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Waymo's Driverless Data Fields


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This article from datapro.news explores the data engineering behind Waymo's self-driving vehicles, focusing on how they determine right-of-way at four-way stops. The author details the multi-sensor system (LiDAR, cameras, radar) and sensor fusion algorithms used for environmental perception. This perception data feeds into deep learning models (VectorNet, CNNs, RNNs) to predict the behaviour of other road users. Decision-making combines rule-based systems, reinforcement learning, and planning algorithms. Finally, the piece highlights Waymo's rigorous training, simulation (Carcraft), and continuous improvement processes, including data collection, model updates, and human oversight, to ensure safety and reliability.

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The Data Radio Show - Bought to you by the Data Innovators ExchangeBy Paul Barlow