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README.md
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---
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# commaSteeringControl
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---
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# commaSteeringControl
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`commaSteeringControl` is a dataset of car steering information from ~12500 hours of driving with openpilot engaged. We control steering on most
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cars in openpilot using `steeringTorque`. This results in some lateral acceleration depending on both the car's internal vehicle dynamics and external factors (car speed, road roll,
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etc). Learning this relationship is essential to having accurate steering control in openpilot. `commaSteeringControl` is the largest controls dataset of its kind, spanning
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hundreds of car models across 10+ brands.
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The main purpose of this dataset is to give the community access to the data needed to model the steering of their car, and with that make a more accurate steering controller in openpilot to improve openpilot's performance on that car. We hope this dataset aids
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and accelerates that effort.
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This is the largest dataset of vehicle dynamics ever released. It can also be used to develop or verify practical vehicle dynamics models for tire slip, road roll, lateral acceleration, understeer/oversteer, etc. We may add more fields for this goal in the future.
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## Timeline
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- In [0.8.15](https://blog.comma.ai/0815release/#torque-controller),
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we introduced a [new controller](https://github.com/commaai/openpilot/blob/master/selfdrive/controls/lib/latcontrol_torque.py) that leveraged the relationship between
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steering torque and lateral acceleration.
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- In [0.9.0](https://blog.comma.ai/090release/#torqued-an-auto-tuner-for-lateral-control), we introduced
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[torqued](https://github.com/commaai/openpilot/blob/master/selfdrive/locationd/torqued.py), which learns the relationship online.
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- In [0.9.2](https://blog.comma.ai/092release/#chevrolet-bolt-euv), we introduced a non-linear feed-forward function.
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- There has been [extensive community effort](https://github.com/twilsonco/openpilot/tree/log-info) to improve the controller (speed-based relationships, using neural networks, etc)
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- We are working on similar techniques to further improve steering controls
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## Dataset
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- Download the dataset from [HuggingFace](https://huggingface.co/datasets/commaai/commaSteeringControl/tree/main/data)
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- Checkout the example notebook at `visualize.ipynb`
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```
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# Data Structure
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data/
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├── Platform 1
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| ├── Segment 1
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| ├── ...
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| └── Segment N
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└── Platform M
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├── Segment 1
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└── ...
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| | Fields | Description | Value Range |
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|---:|:----------------------|:---------------------------------------------------------------------------------|:----------------|
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| 0 | t | Time | [0, 60] |
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| 1 | latActive | Is openpilot engaged? | {True, False} |
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| 2 | steeringPressed | Is steering wheel pressed by the user? | {True, False} |
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| 3 | vEgo | Forward velocity of the car (m/s) | [0, ∞] |
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| 4 | aEgo | Forward acceleration of the car (m/s^2) | [-∞, ∞] |
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| 5 | steeringAngleDeg | Steering Angle (Deg) | [-∞, ∞] |
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| 6 | steer | Normalized steer torque | [-1, 1] |
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| 7 | steerFiltered | Normalized, rate limited steer torque | [-1, 1] |
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| 8 | roll | Road roll (rad) | [-0.174, 0.174] |
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| 9 | latAccelSteeringAngle | Lateral acceleration requested from the planner | [-∞, ∞] |
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| 10 | latAccelDesired | Lateral acceleration computed from the steering wheel angle and vehicle dynamics | [-∞, ∞] |
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| 11 | latAccelLocalizer | Lateral acceleration from the localizer | [-∞, ∞] |
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| 12 | epsFwVersion | EPS firmware version | str |
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```
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## Notes
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- All values from different messages are interpolated and synced to time `t`
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- In `torqued`, we assume that the gravity adjusted lateral acceleration has a linear dependence wrt. the steer command. We fit a Total-Least-Squares solution to obtain the factor. We also assume an error-dependant friction value (causes the hysteresis).
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- Steering torque is normalized in openpilot (to get `steer`), and further rate limits are applied (to get `steerFiltered`). `steerFiltered` is the best input signal.
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- The `latAccelSteeringAngle` is computed using the vehicle model from openpilot (it uses `roll`). This is the best signal to predict as `latAccelLocalizer` can be quite noisy.
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- In reality (especially for some cars), the relationship is non-linear depending on vehicle speed, and is temporally correlated (there is a lag between the signals).
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- There may be a lag in openpilot fully regaining steering control after `steeringPressed` which may have to be accounted for.
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- In some platforms, cars with different `epsFwVersion` have dramatically different steering behaviour, although this is not common.
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- Any algorithm that could be upstreamed to openpilot needs to be simple, fast, and reliable - similar to `torqued`, simple non-linear functions, or simple MLPs etc.
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