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There is also... AirSim from Microsoft - https://github.com/microsoft/airsim https://github.com/microsoft/airsim Gazebo car simulator - https://www.osrfoundat

There is also... AirSim from Microsoft - https://github.com/microsoft/airsim https://github.com/microsoft/airsim Gazebo car simulator - https://www.osrfoundation.org/simulated-car-demo/ https://www.osrfoundation.org/simulated-car-demo/ Udacity car simulator - https://github.com/udacity/self-driving-car-sim https://github.com/udacity/self-driving-car-sim It would be great if someone has experience with any of above and comment on how they compare with CARLA.

giancarlostoro 9y ago
Also since some are using GTA for these sort of experiments I wonder if Grit is suited for this sort of thing as well: http://www.gritengine.com/ http://www.gritengine.com/ I believe Grit has Lua scripting built in as well. Looks like CARLA has some Python thrown in which is good too, but no direct mention about it. Edit: Youtube demo of Grit: https://www.youtube.com/watch?v=RpORM_it8l8 https://www.youtube.com/watch?v=RpORM_it8l8 (from 2014)
pj_mukh 9y ago
Use gazebo on the regular, but at first glance, only AirSim looks to use rendering real enough to test computer vision algorithms with. I do love that Udacity's simulator is in Unity though.
alexleegk 9y ago
Both AirSim and CARLA aim to fill in similar gaps in the current state of photorealistic simulators for perception and control research. Here are some differences one of my collegueas told me (he has used AirSim and beta-tested CARLA): AirSim + Also has quadcopter simulator (besides car) + Large environment + Easy to add new environments CARLA + Simulator can be stepped + Multiple weather conditions + Environment has pedestrians and other cars
cr0sh 9y ago
My experience with Udacity's simulator is purely from the perspective of having used it as a part of participating and completing their nanodegree program. First off, it's very easy to use; it's written in Unity and uses something like (or maybe is?) a websocket-like interface to communicate with the simulated vehicle. I'm not sure what its complete capabilities are, because we used a number of different versions over the course. Our initial uses were with a "closed source" version, but toward about the middle of the course it was opened up (I was in the October 2016 cohort, which was either the first or second group going thru - so we were very much "beta testers"). From my recollection, we could get camera info (from one camera in the "center" of the windshield - but there were supposedly two other cameras on the left and right corners - these cameras were similar in position to what was on the real Udacity car, "Carla" - no relation). We could also get waypoint data, plus a variety of other data from the "car"; we could send steering and acceleration/braking data. But at no point in the course did we get all of these bits of data at the same time; it was very much tailored to the lesson or project. Another note: I used this simulator on my workstation at home, which is a 6-core AMD system with 8 gig of RAM and a NVIDIA GTX 750 TI OC video card (getting to be long in the tooth). I run a very tweaked version of Ubuntu 14.04 LTS on it. I also set up TensorFlow and cuDNN, to utilize my GPU. I found that my system had no problem running various CNN models, training, etc at the same time as using the GPU to run the simulator. It seemed to work well for that, but it probably isn't ideal. As a simulator, I would say it is fairly simplistic, but it wasn't meant to be a production-ready solution, but rather a training/educational simulation for teaching the topic. I'd say it does that part well.

giancarlostoro 9y ago

Also since some are using GTA for these sort of experiments I wonder if Grit is suited for this sort of thing as well: http://www.gritengine.com/ http://www.gritengine.com/ I believe Grit has Lua scripting built in as well. Looks like CARLA has some Python thrown in which is good too, but no direct mention about it. Edit: Youtube demo of Grit: https://www.youtube.com/watch?v=RpORM_it8l8 https://www.youtube.com/watch?v=RpORM_it8l8 (from 2014)

pj_mukh 9y ago

Use gazebo on the regular, but at first glance, only AirSim looks to use rendering real enough to test computer vision algorithms with. I do love that Udacity's simulator is in Unity though.

alexleegk 9y ago

Both AirSim and CARLA aim to fill in similar gaps in the current state of photorealistic simulators for perception and control research. Here are some differences one of my collegueas told me (he has used AirSim and beta-tested CARLA): AirSim + Also has quadcopter simulator (besides car) + Large environment + Easy to add new environments CARLA + Simulator can be stepped + Multiple weather conditions + Environment has pedestrians and other cars

cr0sh 9y ago

My experience with Udacity's simulator is purely from the perspective of having used it as a part of participating and completing their nanodegree program. First off, it's very easy to use; it's written in Unity and uses something like (or maybe is?) a websocket-like interface to communicate with the simulated vehicle. I'm not sure what its complete capabilities are, because we used a number of different versions over the course. Our initial uses were with a "closed source" version, but toward about the middle of the course it was opened up (I was in the October 2016 cohort, which was either the first or second group going thru - so we were very much "beta testers"). From my recollection, we could get camera info (from one camera in the "center" of the windshield - but there were supposedly two other cameras on the left and right corners - these cameras were similar in position to what was on the real Udacity car, "Carla" - no relation). We could also get waypoint data, plus a variety of other data from the "car"; we could send steering and acceleration/braking data. But at no point in the course did we get all of these bits of data at the same time; it was very much tailored to the lesson or project. Another note: I used this simulator on my workstation at home, which is a 6-core AMD system with 8 gig of RAM and a NVIDIA GTX 750 TI OC video card (getting to be long in the tooth). I run a very tweaked version of Ubuntu 14.04 LTS on it. I also set up TensorFlow and cuDNN, to utilize my GPU. I found that my system had no problem running various CNN models, training, etc at the same time as using the GPU to run the simulator. It seemed to work well for that, but it probably isn't ideal. As a simulator, I would say it is fairly simplistic, but it wasn't meant to be a production-ready solution, but rather a training/educational simulation for teaching the topic. I'd say it does that part well.