SidewalkPilot-v1.0

SidewalkPilot-v1.0 is a PyTorch steering model for a small autonomous RC car. It takes a full camera frame as input and predicts a steering servo angle from 0 to 180 degrees.

The model is used for camera-based sidewalk/path following. In the full RC car stack, LiDAR runs above the model as a safety layer for obstacle avoidance, LiDAR override steering, and hard braking.

This is the final checkpoint for this training version.

Model Details

• Developed by: Ram Shreyas Naik Sabavat
• Model type: CNN steering regression model
• Library: PyTorch
• License: Apache 2.0
• Checkpoint: SidewalkPilot-v1.0.pth
• Checkpoint created: 2026-04-24 10:07 PM America/Los_Angeles
• Input: Full camera frame, resized/normalized before inference
• Evaluation input format: 200x66, OpenCV BGR
• Output: Steering servo angle from 0 to 180

Specific Improvements

• Created the first SidewalkPilot CNN checkpoint using a 0..180 servo-degree output target.
• Established the raw OpenCV BGR preprocessing path: full frame resized to 200x66 and normalized before inference.
• Set the first measured baseline: 20.990 MAE, 423 / 1287 within 5 degrees, and signed error -7.262.

Specific Issues Observed / Remaining

• High full-set error: 20.990 MAE made it a baseline, not a field-ready checkpoint.
• Strong left bias: signed error -7.262 degrees meant predictions leaned left on average.
• Failure groups remained large on curved curbs, driveway cuts, shadows, and hard-right turns.

Output Meaning

Output	Meaning
0	full left
90	straight
180	full right

Evaluation Setup

• Eval set: 1,287 images
• Failed samples: 0
• Corrections included: 564
• Input format: 200x66, OpenCV BGR
• Output scale: servo angle 0..180
• Error unit: servo degrees
• Score formula: max(0, 100 * (1 - absolute_error / 180))

Version Update Categories

Version	Main update category	Data/status	Result
1.0	Baseline steering	initial mixed sidewalk/CARLA set	complete

Evaluation Summary

Model	Checkpoint	Full Score	MAE	Median AE	Max AE	Signed Error	Within 2°	Within 5°	Within 10°	Within 20°
1.0	SidewalkPilot-v1.0.pth	88.339%	20.990	9.934	151.830	-7.262	202 / 1287	423 / 1287	648 / 1287	869 / 1287

Negative signed error means the model is left-biased on average.

Prediction Distribution

Model	Pred Min	Pred Max	Pred Mean	Pred Median	Pred P05	Pred P25	Pred P75	Pred P95
1.0	8.459	174.146	89.579	89.676	37.520	68.904	108.229	145.649

Ranking

Rank In This Card	Model	Checkpoint	Score	MAE	Median AE	Max AE	Within 5°	Within 10°	Signed Error
1	1.0	SidewalkPilot-v1.0.pth	88.339%	20.990	9.934	151.830	423 / 1287	648 / 1287	-7.262

Field Case Comparison

Model	D26 curves/shadows MAE	D27 curved curb MAE	D28 driveway MAE	D29 driveway/shadow MAE	20260502_12 shadow MAE	20260502_19 hard/curb/smooth MAE
1.0	44.12	53.69	56.18	37.44	29.57	29.57

Current Version Snapshot

• Model: 1.0
• Checkpoint: SidewalkPilot-v1.0.pth
• Checkpoint created: 2026-04-24 10:07 PM America/Los_Angeles
• Full score: 88.339%
• MAE: 20.990 servo degrees
• Median AE: 9.934 servo degrees
• Rank in this card: 1 of 1 checkpoints

Intended Use

This model is intended for:

• RC car autonomy experiments
• Sidewalk/path steering research
• Raspberry Pi robotics projects
• Small-scale computer vision control systems
• Testing steering regression from camera images

Out-of-Scope Use

This model is not intended for:

• Real cars
• Public road vehicles
• Human transportation
• Safety-critical systems
• Fully autonomous deployment without external safety layers

System Context

The full RC car autonomy stack uses layered control:

camera frame
-> resize/normalize image
-> PyTorch steering model
-> predicted servo angle (0-180)
-> runtime decision logic
-> LiDAR safety override when triggered
-> final steering/throttle/brake command
-> servo + motor controller

LiDAR runs as a higher-priority safety layer:

LiDAR clear
-> use model steering

LiDAR obstacle
-> LiDAR override mode

LiDAR blocked/too close
-> hard brake

The model handles normal path following, while LiDAR handles obstacle avoidance and emergency behavior.

Training Data

The model was trained on camera images collected from the RC car driving in sidewalk-like environments. Labels represent steering servo angles from 0 to 180 degrees.

Detailed dataset composition belongs in the dataset README, not this model card.

Preprocessing

During inference/evaluation, the pipeline:

1. Captures the full camera frame.
2. Resizes the image to 200x66.
3. Uses OpenCV BGR image ordering.
4. Normalizes pixel values with (x / 255 - 0.5) / 0.5.
5. Runs the PyTorch model.
6. Clamps the output to 0..180.

The model sees the whole frame, not a cropped region.

Limitations

SidewalkPilot models can fail when lighting, sidewalk shape, camera angle, shadows, driveway cuts, curved curbs, hard turns, or curb-hugging cases differ from the training data.

The model does not understand obstacles by itself and is not a standalone safety system.

Use with external safety logic, manual override, and obstacle detection.

Safety Recommendation

Do not use this model alone to control a robot. In the original project, LiDAR has priority over the model and can override steering or trigger hard braking.

Model Card Contact

Ram Shreyas Naik Sabavat