[vojtamolda]

I think a fair comparison would be to AirSim [1] and Carla [3]. These are much more mature projects and are similar to Autodrome in a lot of ways. As far as I know Udacity's simulator is done their class and it's not being actively developed.

+ Both [1] and [3] have much fewer assets (like 3D models of houses, factories, bridges, cars, trucks, and so on) you'd have to buy them on the Unreal/Unity model marketplace and it still wouldn't be enough. Autodrome can take advantage of almost entire Europe and a third of USA at 1:20 scale.

+ Autodrome has a sparse map representation that is really easy to randomly fuzz. I.e. it's easy to shift a segment of the road a little bit and see how the algorithm would react to the fuzzed scenario. I believe this is only way how to achieve robust agents and effectively prevent testing on the training set.

- Biggest disadvantage of Autodrome is a lack of access to in-game dynamic NPCs (like other trucks, cars or pedestrians). As far as I know there's no API for this. Without help (or a lot of very fragile memory hacking) from the developers of the game this feature is very hard to achieve and both [1] and [3] already have it.

[3] http://carla.org

PS: Keep in mind that I'm the developer of Autodrome so I my objectivity is very questionable.

[syntaxing]

Thank you for the detailed response! Is there any thoughts on ground truth segmantic segmentation camera view? Simulated lidar data would be super awesome too.

[vojtamolda]

Both are also not easy like the dynamic NPCs. I think Carla [3] supports both raytraced lidar and segmented rendering now so this is another minus “-“ point.