotics Group have compiled a massive public dataset for self-driving vehicles culled from thousands of hours of data from a single 10-kilometer (slightly more than six miles) stretch of road over a 12-month period.
The project emphasizes how the sharing of such data could expedite the deployment of driverless cars.
The researchers monitored the sort of variation self-driving cars will need to contend with every day, such as moving vehicles, cars parked in different ways, and lighting variations. Oxford's Will Maddern says longer-term changes are tracked as well, including "construction, roadworks, seasonal changes in vegetation, etc."
Maddern notes one of the purposes for the dataset was to gain insights into at what points autonomous vehicle systems would malfunction, especially those that depend on precise mapping.
Oxford's data on showing variation along a single route will be more extensive than that collected by Google and Tesla.
Massachusetts Institute of Technology professor John Leonard envisions large-scale and long-duration datasets providing "a huge boost to the rate of progress." He believes the sharing of such data by self-driving vehicle developers could accelerate the technology's practical use.
From Technology Review
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