Driving Benchmark Structure
The figure below shows the general structure of the driving benchmark module.
Figure: The general structure of the agent benchmark module.
The driving benchmark is the module responsible for evaluating a certain agent in an experiment suite.
The experiment suite is an abstract module. Thus, the user must define its own derivation of experiment suite. We already provide the CoRL2017 suite and a simple experiment suite for testing.
The experiment suite is composed by set of experiments. Each experiment contains a task that consists of a set of navigation episodes, represented by a set of poses. These poses are tuples containing the start and end points of an episode.
The experiments are also associated with a condition. A condition is represented by a carla settings object. The conditions specify simulation parameters such as: weather, sensor suite, number of vehicles and pedestrians, etc.
The user also should derivate an agent class. The agent is the active part which will be evaluated on the driving benchmark.
The driving benchmark also contains two auxiliary modules. The recording module is used to keep track of all measurements and can be used to pause and continue a driving benchmark. The metrics module is used to compute the performance metrics by using the recorded measurements.
Example: CORL 2017
We already provide the CoRL 2017 experiment suite used to benchmark the agents for the CoRL 2017 paper.
The CoRL 2017 experiment suite has the following composition:
- A total of 24 experiments for each CARLA town containing:
- A task for going straight.
- A task for making a single turn.
- A task for going to an arbitrary position.
- A task for going to an arbitrary position with dynamic objects.
- Each task is composed of 25 poses that are repeated in 6 different weathers (Clear Noon, Heavy Rain Noon, Clear Sunset, After Rain Noon, Cloudy After Rain and Soft Rain Sunset).
- The entire experiment set has 600 episodes.
- The CoRL 2017 can take up to 24 hours to execute for Town01 and up to 15 hours for Town02 depending on the agent performance.