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Artificial Intelligence · Capstone
Self-Driving Car Simulator with Behavioural Cloning
A CNN learns to steer by watching you drive in a simulator
4-5 weeks build
Overview
You drive laps in the open-source Udacity driving simulator to record camera frames and steering angles, then train an NVIDIA-style CNN to predict steering from a single image. A socket server feeds the trained model back into the simulator for autonomous laps, with a lane-detection overlay drawn on the live feed to show what the road looks like to the car.
What makes it stand up
- 01Data collection guide for balanced, recoverable driving laps
- 02Augmentation with flips, shifts, brightness and side-camera offsets
- 03End-to-end CNN regressing steering angle from one frame
- 04Live autonomous mode driving the simulator through a socket bridge
- 05Lane overlay and steering trace plotted during each run
Module breakdown
- 01
Recording
Simulator capture, CSV logs and steering histogram balancing.
- 02
Augmentation
Image transforms that teach recovery from road edges.
- 03
Model
Convolutional regressor with training curves and checkpoints.
- 04
Drive server
Flask-SocketIO bridge sending steering and throttle live.
- 05
Overlay
Lane detection drawn over the simulator feed.
After this, you will be able to
- Explain behavioural cloning and its distribution-shift problem
- Defend your augmentation choices with before-and-after runs
- Demo the car completing a lap on its own
Technology stack
- Python
- TensorFlow
- OpenCV
- Flask
- NumPy
- Level
- Capstone
- Domain
- Artificial Intelligence
- Build time
- 4-5 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support