Self Driving Car

Used Udacity's car simulator to drive a car autonomously and trained the model using Nvidia's self-driving car model. Used various data augmentation techniques to increase the size and variety of data for training the model. The model scoring metrics were pretty good with loss and val-loss being 0.0450 and 0.0334.

Project Demonstration Link:

link

Download this Udacity's Self-Driving Car Simulator:

Repo link

Generating Data:

Open the Udacity simulator and select Training Mode. Then click the record button and start driving. A folder will be created with two files IMG and driving_logs.csv

STEPS to run the project:

STEP 01:

Clone the repository

git clone https://github.com/Hirak010/Self-Driving-Car-Simulation

STEP 02:

Create an environment & activate

conda create -n env python=3.8 -y

STEP 03:

Install the requirements

pip install -r requirements.txt

STEP 06:

Now to start the drive run the following command

python app.py

Now, open the udacity simulator and select autonomous mode.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── model             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience

Authors:

Author: Hirakjyoti Medhi
Email: hirak170802@gmail.com