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SidewalkPilot-v3.2b
ram-shreyas-naik-sabavat/SidewalkPilot-v3.2b
SidewalkPilot-v3.2b is a robotics model from ram-shreyas-naik-sabavat. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
SidewalkPilot-v3.2b maps one 320x180 OpenCV BGR frame to steering control for the SidewalkPilot RC car. Best-validation checkpoint from the v3.2 run.
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Public
Updated Jul 13, 2026
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22.1 MB
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At a glance
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Jul 11, 2026
- Updated
- Jul 13, 2026
- SHA
- bbc11973
Model card
From the Hugging Face model README
SidewalkPilot-v3.2b
SidewalkPilot-v3.2b maps one 320x180 OpenCV BGR frame to steering control for the SidewalkPilot RC car. Best-validation checkpoint from the v3.2 run.
Model Details
- Developer: Ram Shreyas Naik Sabavat
- Series: 3.x
- Architecture: SidewalkPilotV3 CNN, approximately 5.53 million parameters
- Model type: 9-class steering classification plus within-bucket offsets and throttle
- Checkpoint role: best-validation
- Artifact:
SidewalkPilot-v3.2b.onnx(FP32 ONNX) - Checkpoint created: 2026-07-08 11:53 PDT
- Input:
[batch, 3, 180, 320], OpenCV BGR - Preprocessing: resize to
320x180, then(x / 255 - 0.5) / 0.5 - Output:
[batch, 19]: 9 class logits, 9 offsets, and throttle
Output Decoding
class_id = argmax(output[0:9])
fraction = sigmoid(output[9 + class_id])
steering_deg = bucket_low[class_id] + fraction * bucket_width[class_id]
throttle = sigmoid(output[18]) # not used by the current runtime
Logical steering uses 0 for full left, 90 for straight, and 180 for full right. The throttle output is not deployed because the collected throttle labels do not contain enough variation to train useful throttle control.
Evaluation Setup
- Evaluation set: 81,237 current Series 3 real field images
- Purpose: training-set fit check, not held-out generalization proof
- Error unit: logical steering degrees
- Selection priority: balanced 9-bucket accuracy and turn recall first; MAE is supporting evidence
- Cross-series warning: do not compare these numbers directly with Series 1/2, which use their original 2,224-image dataset
Chronological Metrics Through This Version
| Model | Bal9 | Turn exact | Turn +/-1 | Straight exact | MAE | Median AE | Signed error |
|---|---|---|---|---|---|---|---|
3.0 | 16.0% | 15.3% | 42.9% | 23.4% | 18.971 | 13.623 | -3.594 |
3.0b | 15.9% | 14.8% | 42.3% | 24.6% | 18.450 | 13.129 | -3.944 |
3.1 | 28.1% | 26.8% | 53.3% | 55.2% | 22.647 | 9.729 | -2.353 |
3.1b | 27.4% | 25.8% | 52.3% | 56.7% | 20.958 | 9.591 | -3.769 |
3.2 | 34.0% | 31.1% | 57.7% | 52.4% | 16.776 | 9.648 | -1.474 |
3.2b (this model) | 25.1% | 19.8% | 46.0% | 67.2% | 14.457 | 5.909 | -4.691 |
Class-Balanced Ranking Through This Version
| Rank | Model | Bal9 | Turn exact | Turn +/-1 | MAE | Median AE | Signed error |
|---|---|---|---|---|---|---|---|
1 | 3.2 | 34.0% | 31.1% | 57.7% | 16.776 | 9.648 | -1.474 |
2 | 3.1 | 28.1% | 26.8% | 53.3% | 22.647 | 9.729 | -2.353 |
3 | 3.1b | 27.4% | 25.8% | 52.3% | 20.958 | 9.591 | -3.769 |
4 | 3.2b | 25.1% | 19.8% | 46.0% | 14.457 | 5.909 | -4.691 |
5 | 3.0 | 16.0% | 15.3% | 42.9% | 18.971 | 13.623 | -3.594 |
6 | 3.0b | 15.9% | 14.8% | 42.3% | 18.450 | 13.129 | -3.944 |
Current Version Snapshot
- Balanced 9-bucket exact: 25.1%
- Turn exact: 19.8%
- Turn within one bucket: 46.0%
- Straight exact: 67.2%
- MAE: 14.457 degrees
- Median absolute error: 5.909 degrees
- Signed error: -4.691 degrees
Intended Use
- Small RC-car autonomy experiments
- Sidewalk/path steering research
- Jetson ONNX/TensorRT deployment experiments
Limitations and Safety
This model does not identify obstacles, prove a clear path, estimate confidence, or detect out-of-distribution scenes. Shadows, lighting changes, curb geometry, driveways, camera movement, and underrepresented turns can cause unsafe steering. Keep independent LiDAR emergency braking, manual takeover, conservative speed limits, and bounded operating conditions above model output. v3.3 through v3.4b have not yet received a physical-car field verdict.
Reproducibility
artifact_manifest.json records the artifact SHA-256, tensor signature, checkpoint role, evaluation set, and current report metrics.
Links
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Languages
- en
- Created
- Jul 11, 2026
- Updated
- Jul 13, 2026