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Autonomous Vehicle International (a trade mag) is running this story,
https://www.autonomousvehicleinternational.com/news/testing/aurora-announces-release-of-open-source-autonomous-driving-data-set-to-support-advances-within-the-sector.html [autonomousvehicleinternational.com] which describes a large vehicle sensor data set being made available to university researchers.

In partnership with the University of Toronto, Aurora Innovation has publicly released the Aurora Multi-Sensor Dataset, a large-scale multi-sensor data set with localization ground truth. The data set consists of rich metadata which includes semantic segmentation and a variety of weather patterns such as rain, snow, overcast cloud and sunshine, in addition to different times of day and varying traffic conditions.
[...]
By releasing the data set to the academic sector, Aurora aims to contribute “meaningful engineering research and development” to support progress within the autonomous systems field. Additionally, due to the size and the diversity of Aurora’s Multi-Sensor Datatset, it can be used for 3D reconstruction, HD map construction, map compression and more.

The data set was captured by Uber Advanced Technologies Group (ATG) in the metropolitan area of Pittsburgh, USA between January 2017 and February 2018. Aurora acquired the data set in January 2021. Uber ATG used a 64-beam Velodyne HDL-64E lidar sensor and seven 1920×1200-pixel resolution cameras, in addition to a forward-facing stereo pair and five wide-angle lenses providing a 360-degree view around the vehicle to capture the data.

The dataset is available at https://registry.opendata.aws/aurora_msds/ [opendata.aws] and the license information on that page is,

License
This data is intended for non-commercial academic use only. It is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Documentation
A third-party development kit authored by Andrei Bârsan of the University of Toronto, made available under the MIT License, can be found here: https://github.com/pit30m/pit30m. [github.com] Aurora makes no representations as to the functionality or performance of the dev-kit.

It's Pittsburgh, they have below freezing temps, the roads develop potholes, construction changes the roads and so on. My guess, while this dataset is large and rich, by now it's also less than accurate. Still, better than nothing for grant-starved university researchers.

Original Submission