[MOOC] Apollo Lesson 2: HD Maps

· 2 min read · 256 words

Authors

This is my note for lesson 2 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: High Definition maps for self driving cars.

HD Maps have a high precision and contain a lot of information than your ordinary map on smartphone, such as lane line markings, 3D representation of the road network, traffic signs... You can what you see and GPS to locate your self in the world and identify other objects. However, it's very difficult with a self driving car, so we need HD Maps for current SDCs.

A navigation map carries a road graph; an HD map carries lane geometry, curbs, signs and a 3D surface

Precision

  • Navigation map on your phone: meter-level precision
  • HD Maps: centimeter-level precision

Localization on HD Maps

Self-driving car uses sensor and camera signals to recognize where it is on HD Map.

The live sensor frame is matched against the prior map to recover pose

Standard

  • Apollo uses OpenDRIVE map format, and improve it to become Apollo OpenDRIVE standard.

Map construction

  • Steps for map production:
Five stages: data sourcing, processing, object detection, manual verification, map products
  • [MOOC] Autoware Course - Lecture 1 - Setup the environment

    Lecture 1 notes from the Apex.AI Autoware course: getting the development environment up with Docker, ROS 2, Terminator, and Autoware.Auto, the errors I hit along the way, and how I fixed them.

  • [MOOC] Apollo Lessons on Self-Driving Cars

    Course notes from Udacity's Self-Driving Fundamentals, featuring Apollo. What the seven lessons cover, from HD maps and localization through perception, prediction, planning, and control. Every diagram in the series is redrawn.

  • [MOOC] Apollo Lesson 7: Control

    Lesson 7: steering, throttle, and brake that follow the planned trajectory while keeping passengers comfortable. PID, LQR, and model predictive control, with the tradeoffs of each.