Comments (4)
This is due to the preset routes. Most of the time is simply lane following (which I have merged with straight)
from 2020_carla_challenge.
Training the MapAgent on this data would probably result in a very biased agent that would perform poorly at turns and lane changes?
from 2020_carla_challenge.
in our experiments, we've seen the map agent to do quite well due to the nice representation of the map
the image agent, if trained naively, fails in the ways you mentioned
to do better across all navigational commands, we use the all-branches supervision from LBC, which acts as a sort of nice sampling
note: in this repo, we don't condition on discrete actions, but actually render a sort of "command heatmap" that you can see in the readme. I found these to work better in the CARLA challenge
from 2020_carla_challenge.
Thanks for the clarification.
from 2020_carla_challenge.
Related Issues (20)
- Updated image model weights for CARLA 0.9.10 HOT 6
- Map not found Error while running a docker file in local machine
- Generation of Routes and Scenarios HOT 1
- Plotting image waypoints from the BEV perspective
- Is the data collected in map view? HOT 2
- Segmentation model weights? HOT 2
- route_19.xml, route_15, route_10 does not work HOT 7
- Not able to train STAGE_1 HOT 5
- The performance is not as good as LBC
- Does map model training use data augmentations?
- CoordConverter HOT 2
- Results of running a pretrained model is FAILURE HOT 2
- Where can I download the teacher model HOT 1
- Unknown error when submitting docker image to leaderboard benchmark HOT 2
- about sensormotor agent HOT 1
- the trained time took HOT 1
- Trying to update to work with CARLA 0.9.13 HOT 3
- cannot find image_agent.py HOT 2
- Pretrained Model Failed All with AgentBlocked
- About Converter world <--> cam conversion hack HOT 1
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