Navigant Research Blog

Autonomous Vehicles Drive Themselves toward Reality

— March 19, 2014

Australian startup Zoox made a splash at the LA Auto Show in December by hosting a stand at the Connected Car Expo to promote its ideas about autonomous vehicles.  Zoox’s view is that the best way to introduce fully autonomous driving is to start with a clean sheet of paper and develop a new type of transport from scratch, rather than incrementally changing existing vehicles.  The initial concept currently under development is a taxicab that uses a chassis made of four identical quadrants.  Each quadrant will have a wheel with its own electric motor, and all four wheels can steer.

The passenger compartment will have no steering wheel or pedal controls and will utilize a carriage layout, with passengers facing each other.  The design is being optimized for rapid prototype manufacturing techniques rather than mass production.  Zoox is targeting taxi fleets as its first customers, because the business model shows that the biggest savings come from eliminating the drivers’ wages.  The vehicles will be designed for low-speed travel on city streets.

Experience Not Required

At the Autonomous Driving conference in Berlin hosted by we.CONECT, the Zoox team actively sought feedback from the other participants.  They are in the first year of a 7-year product development plan, so there is no vehicle to sit in at present, but the overall concept is well thought out and some detail work has begun.  I am sure that the Zoox developers will be tracking progress of the Navia, a robotic driverless shuttle, and Tesla has shown what can be accomplished in the automotive industry without decades of experience.

One recurring theme in Berlin was how to develop an automated driving system that can return control to the driver safely when necessary, particularly when road conditions change beyond what the developers anticipated.  While driver assistance functions are steadily getting more sophisticated, there are huge advances to be made before people can safely be removed completely from the driving process.  Today’s incremental improvement process involves automating the control systems that have been developed over the last century for humans to use.  This seems to be the fundamental challenge that Zoox has identified, and it wants to approach the solution from the other direction.

Maps from the Cloud

In addition to the intriguing Zoox concept, the presentations at the Berlin conference were of high quality and networking opportunities were abundant.  Here are some of the highlights that I noted that gave me some fresh perspectives on the current state of autonomous driving technology:

  • Professor Emilio Frazzoli of the Massachusetts Institute of Technology pointed out that the biggest potential benefit from autonomous driving will be carsharing, far exceeding improved road safety.  His detailed analysis of traffic in Singapore indicated that 800,000 personal cars could be replaced by only 300,000 shared autonomous vehicles.
  • Dietmar Rabel, from digital map company HERE (formerly known as NAVTEQ and Nokia Location & Commerce), promoted the Internet cloud for continuous map updates and introduced the concept of crowdsourcing for accurate map data.  Rather than relying on map suppliers to continuously update the information, sensor data from connected vehicles could be shared through the cloud, thus providing near real-time updated map and road condition information locally.
  • Geoff Ballew of NVIDIA explained how his company has grown from a supplier of graphics boards for PCs into a high-performance computing specialist.  Rapid data processing will be a key requirement for self-driving vehicles to become a reality.

While the automotive industry makes slow but steady progress toward the goal of a self-driving vehicle, it’s also good to hear about new companies approaching the topic from a different perspective.  I shall continue to watch with interest as I work on an update to Navigant Research’s 2013  Autonomous Vehicles report.

 

Self-Driving Cars and Real-World Roadways

— February 16, 2014

On a recent weekend road trip, I took the opportunity to consider the practicality of an autonomous vehicle doing the driving. The 300-mile journey involved single-lane twisty country roads, dual carriageways (in U.S. terms, a four-lane divided highway), and motorways (freeways). The first part of my journey took place on a narrow country road with speed limits that ranged from 30 mph through small villages to 60 mph on the open stretches. On this route, there were very few opportunities for passing, so the driving process was relatively straightforward. A combination of the latest advanced driver assistance systems (ADAS) should be able to cope with such a drive with minimal driver input.

The next part of the journey took place on a dual carriageway, and again the driving process was quite simple, requiring that my vehicle stayed within well-marked lanes, kept to the speed limits, and avoided running into the back of slower vehicles.  All these functions could be handled by adaptive cruise control, lane keeping, and traffic sign recognition.  The one activity that would need a new system is lane changing to move to the inside lane when not overtaking. Blind spot detection would be a partial solution to this, but it would also need some highly sophisticated decision-making software.

Smoothing the Flow

The bulk of the driving took place on the U.K. motorway system, and the satnav in my car proved that it could handle giving directions to navigate the quickest route. Driving on motorways is where the benefits of autonomous vehicles would be more widespread. For some of the journey, traffic moved along briskly at the speed limit, but as vehicle volumes increased, there were periods where all lanes of traffic slowed down. If all the vehicles in the outside lane used adaptive cruise control, the traffic flow would be much smoother, and some traffic jams would be eliminated.

So the three main parts of my journey could have been handled effectively by technology that is available today. Intersections, however, represent more of a challenge. Simple traffic lights at a crossroads are not too difficult, but some roundabouts are a different matter, and will require considerable development of decision-making software. While the mechanics of driving can be replicated today, the role of the driver cannot. There are many considerations involved in driving, such as estimating closing speeds of vehicles in front and behind to decide whether it is appropriate and safe to change lanes. Anticipating what other drivers will do is another useful driving skill. It may be that an artificial intelligence system that can learn from experience will be a key component of the self-driving vehicle of the future.

10 Years Out

Some of the more advanced autonomous driving features that I outlined above will be coming to market in the next few years. As long as they are treated as driver assistance features, I believe they will be very attractive to customers and will contribute to safer and more efficient road travel. Full details about all the systems are described in Navigant Research’s recent report, Autonomous Vehicles. However, the jump to fully autonomous driving that can handle any situation remains at least a decade away. We can forget about catching up with emails or sleep while the car does the driving for many years, but the number of crashes due to driver error will surely be reduced, and soon.

One consideration for governments at present is how to encourage the development and implementation of this advanced driving technology. On one section of the trip, there was an alternative toll road to the standard highway. It appears that the majority of drivers prefer to travel on the free roads even when road work causes lane narrowing and speed limit reductions. It would improve revenue if more people used the toll road, so perhaps an incentive for drivers who use ADAS would make sense. A toll road that offered higher speed limits for vehicles with self-driving capability would both generate demand for the technology and increase road revenue.

I am looking forward to discussing these and other autonomous vehicle issues with industry colleagues at the upcoming Autonomous Driving 2014 conference in Berlin, Germany, February 27-28, 2014. I hope to share Navigant Research’s perspectives on the topic and learn more about other aspects of this rapidly evolving technology. Let me know if you will be there.

 

Making Cars Smarter – and Safer

— February 11, 2014

In early February, the U.S. National Highway Traffic Safety Administration (NHTSA) announced it will begin to promote vehicle-to-vehicle (V2V) communication technology for light vehicles.  The announcement focused mainly on the technology helping drivers avoid crashes.  Since August 2012, the Department of Transportation has been running a safety pilot program in Ann Arbor, Michigan, where about 3,000 vehicles were deployed in the largest road test of V2V technology yet.

In its early phase, the trial has centered on gathering, interpreting, and managing data from all the vehicles driving in the vicinity.  Later, the pilot vehicles will move on to sharing that information to warn against collisions.  One of the key targets is to improve traffic safety at intersections.  Other future benefits may include traffic jam and road hazard alerts.

The hardware required for V2V systems is not very expensive and most modern vehicles are already equipped with some of the necessary sensors.  V2V systems can also be easily made available as an aftermarket retrofit so that existing vehicles can participate.  The benefits of the technology, though, will remain limited until the percentage of V2V-equipped vehicles on the road is very high.

Speed Saves

Some articles in the media are quoting NHTSA as estimating that V2V could prevent up to 80% of accidents that don’t involve drunken drivers or mechanical failure.  That could be true in an ideal scenario where 100% of vehicles are operational with V2V.  But in practice, it would only take one vehicle without the communication device to cause a serious accident.  For example, when passing on a two-lane road, the V2V system could detect if a vehicle is coming the other way – but only if the oncoming car is also equipped with a working system.

Another challenge is the wireless communication technology.  To date, the V2V testing has used the 5.9 GHz frequency band.  This band has been reserved for dedicated short-range communications, a situation that is essential for safety systems because it minimizes transmission latency.  Yet, the Federal Communications Commission (FCC) is considering opening this band to unlicensed Wi-Fi devices.  In that case, it would be difficult to guarantee that V2V would react quickly enough to prevent accidents.

NHTSA is considering requiring V2V communication equipment in all new cars.  Another big challenge will be ensuring that the technology is compatible with future developments.  Requiring original equipment manufacturers (OEMs) to fit this technology is one way to kick-start the rollout of V2V, but full effectiveness will only be achieved if there are significant incentives for drivers to install the systems on existing vehicles.

 

Google Robots: More About the Patents Than the Products

— December 18, 2013

Google has quietly bought up more than eight bleeding-edge robotics companies in the last 6 months.  They include Bot and Dolly, a designer of robotic camera systems, Boston Dynamics, the creator of the famous Big Dog, and Industrial Perception, an machine learning engineering firm.  Clearly, the Mountain View, California-based search giant is planning a big move in robotics.  But it might not be what everyone is expecting.

While the head of the robot blitz, Andy Rubin, has declared that a Google robotics product will be available soon, that might end up being more of a sideshow than the real goal.  The prize for Google in this shopping spree is in the patents, not the people or the products.  That’s because, it’s my belief that, Google’s primary interest is in creating the operating system for the next generation of robots, not the robots themselves.

Rubin has always been obsessed with robot design.  In 2003 he chose the name for his photography software startup, Android Inc., as an homage to his obsession with robotics.  A year later, the company pivoted to a different business model: smartphone software.  Bought by Google in 2005, the platform that Rubin and his team created became the Android Operating System, a multibillion dollar enterprise, which is Google’s primary engine of profit growth today.

Android, Again

I believe that Rubin is returning to his original passion: creating a universal software platform for robotics.  If that is what he is doing, then it would make sense that Google’s executives and board of directors would fund it.  After all, the biggest obstacle to the ability of Android (the mobile phone OS) to completely take over the smartphone industry are the patents they don’t own that are required to make Android phones work.  Apple’s phones have some functional advantages that are protected by its patents.  And Microsoft gets more money from Google’s royalty payments for its smartphone patents than it gets from its own smartphone operating system.  This is all because Google was slightly late to the smartphone party.  It doesn’t want to be late to the next big thing.

A bigger clue as to where Google is going with this is in another of its robotic ventures: autonomous vehicles. That’s an area near and dear to our hearts here at Navigant Research because we published our first report on the topic, Autonomous Vehicles, in November. The search giant puzzled the world in 2010 when it divulged that it was experimenting with driverless cars. After the announcement, a few tittering articles were written about Google becoming a car company, but that hasn’t happened. Instead, Google has been hiring the brightest minds in the field of autonomous vehicles, getting them to invent things, and then salting away the patent trove. At some point, the income stream from those royalty payments will be considerable, all without Google ever having to learn how to bend steel.

So my best guess is that Google will utilize the talent it has acquired in the eight robotics company acquisitions (as well as many more that have probably been made that have so far gone unreported) to make a few flashy products.  Maybe it will be a disaster recovery robot or a land mine detection robot.  But the real treasure for the company will be sitting in the file cabinets of the U.S.  Patent and Trademark Office, where the more than 600 patents (according to my initial count) that go along with those acquired companies, will be sitting, waiting for this robotics thing to take off.

 

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