Skip to main content
The driver in AlpaSim is a policy for the ego vehicle that takes in sensor inputs and optional navigation commands, and outputs a trajectory for the ego vehicle to follow, along with other optional outputs, such as chain-of-causation reasoning text. The driver is specified by a pair of config files under src/wizard/configs/, one for the driver service itself, and one for the runtime (so that it provides the inputs required for the specific driver).

​
VaVAM (Default Driver)

The wizard uses VaVAM as the default driver. This is a vision-based action model that learns to drive from video inputs.

​
Using VaVAM

To explicitly define the VaVAM driver config: 
alpasim_wizard +deploy=local wizard.log_dir=$PWD/tutorial_vavam driver=[vavam,vavam_runtime_configs]

​
VaVAM Configuration

VaVAM runs at 2Hz inference frequency with the following parameters:
  • Inference frequency: 2Hz (500ms between decisions)
  • Force GT duration: 3 seconds
  • Simulation steps: 100
  • Camera resolution: 1920x1080
  • Frame interval: 500ms (synchronized with inference)
The model weights are automatically downloaded using data/download_vavam_assets.sh and stored in data/vavam-driver/.

​
Alpamayo-R1

Alpamayo-R1 is a reasoning-capable autonomous driving model with 10 billion parameters that can provide chain-of-causation explanations for its driving decisions.

​
Model Download

First, download the model weights from HuggingFace: 
huggingface-cli download nvidia/Alpamayo-R1-10B
The wizard will use the HF_HOME environment variable to find the system HuggingFace cache (~/.cache/huggingface by default). If the model weights do not exist locally, the driver service will automatically download them, but the download may timeout, requiring you to re-run.
Alternatively, you can specify the path to the model directory by setting the model.checkpoint_path configuration field.

​
Running Alpamayo-R1

1

Run with Alpamayo-R1

Execute the wizard with the Alpamayo-R1 driver configuration:
alpasim_wizard +deploy=local wizard.log_dir=$PWD/tutorial_alpamayo driver=[ar1,ar1_runtime_configs]
2

Visualize Reasoning (Optional)

To visualize the predicted chain-of-causation reasoning, change the generated video layout:
alpasim_wizard +deploy=local wizard.log_dir=$PWD/tutorial_alpamayo driver=[ar1,ar1_runtime_configs] eval.video.video_layouts=[REASONING_OVERLAY]
The Alpamayo-R1 model is large (10B parameters). Please ensure that your GPU has sufficient capacity to run it.

​
Alpamayo-R1 Configuration

Alpamayo-R1 uses multiple cameras:
  • Front wide camera (120° FOV): 1080x1900 resolution
  • Front tele camera (30° FOV): 1080x1900 resolution
  • Left cross camera (120° FOV): 1080x1900 resolution
  • Right cross camera (120° FOV): 1080x1900 resolution
All cameras run at 10Hz (100ms frame interval) with synchronized inference.

​
Video Layout Options

You can choose which video layouts to render via eval.video.video_layouts.
The default layout shows:
  • BEV (Bird’s Eye View) map visualization
  • Camera view
  • Metrics table
alpasim_wizard +deploy=local wizard.log_dir=$PWD/tutorial driver=[vavam,vavam_runtime_configs] eval.video.video_layouts=[DEFAULT]

​
Transfuser (Provisional)

As an example for how to integrate a different driver model, AlpaSim provides a provisional integration for the Transfuser policy, specifically the Latent TransFuser v6 (LTFv6) model developed for NAVSIM.

​
Model Download

First, download the Transfuser model weights and config from HuggingFace: 
huggingface-cli download longpollehn/tfv6_navsim model_0060.pth --local-dir=data/drivers/transfuser/
huggingface-cli download longpollehn/tfv6_navsim config.json --local-dir=data/drivers/transfuser/

​
Running Transfuser

Run the wizard with the Transfuser configuration: 
alpasim_wizard +deploy=local wizard.log_dir=$PWD/tutorial_transfuser driver=[transfuser,transfuser_runtime_configs]

​
Log Replay Driver

If you would like to force the ego vehicle to follow its recorded trajectory, instead of following the predictions of a policy, you can configure the simulation in log replay mode. 
alpasim_wizard +deploy=local wizard.log_dir=$PWD/tutorial_replay \
  runtime.endpoints.physics.skip=true \
  runtime.endpoints.trafficsim.skip=true \
  runtime.endpoints.controller.skip=true \
  runtime.default_scenario_parameters.physics_update_mode=NONE \
  runtime.default_scenario_parameters.force_gt_duration_us=20000000
In log replay mode, the ego vehicle follows the exact trajectory from the original recording, which is useful for evaluation and debugging purposes.

Build docs developers (and LLMs) love

Get started for freeTalk to us