I will be in Detroit next Monday and Tuesday, speaking at the CAR HMI USA Conference! If youâll be at the conference, please stop by at 9:30am on Tuesday to say hello.
If you wonât be at the conference, but youâre in the Detroit area, send me an email (david.silver@udacity.com). I am looking forward to meeting with people while Iâm in the area, especially people who are interested in the Udacity Self-Driving Car Nanodegree Program.
And if youâre a Udacity student, join the #detroit channel in the student Slack community! Weâre organizing an event for students.
I jest, of course. Baidu is going to release a âfreeâ operating system for self-driving cars, although the news leaves unclear whether this will be âfree, as in lunchâ or âfree, as in speechâ.
But Baiduâs goal quite clearly seems to be commercialization, whereas Udacityâs project is primarily educational.
The article in the MIT Technology Review supposes that Baidu is undertaking this as a catch-up move in a race with the Silicon Valley giants. Perhaps by getting more companies on its operating system, it can get more data.
If so, this really does look like a repeat of the mobile phone wars, and Baidu might need to be prepared to spend a lot of money to support its free operating system.
Here are posts from five Udacity Self-Driving Car students, sharing what theyâve learned about the program, their projects, Docker, and even how to hack your own car!
Andrew provides the most comprehensive review (in Spanish) of the Self-Driving Car Nanodegree Program that I have seen yet. He covers the forums, the mentors, the hiring partners, the classes, and all of the projects. Itâs a very positive review, which is flattering:
Gungor provides a concise tutorial for students looking to spin up Docker for the Self-Driving Car Nanodegree Program:
âI just realized there are still a lot of people having problem with Docker and starter kit for Self Driving Car Nanodegree program. In this post, I will give you a step by step guide.â
Muddassir covers some really cool data augmentation he performed on his Behavioral Cloning training set. By the end, his network is able to drive multiple laps around the crazy jungle track!
âI used a python generator in order to feed training batches to the network. The generator I designed also augments the data before generating a batch. I apply different types of augmentation to the data such as varying the brightness, color saturation, adding random shadows, translations, and horizontal flips to the images.â
Harish reflects on the challenges of the program, what was awesome about it, and what we need to improve:
âDuring this period, I have spent many all-nighters chugging down on redbull and coffee in an attempt to consume enough caffeine to stick it out and get through the various hurdles the course throws at you. I have on multiple ocassions spent days working on an idea only to get so frustrated with the results and progress to go ahead and scrap it entirely. Only to later realize that I had been right all along but made a tiny error in executing it!
In hindsight(*spoiler alert), it was worth all the trouble!â
Ariel is building a self-driving car from scratch, and has learned all sorts of practical lessons. This is a great list to read if you want to learn from somebody who is hacking a car:
âCan buses are good, dual can buses are great. They allow you to be able to separate key traffic in order to be able to âreplaceâ a factory module like the LKAS or the ACC. Get an Arduino due with dual can. ($70)â
Weâll be filming a Q&A with him this week about self-driving cars and the Udacity Self-Driving Car Nanodegree Program.
This is an awesome opportunity to pick the brain of the winner of the DARPA Grand Challenge, the founder of the Google Self-Driving Car Program, and the driving force between the Udacity Self-Driving Car Nanodegree Program!
Self-driving trucks have been a concept in mining operations for many years, because of the well-structured, private roads and dependable routes. Dump trucks basically drive the same route over and over, which makes them an ideal target for autonomous technology.
âFortescue Mining Groupâs Solomon Hub comprises the Firetail and Kings Valley iron ore mines in the Pilbara region of Australiaâs North West which together have a production capacity of over 70 mega tonnes each year. When the project was scoped in 2010, the initial feasibility study called for 75 manned trucks but in July 2011 FMG ordered 12 autonomous 793F vehicles as a pilot. Now with the mines up and running, FMG operates 54 driverless dumpsters which alone results in a $100 million capital saving on twenty trucks.â
Thereâs also this:
âBy replacing the drivers, Westrac and Caterpillar also found they can make further cost savings by eliminating some comfort and safety features on the trucks with weight savings of up to four tonnes per vehicle.â
Iâve had a few people come to me recently asking about how to get up and running in this industry. Iâm not that knowledgeable about mining, but the fact that people are asking makes me think this isnât yet a solved problem.
Appleâs autonomous vehicle work has been an open secret for a few years, so Iâm skeptical that this announcement will change much or lead the way to a more meaningful understanding of what Apple is working on.
Beyond just Apple, though, this has me back to thinking about the tradeoff between stealth and transparency.
Transparency in product development seems like an aggressive approach. By opening up about what theyâre doing, companies hopes to attract the best talent, the best partners, the earliest and best customers.
Conversely, a stealth approach seems cautious. Companies developing products in secret seem nervous with competitors and the press. Competitors might steal key elements of a developing product, while the press might pressure a company to alter its schedule or pricing or go-to-market strategy.
All else equal, it seems more fun to be aggressive than cautious, but of course all else is never equal. A company is in a good position and has a lot to lose has a lot more reason to be cautious and stealthy. A company in poor position, with nothing to lose, is likely to act aggressively and transparently.
What gets interesting is when companies like Apple, which seems to have nothing to lose in the automotive industry, approaches product development secretively, perhaps because of its culture.
When GM announced its $1 billion acquisition of Cruise Automation, I was skeptical. Cruise was a San Francisco software startup; GM is a venerable American automotive company with a bureaucracy that I presume is optimized for rolling vehicles off the assembly line. It was not obvious that this was a match made in heaven.
But the acquisition is now over a year old and it seems to be working out really well.
The most recent news is that GM will be adding 1100 jobs to its San Francisco office over the next five years.
Self-driving Chevy Bolts are maybe not quite ubiquitous in San Francisco, but theyâre normal enough to make me guess that GM is probably the number two tester of Level 4 autonomous vehicles in the Bay Area (and the world?), after Google.