We also provide the analysis code that details how we collect the data and how we preprocess it. The data and code were collected from a fleet of self-driving vehicles, allowing us to collect data in a variety of different environments. The vehicles were capable of changing velocity and acceleration in real time in response to changes in their environment, including self-calibrating to make sure that they were having a smooth ride!
If you are sharing this data or any of the code used to create it, please provide a link to this project. This will ensure that we can credit the data source, and help share the benefits of this dataset with the wider community.
In the source code , we plan to make the videos available to the public, so they can be used for research. We would also like to share the videos as they are collected in the streets, to enable the public to participate in the research.
The dataset has been described in detail in the paper “Driving in Real-World Environments with Autonomous Vehicles: Data, Models, and Applications” (2017). The authors’ Github repo contains the dataset, as well as code that allows for pre-processing and training on this dataset. If you use this data, please cite: “Driving in Real-World Environments with Autonomous Vehicles: Data, Models, and Applications” , “The Stanford Driving Dataset” , “The Stanford Driving Dataset” .
Moon Gorilla is not responsible for any change to Coolorus, its operation, or any other Services, and the Licensee is not entitled to compensation or reimbursement of any kind for any losses that it may suffer.
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One of the drawbacks to this approach was that we needed to recompile every time we made even a small change to the code. This slowed everything else we were doing down and because we couldn't always have someone waiting for the application to finish building. 827ec27edc