논문

• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

• Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

• A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms

2017: The year for autonomous vehicles

0. 개요

Self-Driving Engineer

• How to Become a Self-Driving Car Engineeer Talk : 추천, ppt,Jupyter코드 포함
• But, Self-Driving Car Engineers don’t need to know C/C++, right? : 필요 지식 및 기술(개인 의견)
• Self Driving Car Engineer Deep Dive
• Who’s Hiring Autonomous Vehicle Engineers
• Five Skills Self-Driving Companies Need
• [5 Things That Give Self-Driving Cars Headaches](https://getpocket.com/a/read/1625729922): 예측 불가 인간, 날씨, 우회길, 웅덩이

Youtube

• 16 Questions About Self-Driving Cars[Q List]

• Autonomous Vehicles Overview : Wiley Jones,2016. 8. 28, 56분, Robotics, actuation, sensors, SLAM, computational platforms

• udacity SDC 2 한글 설명(Youtube)

논문

• 논문: End to End Learning for Self-Driving : NVIDIA 2016 Paper
• 논문: End to End Learning for Self-Driving Cars : 카메라 3대와 운전자의 핸들 조작+알파로 학습한다음 카메라 하나만 입력으로 사용하고 운전대를 어떻게 움직이는지를 예측하여 자동운전, [YOUTUBE]

Affordable Self-Driving Cars

• 토론토 대학교 Raquel Urtasun 교수 연구팀

• ppt: Towards Affordable Self-Driving Cars

Youtube

• Raquel Urtasun - Towards Affordable Self-Driving Cars - The Frontiers of Machine Learning
• Raquel Urtasun - Q&A - The Frontiers of Machine Learning

Paper

• TorontoCity: Seeing the World with a Million Eyes: 2016

Nexar

Nexar is a community-based AI dash cam app for iPhone and Android : 홈페이지, Challenge

• You can compete to win prizes (1st place $5,000, 2nd place $2,000, 3rd place iPhone 7)

Challenge #1 : USING DEEP LEARNING FOR TRAFFIC LIGHT RECOGNITION

챌리지 개요/요구사항

• Recognizing Traffic Lights With Deep Learning : David Brailovsky

• The world through the eyes of a self-driving car : David Brailovsky

• How I learned deep learning in 10 weeks, then won $5,000 by recognizing traffic lights

Challenge #2 : Coming Soon

commai

• 홈페이지 [GitHub]
• 논문 : Learning a Driving Simulator

OSSDC

• Open Source Self Driving Car Initiative: GitHUb, OSSDC-VisionBasedACC, OSSDC-LKAS, OSSDC-SmartCamera

AutoX

• 센서 없이 카메라 만으로 자율주행차 구현을 목적으로 함

• 홈페이지: 인트로 동영상외 자료 없음

• CEO Professor X: CEO 프로필

• AutoX - Self Driving Car startup that makes sense: Meduim 소개글

• 신문 소개글

SegNet

• Homepage: 캠프리지
• 논문

Article

• Self-driving cars in the browser

• Towards a real-time vehicle detection: SSD multibox approach : Vivek Yadav

• SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving: GitHub

elcano project

• 홈페이지

• 코드

• C2 – Dual control: low level vehicle control, either from the driver or the AI.
• C3 – Pilot: Detects obstacles and feeds settings for the next path segment to C2.
• C4 – Path Planner: Computes the best route from current location to destination.
• C5 – Obstacle detection from sonars.
• C6 – Navigator: Reads GPS, INU, Odometer, Compass etc. to get best position estimate.
• C7 – Vision: Locates certain features of interest (Raspberry Pi)..

Implementation

• Source Code for “Self Driving Car Learns Online and On-board on Raspberry Pi 3”

장비/센서

• Lida : 간략 설명

• Mini Autonomous Vehicle

• Startup Watch: Luminar: Lidar 센서

Lab

• 버클리대 DeepDrive : 선진 연구 분야 살펴 보기 좋음

Startups

• NAUTO
• Zoox]

V2X

• Autotalks: automotive-grade communication chips

• Cohoda Wireless: automotive-grade communication chips

• Kymeta: automotive satellite communications

• RoboCV: collision avoidance with vehicle-to-vehicle communication

• Savari: vehicle-to-anything communication infrastructure

• Veniam: automotive mesh WiFi

Tools

Autoware

• Open-source software for urban autonomous driving