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research

comma.ai for the people to experiment with

sandbox

sandbox for DALF

sasensorfusionlocalization

Sensor Fusion and Localization related projects of Udacity's Self-driving Car Nanodegree Program:

scikit-llm

Seamlessly integrate powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks.

sdc_prj01_lane_line

Finding Lane Lines on the Road

sdc_prj02_tsc

This repository is the code and writeup of the Traffic Sign Recognition project. We apply Deep Learning and Convolutional Networks (ConvNets) to the task of traffic sign classification as part of the SELF-DRIVING CAR nanodegree program. The project is broken down into three steps, which are: Step 1 Data Set Summary & Exploration, Step 2 Design and Test a Model Architecture, Step 3 Test a Model on New Images.

sdc_prj03_cloning

Project 3 -  Behavioral Cloning, Teaching a car to drive

sdc_prj04_lane_finding

In this project, the goal is to write a software pipeline to identify the lane boundaries in a video. The project is broken down into six steps, which are: camera calibration, distortion correction, color and gradient threshold, perspective transform, detect lane lines, determine the lane curvature.

sdc_prj05_vehicle_detection

In this project, the goal is to write a software pipeline to identify vehicles in a video from a front-facing camera on a car.

sigma_coding_youtube

This is a collection of all the code that can be found on my YouTube channel Sigma Coding.

simbody

High-performance C++ multibody dynamics/physics library for simulating articulated biomechanical and mechanical systems like vehicles, robots, and the human skeleton.

simvis

slime-simulation

spine

UP board / rasperry pi to quad CAN Bus

stanforddoggoproject

tmptsc

traffic_sign_classifier

This repository is the code and writeup of the Traffic Sign Recognition project. We apply Deep Learning and Convolutional Networks (ConvNets) to the task of traffic sign classification as part of the SELF-DRIVING CAR nanodegree program. The project is broken down into three steps, which are: Step 1 Data Set Summary & Exploration, Step 2 Design and Test a Model Architecture, Step 3 Test a Model on New Images. Beyond the initial requirements, I also implemented the Tensorboard features: embedding visualizer, summary{images, loss, accuracy, weights, biais}, a comparaison of different architectures, and compared the model result with Google Images search. Here, you will find the PROJECT SPECIFICATION and the PROJECT CODE.

vcstool

Vcstool is a command line tool designed to make working with multiple repositories easier

vesc

xo

Lightweight c++17 utility library