Software: A Key Differentiator

This past weekend I had the happy accident of tuning in to my local PBS station just as it was airing the Nova episode called The Great Robot Race. The show covers the DARPA Grand Challenge, the 132-mile autonomous vehicle race that was run—and, amazingly, won—on October 8. 2005. Seeing it reminded me how much I'm looking forward to hearing Sebastian Thrun, leader of Stanford University's winning team, speak at Sensors Expo on June 6.

Do Yourself a Favor
If you don't know about the DARPA Grand Challenge, you should. It has implications for your work, but perhaps more importantly it will charge your imagination and your spirit. And if you're familiar with it already, give yourself a treat and refresh your memory. Think about how five completely autonomous vehicles drove themselves 131.2 miles across the Mojave Desert, dodging obstacles such as wrecked cars and hay bales, chugging up hills they couldn't see over, and conquering other challenges that sometimes trip up humans.

PBS has done an excellent job of documenting the Nova installment. You can watch a 1.5 minute preview, and continue on to see the whole episode, which is broken into "chapters" for easy viewing. There's also a transcript of an interview with Thrun.

The Software Perspective
While the interview is great, I was disappointed to see that it doesn't cover the philosophical difference between Thrun's team and the other Grand Challenge competitors. In the broadcast, Thrun emphasized software in Stanford's approach to the Grand Challenge—whereas, he said, the others emphasized hardware. This is a fascinating distinction.

The Nova episode was filmed before the 2005 Grand Challenge race was run; the segment's directors didn't know at the time that the Stanford team would win. Although Thrun has a long history in autonomous robotics, this race was his team's first DARPA Grand Challenge entry, whereas the other teams whose robots crossed the finish line were Grand Challenge veterans.

I won't go so far to say that the software approach made all the difference in Stanford's win—I just don't know (though Thrun is slated to discuss the role of software in his keynote at Sensors Expo). But for sure, software is an increasingly important ingredient in making the most of sensor hardware in any situation. While hardware continues to make important progress, it can go only so far. Software is necessary to make extrapolations and tie in to other data sources and systems for greater knowledge and usefulness.

The Personal Future of Autonomous Vehicles
Meanwhile, I'm grooving on Thrun's view of the future. He notes that automotive technology is still fairly new and that it will undoubtedly evolve to include autonomous vehicles for the masses. "I personally think cars that drive themselves will have their greatest impact in everyday life," he said in the Nova interview. "For instance, for elderly people, typically when they stop driving, their social networks go away because they can't see their friends anymore. They can't go shopping. They become dependent. And it often correlates with physical deterioration. What if we give them a car that drives itself? . . . They'd certainly have a better quality of life."

Gee, I could have used one of these cars when I was on crutches a couple of weeks ago.

Suggested Articles

Sidewalk is designed to allow neighbors to share a wireless network for IoT devices

President Trump issued his “blessing” of the tentative deal on Saturday and then directed a delay of a week of a ban on TikTok downloads.

Company also foresees a Poseidon generation for 2022 and beyond