![]() ![]() Although these situations are not experienced everyday, a self-driving vehicle still needs to learn how to deal with it. These rare scenarios can include a shopping cart in the middle of the roadway. The dataset includes all of the complexities that human drivers encounter every day in urban areas, such as navigation in busy intersections and identifying road signs and following traffic laws It also includes extreme real world edge-case scenarios that are only experienced roughly one time every 1,000 hours of driving. It contains around 500 million images and 100 million lidar scans. The dataset contains 1,500 hours, or 4.7 years of average driving data that was collected across four different cities where Motional is testing its robotaxis: Boston, Pittsburgh, Las Vegas, and Singapore. It's essentially a "virtual driving test" that contains a large-scale machine learning dataset and a toolkit for measuring the performance of motion planning techniques for self-driving vehicles. ![]() nuScenes is composed of millions of photos and data-points collected from the vehicles' full sensor suites, were then hand-annotated, and used to inform and advance machine learning models to build the safest possible self-driving vehicles. The initial release of nuPlan and follows the release of "nuScenes" in March 2019. The company believes that nuPlan will help fill this gap by providing an ML-based planning dataset, closed-loop evaluation, and planning related metrics. While ML-based planning has been studied extensively, the lack of published datasets that provide a common framework for closed-loop evaluation has limited progress in this area, according to Motional. The data in nuPlan will be available to the public and can be used to teach an autonomous vehicle how to handle unique driving situations. Motional says the nuPlan data set is the world's first benchmark for autonomous vehicle planning.įor developers of autonomous vehicles, this type of data is used to train machine learning algorithms so self-driving vehicles can navigate safely. The new open-source planning dataset will allow researchers to better understand how a driverless vehicle can find its way through a dynamic environment, including driving in the city like a human driver. Autonomous driving joint venture Motional, which was formed by Hyundai Motor Group and Aptiv to develop autonomous driving technology for the automaker and as well as ride-hailing company Lyft, announced the launch of the initial version of a new open dataset called "nuPlan", which the company says is the world's largest public dataset for autonomous vehicle prediction and planning. ![]()
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