TruPercept: Synthetic Data and Trust Modelling for Autonomous Vehicle Cooperative Perception
Real-world, large-scale semantic segmentation datasets are expensive and time-consuming to create. Thus, the research community has explored the use of video game worlds and simulator environments to produce large-scale synthetic datasets, mainly to supplement the real-world ones for training deep neural networks. Another use of synthetic datasets is to enable highly controlled and repeatable experiments, thanks to the ability to manipulate the content and rendering of the synthesized imagery.
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing work creating synthetic data for autonomous driving with GTA V have not released their datasets and rely on an in-game raycasting function which represents people as cylinders and can fail to capture vehicles past 30 metres.
Abstract. In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datasets. The limited availability of such data is due to the time cost and human effort required to accurately and consistently label real images on a pixel level. Modern sandbox video game engines provide open world environments where traffic and pedestrians behave in a pseudo-realistic manner. This caters well to the collection of a believable road-scene dataset.
"Autonomoose" is a collaboration between WISE Lab and WAVE Lab to build the University of Waterloo's self-driving research platform. The result is the "UW Moose": the first self-driving car built in Canada to drive on public roads.
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