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Uber ATG R&D

Research Areas

  • Perception and prediction

    ATG’s work perceives and understands the dynamic environment around a self-driving car through a multi-sensor setup and uses this sensor information to predict possible future states of the world.

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  • Motion planning

    Given the observations and the predictions of the dynamic actors around the self-driving vehicle, as well as the static constructs provided by HD maps, the problem of motion planning is to efficiently solve for the trajectory the self-driving vehicle will follow.

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  • Simulation

    Simulation facilitates realistic scenario evaluation and training for the entire self-driving stack, allowing for scalable research and safety analysis of self-driving algorithms.

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  • Mapping

    AI-based methods to automate and assist human-in-the-loop HD map production processes through machine learning and computer vision applied to offline map generation pipelines.

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  • Localization

    Efficient and effective multi-sensor approaches help localize the self-driving vehicle within the HD map in real time. Our research focuses on LiDAR point clouds, camera imagery and other sources of information to boost the robustness of localization algorithms.

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  • Core AI tech

    The fundamentals of machine learning science such as sparse and efficient operations for real-time inference, theoretical analysis of the robustness and safety of neural networks, stochastic and non-linear optimization, learning in the presence of noisy biased data, and more.

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