AutonomousConnected Vehicle

Scale releases open source multi-sensor self-driving dataset

The nuScenes open source dataset is based on LIDAR point cloud, camera sensor, and RADAR data sourced from nuTonomy and then labeled through Scale’s sophisticated and thorough processing to deliver data ideal for training autonomous vehicle perception algorithms.

Scale has announced the release of what it claims the largest open source multi-sensor (LIDAR, RADAR, and camera) self-driving dataset published by nuTonomy, with annotations by Scale.

Academic researchers and autonomous vehicle innovators can access the open-sourced dataset, nuScenes.

Scale’s Sensor Fusion Annotation API, leverages machine learning, statisti`cal modeling, and human labeling to process LIDAR, RADAR, and camera sensor data into ground truth data, played a critical role in the creation of this new standard.

The nuScenes open source dataset is based on LIDAR point cloud, camera sensor, and RADAR data sourced from nuTonomy and then labeled through Scale’s sophisticated and thorough processing to deliver data ideal for training autonomous vehicle perception algorithms.

The open source tool made available by nuTonomy and Aptiv surpasses the public KITTI dataset, Baidu ApolloScape dataset, Udacity self-driving dataset, and the even the more recent Berkeley DeepDrive dataset that have until now served as the standard for academic and even industry use.

 nuScenes provides significantly greater data volume, accuracy, and precision; the full dataset will include 1,000 twenty-second scenes, nearly 1.4 million camera images, 400,000 LIDAR sweeps, and 1.1 million 3D boxes.

Similar to RADAR, LIDAR emits invisible infrared laser light that reflects off surrounding objects, allowing systems to compile three-dimensional point cloud data maps of their environments and identify the specific objects within them.

Correctly identifying surrounding objects from LIDAR data allows autonomous vehicles to anticipate those objects’ behavior – whether they are other vehicles, pedestrians, animals or other obstacles – and to safely navigate around them. In this pursuit, the quality of a multi-sensor dataset is a critical differentiator that defines an autonomous vehicle’s ability to perceive what is around it and operate safely under real-world and real-time conditions.

Scale, whose autonomous vehicle customers also include Lyft, General Motors (Cruise), Zoox, Nuro and many others, recently announced $18 million in Series B funding.

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