self-driving-car

This is a multi-year project undertaken by the Cornell Data Science team intended to demonstrate autopilot for a full-size car.

CDS Self-Driving Car aims to demonstrate tight integration of a camera based vision algorithm to navigate a car safely. We utilize a SLAM system to localize ourselves, and an additional lane recognition pipeline to see the road. This is all built upon a Python control loop and will leverage robotics to directly actuate the steering wheel to augment a normal car.

Team

This ambitious project was undertaken by Evan Williams, Iris Li, Edward Gu, Tobi Alade, Elias Little, Eric Zhang, Adarsh Sriram, and Shihao Cao

Demo Results:

FINAL PRESENTATION: https://docs.google.com/presentation/d/1MIJ480jFvm6mGaNohGM7KqMZyA3pAJ67wQSmTtvUBTY/edit?usp=sharing

Moving Pieces:

Here's some extra details about each of the individual parts that we developed and how they work.

Lane Detection

https://github.com/maunesh/advanced-lane-detection-for-self-driving-cars

SLAM

Main Control Loop Framework

The main control loop framework was based on a Read-Estimate-Control-Command-Actuate architecture inspired by work from the Pathfinder for Autonomous Navigation team.

Embedded Systems

Blah blah

---

Hardware

Running it yourself

Please scroll to the bottom for continuation details.

Running it Yourself

Installation

See INSTALL.md.

Run the MCL

Always remember to have the virtual environment venv activated with:

. venv/bin/activate

Actually running the MCL

Our code is structured as a module: src we invoke it with for example:

python -m src hootl.yaml NoSIM

This means to use the hootl.yaml configuration file in src/configs, along with the NoSIM testcase under runner/. You can go to those directories respectively to find more config files and more test cases.

Hardware Setup

HITL/Flight Configutaion

TODO insert everything required to do an in car demo.