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Update tuto_first_steps.md (#5774) 

Resolve a typo in the spawning random vehicles section.

Co-authored-by: bernat <bernatx@gmail.com>
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Lin Zhengyang 2023-01-30 16:32:27 +08:00 committed by GitHub
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Docs/tuto_first_steps.md
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@ -118,7 +118,7 @@ spawn_points = world.get_map().get_spawn_points()
# Spawn 50 vehicles randomly distributed throughout the map
# for each spawn point, we choose a random vehicle from the blueprint library
for i in range(0,50):
world.try_spawn_actor(random.choice(vehicle_blueprints), random.choice(spawn_points)))
world.try_spawn_actor(random.choice(vehicle_blueprints), random.choice(spawn_points))
```
Now we should also add a vehicle that will be the centerpoint of our simulation. To train an autonomous agent we need to simulate a the vehicle that it the autonomous agent will control. In CARLA parlance, we often refer to this vehicle as the "Ego vehicle".
@ -172,4 +172,4 @@ for vehicle in world.get_actors().filter('*vehicle*'):
Now your simulation is running, with numerous vehicles driving around the map and a camera recording data from one of those vehicles. This data can then be used to feed a machine learning algorithm for training an autonomous driving agent. The Traffic manager has many functions for customising traffic behaviour, learn more [__here__](tuto_G_traffic_manager.md).
This is the most basic possible set up for a simulation, now you can go into further details deeper into documentation about the many extra sensors you can use to generate data, and the many other features of CARLA that can make your simulations more detailed and more realistic.
This is the most basic possible set up for a simulation, now you can go into further details deeper into documentation about the many extra sensors you can use to generate data, and the many other features of CARLA that can make your simulations more detailed and more realistic.