carla VS CARLA

It sounds like you're looking for something like the CARLA simulator.

If you are looking for a different simulator, I would suggest using (Carla)[https://carla.org/] with ROS bridge and it also has an inbuilt support for OSM which worked flawlessly (you have to install it from source to get the OSM plugin).

(3) I would suggest you use CARLA or TORCS for self-driving cars in RL as they are common test beds.

carla env

Link to carla

There are a few ways you could go with this. A fun recent trend has been to make simulators in the unreal engine for photorealistic training. If you wanted to spend your whole project on the simulator part you could make your own environment, but I highly recommend using an open-source sim package. If you don't care too much about photorealism, you could use gazebo just fine. I've also made small visual worlds in blender, then simulated with RVIZ. Here's a good one with a focus on aerial robotics: https://theairlab.org/tartanair-dataset/ You could also give CARLA a spin for autonomous vehicles: https://carla.org/

Abstract: Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favourable outcomes in the future (e.g., insurance approval). Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. As documented in recent reviews, there exists a quickly growing literature with available methods. Yet, in the absence of widely available open–source implementations, the decision in favour of certain models is primarily based on what is readily available. Going forward – to guarantee meaningful comparisons across explanation methods – we present CARLA (Counterfactual And Recourse Library), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open sourced CARLA and our experimental results on GitHub, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.

4 Min Read| Paper | Github