SidewalkPilot-v1.1

SidewalkPilot-v1.1 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-v1.1.pth
• Checkpoint created: 2026-04-25 08:01 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

• Added manual street-test corrections and raised full-set score from 88.339% to 92.372%.
• Reduced full-set MAE from 20.990 to 13.730 degrees.
• Improved close predictions: within-5-degree count rose from 423 to 746 images.

Specific Issues Observed / Remaining

• Driveway subset regressed badly: D28 driveway MAE rose to 83.08.
• Hard-right / curb-smoothness cases were still weak at 26.13 MAE.
• The model improved normal street-following but overreacted on driveway-cut geometry.

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
1.0b	Best-checkpoint variant of baseline	same v1.0 training run	complete
1.1	Street-test correction pass	manual street corrections	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
1.0b	SidewalkPilot-v1.0b.pth	88.292%	21.075	10.234	152.629	-6.691	179 / 1287	410 / 1287	640 / 1287	866 / 1287
1.1	SidewalkPilot-v1.1.pth	92.372%	13.730	3.886	144.676	-6.197	434 / 1287	746 / 1287	916 / 1287	1044 / 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
1.0b	9.009	173.933	90.149	89.734	38.399	70.772	108.074	144.783
1.1	4.000	176.000	90.643	89.761	37.944	72.754	106.427	152.011

Ranking

Rank In This Card	Model	Checkpoint	Score	MAE	Median AE	Max AE	Within 5°	Within 10°	Signed Error
1	1.1	SidewalkPilot-v1.1.pth	92.372%	13.730	3.886	144.676	746 / 1287	916 / 1287	-6.197
2	1.0	SidewalkPilot-v1.0.pth	88.339%	20.990	9.934	151.830	423 / 1287	648 / 1287	-7.262
3	1.0b	SidewalkPilot-v1.0b.pth	88.292%	21.075	10.234	152.629	410 / 1287	640 / 1287	-6.691

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
1.0b	42.85	54.06	57.52	37.39	29.16	29.76
1.1	38.93	36.60	83.08	33.44	21.42	26.13

Current Version Snapshot

• Model: 1.1
• Checkpoint: SidewalkPilot-v1.1.pth
• Checkpoint created: 2026-04-25 08:01 PM America/Los_Angeles
• Full score: 92.372%
• MAE: 13.730 servo degrees
• Median AE: 3.886 servo degrees
• Rank in this card: 1 of 3 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