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ram-shreyas-naik-sabavat/
SidewalkPilot-v1.7

Name: SidewalkPilot-v1.7
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SidewalkPilot-v1.7

SidewalkPilot-v1.7 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-v1.7.pth
Checkpoint created: 2026-04-29 09:11 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

Targeted driveway and shadow field failures from photo_20260429.
D29 driveway/shadow MAE dropped to 0.93.
Improved full-set score to 94.956%.

Specific Issues Observed / Remaining

The later photo_20260502_12 shadow set was still weak at 17.51 MAE.
Hard-right/curb-smoothness remained around 22.41 MAE.
The checkpoint fixed driveway/shadow cases but did not solve the later low-sun shadow or right-hard-turn sets.

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
`1.1b`	Best checkpoint from street-correction pass	`same v1.1 training run`	complete
`1.2`	Best early field behavior	`no-weather tuning + manual corrections`	complete
`1.2b`	Best checkpoint from early field-behavior run	`same v1.2 training run`	complete
`1.3`	Smoothness / aggression tuning	`same dataset family`	complete
`1.3b`	Best checkpoint from smoothness/aggression run	`same v1.3 training run`	complete
`1.4`	Curves + sloped shadow fixes	`photo_20260426`	complete
`1.4b`	Best checkpoint from curves/shadows run	`photo_20260426`	complete
`1.5`	Curved curb interpreted as sidewalk	`photo_20260427`	complete
`1.5b`	Best checkpoint from curved-curb run	`photo_20260427`	complete
`1.6`	Driveway failure fix	`photo_20260428`	complete
`1.6b`	Best checkpoint from driveway run	`photo_20260428`	complete
`1.7`	Driveway + shadow field fixes	`photo_20260429`	field validated

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`
`1.1b`	`SidewalkPilot-v1.1b.pth`	`92.372%`	`13.730`	`3.886`	`144.676`	`-6.197`	`434 / 1287`	`746 / 1287`	`916 / 1287`	`1044 / 1287`
`1.2`	`SidewalkPilot-v1.2.pth`	`93.634%`	`11.459`	`1.018`	`140.617`	`-5.314`	`748 / 1287`	`843 / 1287`	`945 / 1287`	`1058 / 1287`
`1.2b`	`SidewalkPilot-v1.2b.pth`	`93.585%`	`11.548`	`1.281`	`140.920`	`-5.296`	`712 / 1287`	`849 / 1287`	`948 / 1287`	`1068 / 1287`
`1.3`	`SidewalkPilot-v1.3.pth`	`93.502%`	`11.697`	`1.765`	`148.944`	`-5.557`	`670 / 1287`	`845 / 1287`	`955 / 1287`	`1069 / 1287`
`1.3b`	`SidewalkPilot-v1.3b.pth`	`93.223%`	`12.199`	`3.005`	`140.762`	`-5.423`	`483 / 1287`	`823 / 1287`	`961 / 1287`	`1070 / 1287`
`1.4`	`SidewalkPilot-v1.4.pth`	`94.079%`	`10.657`	`2.194`	`132.078`	`-4.997`	`613 / 1287`	`899 / 1287`	`1008 / 1287`	`1102 / 1287`
`1.4b`	`SidewalkPilot-v1.4b.pth`	`93.840%`	`11.087`	`2.960`	`131.227`	`-5.101`	`513 / 1287`	`845 / 1287`	`1012 / 1287`	`1099 / 1287`
`1.5`	`SidewalkPilot-v1.5.pth`	`94.337%`	`10.193`	`3.711`	`112.289`	`-3.734`	`443 / 1287`	`766 / 1287`	`1011 / 1287`	`1123 / 1287`
`1.5b`	`SidewalkPilot-v1.5b.pth`	`94.337%`	`10.193`	`3.711`	`112.289`	`-3.734`	`443 / 1287`	`766 / 1287`	`1011 / 1287`	`1123 / 1287`
`1.6`	`SidewalkPilot-v1.6.pth`	`94.540%`	`9.828`	`3.953`	`131.186`	`-2.877`	`442 / 1287`	`776 / 1287`	`1013 / 1287`	`1124 / 1287`
`1.6b`	`SidewalkPilot-v1.6b.pth`	`94.522%`	`9.860`	`3.848`	`130.713`	`-3.025`	`436 / 1287`	`774 / 1287`	`1006 / 1287`	`1126 / 1287`
`1.7`	`SidewalkPilot-v1.7.pth`	`94.956%`	`9.079`	`3.523`	`108.732`	`-2.665`	`465 / 1287`	`801 / 1287`	`1007 / 1287`	`1151 / 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`
`1.1b`	`4.000`	`176.000`	`90.643`	`89.761`	`37.944`	`72.754`	`106.427`	`152.011`
`1.2`	`4.000`	`176.000`	`91.526`	`91.271`	`41.237`	`72.686`	`108.045`	`149.390`
`1.2b`	`4.000`	`176.000`	`91.544`	`91.210`	`40.153`	`72.763`	`107.837`	`149.399`
`1.3`	`4.000`	`176.000`	`91.283`	`90.486`	`40.523`	`73.660`	`106.509`	`149.883`
`1.3b`	`4.000`	`176.000`	`91.417`	`90.950`	`41.317`	`74.564`	`105.508`	`149.390`
`1.4`	`4.000`	`176.000`	`91.843`	`90.920`	`40.737`	`74.986`	`106.295`	`154.155`
`1.4b`	`4.000`	`176.000`	`91.740`	`90.955`	`39.214`	`74.615`	`105.238`	`154.870`
`1.5`	`4.000`	`176.000`	`93.107`	`90.536`	`41.311`	`74.592`	`109.059`	`162.565`
`1.5b`	`4.000`	`176.000`	`93.107`	`90.536`	`41.311`	`74.592`	`109.059`	`162.565`
`1.6`	`4.000`	`176.000`	`93.963`	`92.289`	`40.245`	`73.353`	`109.929`	`167.733`
`1.6b`	`4.000`	`176.000`	`93.816`	`91.941`	`40.197`	`73.651`	`109.786`	`167.452`
`1.7`	`4.000`	`176.000`	`94.175`	`91.512`	`39.126`	`74.491`	`110.930`	`165.057`

Ranking

Rank In This Card	Model	Checkpoint	Score	MAE	Median AE	Max AE	Within 5°	Within 10°	Signed Error
`1`	`1.7`	`SidewalkPilot-v1.7.pth`	`94.956%`	`9.079`	`3.523`	`108.732`	`801 / 1287`	`1007 / 1287`	`-2.665`
`2`	`1.6`	`SidewalkPilot-v1.6.pth`	`94.540%`	`9.828`	`3.953`	`131.186`	`776 / 1287`	`1013 / 1287`	`-2.877`
`3`	`1.6b`	`SidewalkPilot-v1.6b.pth`	`94.522%`	`9.860`	`3.848`	`130.713`	`774 / 1287`	`1006 / 1287`	`-3.025`
`4`	`1.5`	`SidewalkPilot-v1.5.pth`	`94.337%`	`10.193`	`3.711`	`112.289`	`766 / 1287`	`1011 / 1287`	`-3.734`
`5`	`1.5b`	`SidewalkPilot-v1.5b.pth`	`94.337%`	`10.193`	`3.711`	`112.289`	`766 / 1287`	`1011 / 1287`	`-3.734`
`6`	`1.4`	`SidewalkPilot-v1.4.pth`	`94.079%`	`10.657`	`2.194`	`132.078`	`899 / 1287`	`1008 / 1287`	`-4.997`
`7`	`1.4b`	`SidewalkPilot-v1.4b.pth`	`93.840%`	`11.087`	`2.960`	`131.227`	`845 / 1287`	`1012 / 1287`	`-5.101`
`8`	`1.2`	`SidewalkPilot-v1.2.pth`	`93.634%`	`11.459`	`1.018`	`140.617`	`843 / 1287`	`945 / 1287`	`-5.314`
`9`	`1.2b`	`SidewalkPilot-v1.2b.pth`	`93.585%`	`11.548`	`1.281`	`140.920`	`849 / 1287`	`948 / 1287`	`-5.296`
`10`	`1.3`	`SidewalkPilot-v1.3.pth`	`93.502%`	`11.697`	`1.765`	`148.944`	`845 / 1287`	`955 / 1287`	`-5.557`
`11`	`1.3b`	`SidewalkPilot-v1.3b.pth`	`93.223%`	`12.199`	`3.005`	`140.762`	`823 / 1287`	`961 / 1287`	`-5.423`
`12`	`1.1`	`SidewalkPilot-v1.1.pth`	`92.372%`	`13.730`	`3.886`	`144.676`	`746 / 1287`	`916 / 1287`	`-6.197`
`13`	`1.1b`	`SidewalkPilot-v1.1b.pth`	`92.372%`	`13.730`	`3.886`	`144.676`	`746 / 1287`	`916 / 1287`	`-6.197`
`14`	`1.0`	`SidewalkPilot-v1.0.pth`	`88.339%`	`20.990`	`9.934`	`151.830`	`423 / 1287`	`648 / 1287`	`-7.262`
`15`	`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`
`1.1b`	`38.93`	`36.60`	`83.08`	`33.44`	`21.42`	`26.13`
`1.2`	`36.54`	`31.76`	`64.48`	`25.38`	`20.86`	`22.74`
`1.2b`	`35.95`	`32.11`	`61.94`	`25.46`	`21.01`	`22.78`
`1.3`	`35.00`	`34.06`	`74.95`	`25.26`	`19.51`	`22.92`
`1.3b`	`34.10`	`35.63`	`71.75`	`24.12`	`19.66`	`22.96`
`1.4`	`3.66`	`31.68`	`70.03`	`25.83`	`18.73`	`24.32`
`1.4b`	`3.82`	`31.91`	`71.01`	`26.65`	`19.21`	`24.41`
`1.5`	`3.98`	`0.99`	`87.18`	`25.74`	`18.59`	`21.06`
`1.5b`	`3.98`	`0.99`	`87.18`	`25.74`	`18.59`	`21.06`
`1.6`	`3.93`	`1.10`	`2.36`	`26.32`	`18.91`	`22.37`
`1.6b`	`4.00`	`1.10`	`2.03`	`26.22`	`19.02`	`22.32`
`1.7`	`4.22`	`1.23`	`1.82`	`0.93`	`17.51`	`22.41`

Current Version Snapshot

Model: 1.7
Checkpoint: SidewalkPilot-v1.7.pth
Checkpoint created: 2026-04-29 09:11 PM America/Los_Angeles
Full score: 94.956%
MAE: 9.079 servo degrees
Median AE: 3.523 servo degrees
Rank in this card: 1 of 15 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:

text

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:

text

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:

Captures the full camera frame.
Resizes the image to 200x66.
Uses OpenCV BGR image ordering.
Normalizes pixel values with (x / 255 - 0.5) / 0.5.
Runs the PyTorch model.
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

ram-shreyas-naik-sabavat/SidewalkPilot-v1.7