Autonomous Driving Dataset Visualization with Python and VizViewer

Autonomous Driving Dataset Visualization with Python and VizViewer

Exploring the Lyft Prediction Dataset with a novel visualization toolkit .Autonomous Driving Dataset Visualization with Python and VizViewer

As part of a recently published paper and Kaggle competition, Lyft has made public a dataset for building autonomous driving path prediction algorithms. The dataset includes a semantic map, ego vehicle data, and dynamic observational data for moving objects in the vehicle's vicinity. The challenge presented by Lyft with this dataset is to use this data to build a model that can predict the paths of moving objects and the path an autonomous vehicle (“AV”) should take based on the observations made by the AV sensors and perception stack.

In more specific terms, the challenge is, given a set of information about the current vehicle state and its surroundings, to predict the best plan — a set of actions and behaviors — for the vehicle to safely navigate autonomously

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