Learning about LIDAR (WIP)August 26, 2017
This post is largely a work in progress, I’ve made a lot of progress in learning that hasn’t been captured here-just need to find more time to make my thoughts more clear in this sort of format.
There are some tools and things I want to pick up in order to manage self-driving car data. In this document, I track the resources and tools I’m picking up to get to the knowledge I have currently. To be honest, I don’t have a very strong physics or computer graphics background, so my selection might reflect that.
What is LIDAR? (high-level)
Industry players/Self-driving car tech
- Winner-takes-all effects in autonomous cars
- Driverless Tech Startups Are Driving Past a Trillion-Dollar Opportunity
- Under the Hood of Luminar’s Long-Reach Lidar
- Quanergy Announces $250 Solid-State LIDAR for Cars, Robots, and More
From this, it seems like the major industry players are
- Velodyne (LIDAR)
- Quanergy (LIDAR)
- Waymo (Alphabet) (everything)
- Uber ATG (software, maybe everything?)
- Lyft (software, maybe everything?)
- Cruise (acquired by GM) (everything but especially software?)
The up-and-coming ones seem to be:
- Drive.ai (the whole car)
- Voyage (the whole car)
- Zoox (the whole car)
- Comma.ai (self-driving car software)
- Luminar (LIDAR)
If I was seriously trying to be a strategist, a competitive landscape map would be apt here. But it’s simply nice to be aware of the players in the space in order to roughly forecast who might be successful in the space and make a decision about joining one of these companies.
The nice thing about all of these new companies attempting to be “thought leaders” in the space is that they publish so much accessible material about the technology.
There are some startups in the space that are making it easier to construct self-driving car data sets. Such as Scale.
LIDAR point cloud data format
Object classification (assumes some ML experience already)
A self-driving car is one piece of a very data-intensive system (the other parts are probably the cloud or servers on-prem, etc.). I have a gut feeling about language interoperability being a powerful tool for writing data-intensive systems quickly. Since if it is true that some engineers (particularly in the research and scientific communities) are more productive when they are writing Python, then there is benefit from investing in using tools that can make Python go faster. Things like Boost Python come to mind…
This is a pretty cool blog post by one of the TA’s of a computational biology class I took a few years ago. It discusses writing a Python module that wraps C++ code, including unit tests: Building and testing a hybrid Python/C++ package
More to come.