Vladimir-Lazic/SFND_Radar_Target_Generation_And_Detection

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Created over 3 years ago
Updated over 3 years ago

Vladimir-Lazic/SFND_Radar_Target_Generation_And_Detection

Vladimir-Lazic

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Implementation of Radar Target Generation And Detection project for the Udacity Sensor Fusion Nanodegree program

SFND_3D_Object_Tracking

Sensor fusion module for 3D Object Tracking. The project uses a combination of Lidar and Camera data for making an estimation of the distance for the preceding vehicle.

CarND-Path-Planning-Project

Implementation of highway driving using localization and sensor fusion. The code implements behavioral planning in a form of a state machine for lane changes and velocity adjustment

SFND_2D_Feature_Tracking

2D Feature Tracking project using OpenCV detectors and descriptors for keypoint tracking in multiple frames. The project uses a variety of detectors and descriptors and performs analysis of the best possible combination with regards to processing time and detection precision.

CarND-PID-Control-Project

Implementation of a PID controller for vehicle steering and throttle for the Udacity Self-Driving Car Nanodegree

SFND_Unscented_Kalman_Filter

Unscented Kalman filter implemented for vehicle position and velocity estimation using constant turn rate and velocity process model

CarND-Extended-Kalman-Filter-Project

Implementation of Extended Kalman Filter for Udacity Self Driving Car Nanodegree program

CarND-Traffic-Sign-Classifier-Project

Traffic sign classifier implemented with LeNet neural network architecture using TensorFlow for the Udacity Self-Driving Car Nanodegree program

CarND-LaneLines-P1

FIrst project for Udacity Self Driving Car Nanodegree

Jupyter Notebook

CarND-Behavioral-Cloning-P3

Behavioural Cloning project for the Udacity Self Driving Car Nanodegree that uses deep learning for vehicle control in simulator

CarND-Kidnapped-Vehicle-Project

Implementation of particle filter for sparse localization based on map landmarks for the Udacity Self Driving Car Nanodegree. Project Kidnapped Vehicle

SFND_Lidar_Obstacle_Detection

Lidar point cloud processing project. This project performs processing of Lidar point cloud information to determine obstacles. This is done by performing segmentation to determine which points belong to the same 3d plane and clustering to determine which points represent an obstacle on the road