SidewalkPilot-v4.0g
SidewalkPilot-v4.0g is a Series 4.0 CF steering model for the SidewalkPilot RC car. Its training contract uses image-only input with current and three future targets used as training supervision. It is the best-validation (epoch 7) checkpoint paired with v4.0f.
Model Details
- Developer: Ram Shreyas Naik Sabavat
- Series: 4.0 experimental temporal steering
- Architecture: SidewalkPilotV4 CNN, 5,537,560 parameters
- Contract: CF
- Checkpoint role: best-validation (epoch 7)
- Field status: tested; worse than v4.0f
- Artifact:
SidewalkPilot-v4.0g.onnx(FP32 ONNX) - Checkpoint created: 2026-07-14 PDT
- Input:
image[batch,3,180,320] - Image preprocessing: OpenCV BGR, resize to
320x180, then(x / 255 - 0.5) / 0.5 - Output:
[batch,4,18]; each horizon contains 9 class logits and 9 class-local offsets - Live steering horizon: horizon 0
- Throttle output: none
Output Decoding
class_id = argmax(softmax(horizon[0:9]))
fraction = sigmoid(horizon[9 + class_id])
steering_deg = bucket_low[class_id] + fraction * bucket_width[class_id]
Logical steering uses 0 for full left, 90 for straight, and 180 for full right. Future targets are training labels only. They are never runtime inputs.
Common Evaluation
- Evaluation set: 6,952 frozen, sequence-valid anchors from the 81,237-image real Series 3/4 dataset
- Purpose: common open-loop comparison across model families
- Error unit: logical steering degrees
- Selection priority: Bal9 and turn recall first; MAE is supporting evidence
- Important limit: PC/PCF evaluation uses ground-truth prior targets, while live driving feeds prior predictions
| Model | Bal9 | Turn exact | Turn +/-1 | ST exact | MAE | Median AE | Signed error |
|---|---|---|---|---|---|---|---|
4.0p | 34.5% | 32.1% | 65.9% | 67.7% | 12.396 | 2.967 | +0.120 |
4.0r | 32.9% | 27.4% | 62.6% | 77.6% | 11.636 | 1.846 | -1.136 |
4.0f | 25.4% | 23.5% | 56.4% | 62.8% | 15.623 | 6.723 | +1.057 |
4.0g (this model) | 20.4% | 17.1% | 46.4% | 76.0% | 14.116 | 2.114 | -1.864 |
4.0a | 33.5% | 30.9% | 65.3% | 68.1% | 12.379 | 3.115 | +0.290 |
4.0c | 32.0% | 29.4% | 62.9% | 75.5% | 11.321 | 1.825 | -0.981 |
Field Evaluation
v4.0g was tested on the car and performed worse than the final-epoch v4.0f checkpoint. It was not selected.
The comparison was supervised and video was captured, but it was not a formal route-controlled benchmark with a preserved per-case score sheet. The claim is limited to the cases presented.
Intended Use
- Small RC-car steering experiments
- Temporal behavioral-cloning research
- Jetson Orin Nano ONNX deployment experiments
- Reproducing the documented difference between open-loop metrics and closed-loop behavior
Limitations and Safety
This model does not identify obstacles, prove a clear path, estimate confidence, or detect out-of-distribution scenes. The Series 4.0 history models can amplify their own earlier predictions during closed-loop driving. Do not treat an offline score or successful ONNX load as approval for autonomous use. Keep independent LiDAR emergency braking, manual takeover, conservative speed, and bounded supervised testing above model output.
Reproducibility
artifact_manifest.json records the model SHA-256, tensor signature, checkpoint role, field status, and common-set metrics.