The latest entrant into the ridesharing world is Cruise, which has alpha-launched Cruise Anywhere, a self-driving ride-sharing service for their San Francisco employees.

The service is available to 10% of Cruise’s employees, and only within San Francisco, making the “Anywhere” portion of the title fairly aspirational.

Nonetheless, I remain impressed by the progress of Cruise, which GM bought for a reported $600MM-$1BB a year and a half ago. Often when big industrial behemoths purchase small Silicon Valley startups, the startup gets sucked into the corporate vortex, the employees flee, and in a few years there’s nothing left.

GM has managed to keep Cruise running like something approximating a startup, and Cruise keeps pushing the envelope, with what I believe are more fully autonomous miles driven than any other automotive manufacturer. If Cruise gets to the point where they are putting actual, non-employee passengers in the car, that will be yet another step forward.

Cruise also released a promotional video highlighting Cruise Anywhere. The best part? Cruise Anywhere is dog-friendly.

My boss, Sebastian Thrun, somewhat famously won the 2005 DARPA Grand Challenge. The car built by his Stanford team successfully traversed the 150-mile desert race course. That led to Sebastian’s role building the Google Self-Driving Car Project, and now the Udacity Self-Driving Car Engineer Nanodegree Program.

Less well-known is the 2004 DARPA Grand Challenge, the year prior, in which no vehicle finished. In fact, no vehicle made it further than 7 miles. Most vehicles just died altogether.

Wired has a pretty neat oral history of the 2004 DARPA Grand Challenge. It’s short and worth a quick read.

The most impressive aspect of the 2004 race, really, is that there even was a 2005 race. After watching every vehicle fail in 2004, DARPA threw down the gauntlet again in 2005, and the rest is history.

A reporter asked, “Well, what are you gonna do?” I said, “We’re gonna do it again, and this time it’s going to be a $2 million prize.” It was so successful and yet so not successful, I had to do it again.

Having grown up in Virginia, I generally keep tabs on Virginia news, especially when it comes to self-driving cars.

This story from ARLNow about a fake self-driving car in Virginia is a little silly. But it’s also bizarre enough to make me wonder what on earth is going on.

I assume that the Virginia Tech Transportation Institute is running some study on whether drivers will even notice self-driving cars. Mission accomplished on that one.

The follow-up tweet by Adam Tuss of NBC is the best. “I’m with the news, dude!”

Motion planning might be the area of autonomous vehicle development that is most open to new discovery right now.

As part of the Udacity Self-Driving Car Nanodegree Program we teach a one-month module on Path Planning that covers environmental prediction, behavioral planning, and trajectory generation. These are the three key components of a planner.

Of these three components, trajectory generation is well understood, and environmental planning involves so much uncertainty that basic estimates are fine.

But behavioral planning remains an unsolved problem. How do you best determine which maneuver to make when cars and bikes and pedestrians are moving around?

One approach is to build a finite-state machine. This is in fact what we and our partners at Mercedes-Benz teach in the Nanodegree Program. Finite state machines work well for highway driving, which is structured. But it can break down in the chaos of urban driving; urban driving requires so many states.

So what other options are available?

Wikipedia’s entry on Motion Planning actually provides a pretty thorough high-level overview.

There are so many options! If you’re interested in becoming a path planner, it’s worth a quick read.

The United States Congress, which has been a pretty partisan institution lately, pushed forward a bill to support self-driving car adoption on a unanimous vote.

This is still a long way from becoming a law — right now it’s just a bill that got reported out of committee — but it’s got momentum.

I have a complicated reaction to this, based on a combination of my knowledge of the industry and my personal politics, but an interesting reaction was from my colleague Andrew, who posted this to our internal Slack channel and said, “It’s real now.”

Andrew’s sense was that for a bill to actually start working its way through Congress, there needs to be an army of lobbyists and interest groups who care enough about it to spend real money pushing it forward.

The fact that the automotive industry cares enough to move these bills forward convinces Andrew that they’re really serious.

Honstely, I was convinced by the billion dollar investments. But either way, it’s real now.

A great irony of the mobile phone revolution of the late 2000s and early 2010s is that so few great technology companies grew out of that disruption. The companies that dominate the mobile ecosystem — Apple, Google, Facebook, Amazon, Netflix — were all born long before smartphones hit the market.

The largest tech company that grew out of the mobile revolution is (I think) Uber. Which is ironic because smartphones at first glance seem to have so little to do with ridesharing.

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I once got to listen to Warren Buffett talk in person, and he relayed how in the 1990s he and Bill Gates spent a lot of time trying to figure out the key opportunities provided by the Internet. But search engines never occurred to them.

Similarly, when I was in business school in the 2000s, I spent a lot of time trying to figure out what the disruptions the smartphone would bring. Transportation never occurred to me.

Now the question is what disruptions will self-driving cars bring? And the answer might be something that isn’t occurring to anybody.

Less than two years ago, a team from Microsoft Research Asia made a huge splash by introducing ResNet, a deep neural network that used residual learning and “skip” connections blew away the competition in image classification.

Sure enough, one of the co-authors of that paper, Shaoqing Ren, has departed MSRA to start a self-driving car company with some of his MSRA colleagues. Ren is also the author of the Faster-RCNN paper, making him something of a star in the world of deep learning.

This is news now because Momenta, the startup founded by Ren and his colleagues, just raised $46 million dollars in funding from NIO and Daimler and Sequoia China.

I know almost nothing about Momenta, but I’m taken by one section of their homepage, which describes their approach to data-driven path planning:

Our data-driven approach is to build a driver with billions of miles of driving experience. Crowdsourcing allows us to obtain billions of driving trajectories localized in semantic HD maps. By mapping from environment perception data to driving trajectories in semantic HD maps, we conduct autonomous driving planning. This provides us a unique and elegant framework to solve corner cases by adding corresponding data rather than adding rules.

I’m excited to see how the Momenta founders apply deep learning to path planning.

Even as Indian technology companies begin working on self-driving cars, India’s Highways Minister says they won’t be allowed on the road.

Union road transport and highways minister Nitin Gadkari said on Tuesday, “We won’t allow driverless cars in India. I am very clear on this. We won’t allow any technology that takes away jobs. In a country where you have unemployment, you can’t have a technology that ends up taking people’s jobs.”

This attitude crops up in other industries in the US, although often “safety” is the given reason, even when “jobs” is widely understood to be the real reason.

For example, in a few US states it’s illegal to pump your own gas, because “safety”. Similarly, the laws on telemedicine vary widely across the US, again because “safety”.

At least the Indian government is being honest about why they’re banning self-driving cars.

Nonetheless, it seems hard for me to believe this ban will last. More than any other country I can think of, India has seen its economy transformed because of information technology. It’s hard to believe the country will sit out the next wave of the computational future.

“There was a similar debate when computers came in. Not all technology leads to joblessness. You have to have the right balance. Technology has to coexist,” said Abdul Majeed, automotive leader, Price Waterhouse & Co.