[1]https://github.com/Microsoft/AirSim [2] https://github.com/udacity/self-driving-car-sim
+ 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.
Would you mind if at some point I share your project at https://weeklyrobotics.com ?
