Basically, we needed a scalable and cost-effective solution to be able to deal with potentially petabytes of data. We needed to be able to process a large amount of data collected for building and training ML models for Autonomous Vehicles. This provides a comprehensive set of cloud, edge, vehicle, and AI services that enable an integrated, end-to-end workflow for developing, verifying, and improving automated driving functions. We aligned our solution with Microsoft’s AVOps reference architecture. This article highlights how we approached some of the concerns and constraints when developing a cloud-based data processing solution for AVOps (Autonomous Vehicle Operations). But to do all this, there are complex processing challenges with respect to the data collected for building this software. Rules, obstacle avoidance algorithms, predictive modeling, and object recognition help the software learn to follow traffic rules and navigate obstacles. Software then processes all this sensory input, plots a path, and sends instructions to the vehicle’s actuators, which control acceleration, braking, and steering. They create and maintain a map of their surroundings based on a variety of sensors situated in different parts of the vehicle like radar, cameras, and LiDAR (Light Detection and Ranging). Large-scale Data Operations Platform for Autonomous VehiclesĪutonomous driving systems depend on sensors, complex algorithms, machine learning systems and processors to run software.
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