• List of posts

#Term 1

• SSD in TensorFlow: Traffic Sign Detection and Classification

• Udacity Self-Driving Car Nanodegree Project 3 — Behavioral Cloning : Jeremy Shannon, 핸들 각도 분포에 따른 성능 평가 [GitHub: 기술적 설명]

• Teaching a car to drive itself Arnaldo Gunzi, 전처리의 모든 부분 커버

• Behavioral Cloning — Transfer Learning with Feature Extraction: Alena Kastsiukavets, Transfer Learning 기법 적용

• Behavioral Cloning For Self Driving Cars : Mojtaba Valipour

• An augmentation based deep neural network approach to learn human driving behavior : Vivek Yadav

• MainSqueeze: The 52 parameter model that drives in the Udacity simulator : Mez Gebre, 추천

• End-to-end learning for self-driving cars : Alex Staravoitau, 강추

• Training a deep learning model to steer a car in 99 lines of code: Matt Harvey, Keras코드 제공

• self-driving truck in Euro Truck Simulator 2

• Behavioral Cloning : Behavioral Cloning
• 6 Awesome Projects from Udacity Students : P1~P5 전체
• 6 Different End-to-End Neural Networks : Behavioral Cloning
• Udacity Self-Driving Car Students on Neural Networks and Docker
• More Udacity Self-Driving Car Students, In Their Own Words
• Udacity Self-Driving Car Students in Their Own Words
• Computer Vision
• Lane Lines, Curvature, and Cutting-Edge Network Architectures
• Cutting-Edge Autonomous Vehicle Tools
• Computer Vision, Tiny Neural Networks, and Careers
• Udacity Students Who Love Neural Networks

• Introduction to Udacity Self-Driving Car Simulator : 시뮬레이터 설명, Naoki Shibuya
• CNN Model Comparison in Udacity’s Driving Simulator : 2개의 CNN모델 비교, Chris Gundling
• Finding the right parameters for your Computer Vision algorithm : cv알고리즘의 알맞은 파라미터 선정, maunesh
• What kind of background do you need to get into Machine Learning? : Chase Schwalbach
• Self-driving car in a simulator with a tiny neural network : Mengxi Wu
• MiniFlow from Python to JavaScript
• Preparation, Generalization, and Hacking Cars
• Hardware, tools, and cardboard mockups : Dylan Brown
• Comparing model performance: Including Max Pooling and Dropout Layers : Jessica Yung

Starter Kit

• Sensors The first lesson of the Sensor Fusion Module covers the physics of two of the most import sensors on an autonomous vehicle — radar and lidar.

• Kalman Filters Kalman filters are the key mathematical tool for fusing together data. Implement these filters in Python to combine measurements from a single sensor over time.

• C++ Primer Review the key C++ concepts for implementing the Term 2 projects.

  • Project: Extended Kalman Filters in C++ Extended Kalman filters are used by autonomous vehicle engineers to combine measurements from multiple sensors into a non-linear model. Building an EKF is an impressive skill to show an employer.

• Unscented Kalman Filter The Unscented Kalman filter is a mathematically-sophisticated approach for combining sensor data. The UKF performs better than the EKF in many situations. This is the type of project sensor fusion engineers have to build for real self-driving cars.

Project: Pedestrian Tracking / Fuse noisy lidar and radar data together to track a pedestrian.

• Motion Study how motion and probability affect your belief about where you are in the world.

• Markov Localization Use a Bayesian filter to localize the vehicle in a simplified environment.

• Egomotion Learn basic models for vehicle movements, including the bicycle model. Estimate the position of the car over time given different sensor data.

• Particle Filter Use a probabilistic sampling technique known as a particle filter to localize the vehicle in a complex environment.

• High-Performance Particle Filter Implement a particle filter in C++.

Project: Kidnapped Vehicle / Implement a particle filter to take real-world data and localize a lost vehicle.

• Control Learn how control systems actuate a vehicle to move it on a path.

• PID Control Implement the classic closed-loop controller — a proportional-integral-derivative control system.

• Linear Quadratic Regulator Implement a more sophisticated control algorithm for stabilizing the vehicle in a noisy environment.

Project: Lane-Keeping / Implement a controller to keep a simulated vehicle in its lane. For an extra challenge, use computer vision techniques to identify the lane lines and estimate the cross-track error.

• Tracking pedestrians for self driving cars

• Helping a Self Driving Car Localize itself: Priya Dwivedi, Particle Filters: higher accuracy — less than 10 cm, [GitHUb]

• Robot Localization using Particle Filter: Arun Kumar

• Udacity Self-Driving Car Nanodegree Project 6 — Extended Kalman Filte: Jeremy Shannon

• Tracking pedestrians for self driving cars: Priya Dwivedi

• Udacity Self-Driving Car Nanodegree Project 7 — Unscented Kalman Filter