Dataset card: controls in measurements.json are the autopilot's; link the recording code

README.md

@@ -93,7 +93,7 @@ Evaluation with retrieval opens the camera frames of the retrieved demonstration
 
 <img src="assets/hardware_rig.jpg" width="45%" align="right" alt="Driver-in-the-loop rig">
 
-15 participants drove CARLA Leaderboard routes at a driver-in-the-loop rig with a real wheel and pedals. They drove the same routes three times, each time under a different instruction:
+15 participants drove CARLA Leaderboard routes at a driver-in-the-loop rig with a real wheel and pedals. The recording code is released with PersonaDrive ([`human_data_collection/`](https://github.com/PervasiveAutonomyLab/PersonaDrive/tree/main/human_data_collection)). They drove the same routes three times, each time under a different instruction:
 
 | Folder | Style | What the driver was told |
 |---|---|---|
@@ -122,7 +122,8 @@ Evaluation with retrieval opens the camera frames of the retrieved demonstration
 
 **What is recorded.** Each route stores, at 4 Hz:
 - the front camera image, plus a shifted-camera copy used for augmentation;
-- the ego state: speed from CARLA, steering from the wheel axis, throttle and brake from the pedals;
+- the ego state from CARLA (speed, pose), which follows the participant's driving;
+- the controls `throttle`, `steer` and `brake` in `measurements.json`. These are the controls of SimLingo's privileged autopilot, which runs alongside the participant on every frame. The participant's own wheel and pedal inputs are recorded by the rig but are not part of this release;
 - the navigation command and the next GPS target points from CARLA's route planner;
 - the objects around the ego vehicle: vehicles, pedestrians, traffic signs, traffic-light states.