Starsky Robotics Autonomous Trucks Won’t Use Lidar
The co-founder of Starsky Robotics believes that for the use case involving long haul trucks running over interstate highways that lidar is not needed. Let’s see what you think of the arguments.
This is a guest post in IEEE Spectrum by Kartik Tiwari.
Last year a partner at a well-known Silicon Valley venture firm wouldn’t take a meeting with me and my colleagues because our autonomous driving system didn’t employ lidar. He said vehicles had to use all available sensors to ensure safety.
I disagree. I’ll explain why, but first let me lay out the approach of my company, Starsky Robotics.
Unlike many other companies, we are not trying to automate every bit of driving, above all those parts that involve navigating crowded, chaotic urban environments. Rather, we specialize in long-distance trucking.
The problem we’re addressing is the shortage of truck drivers. It’s easy to see why they’re getting scarcer: Trucking’s a tough and tedious job, demanding long hours on the road every day and weeks, even months away from family and friends. And without enough truck drivers, the availability and price of goods must rise.
We’re automating the easiest part of the task, which is highway driving. The rest of the job remains in the hands of experienced truck drivers. They operate the rigs remotely when getting on or off the highway and during the tricky situations that occasionally happen on the highway itself. Such interventions take up less than 1 percent of the driving time.
That’s why my company began with the tool that’s closest to the human eye: the camera. They’re automotive grade, which means they’re engineered to work in road vehicles at all phases of their working life; also, they are both relatively cheap and available off the shelf. Another great thing about cameras is that they are highly customizable. Our prototype truck employs seven different cameras, each one configured and oriented to monitor a specific field of view, for a full 360 degrees of coverage.
For more reliability in our measurements we use automotive-grade radar. This, too, is a well-documented, well-understood sensor that is also available at a reasonable price. Radar is really good at sensing the existence of a potential obstacle and measuring its velocity. It’s not so good at identifying its precise location, and it also tends to create a lot of false positives; a manhole cover can loom larger than it is. So, to filter out the false positives, we fuse the data stream from radar with the stream from the cameras. Each sensor covers the weaknesses of the other.
That brings us to lidar. Why not add a third set of eyes, as it were?
There are indeed things that lidar does well. It’s really good at noticing obstacles close to the ground. And, because its laser provides illumination all its own, lidar can see as well in the dark as in the sunlight. But this sensor suffers from serious weaknesses.
Limited range is at the top of the list of weaknesses. A fully loaded truck moving at highway speed needs a lot of space to stop, at least 150 to 200 meters, and the long-range lidars now available can’t sense things in sufficient detail that far ahead. They return points that are too spread apart to provide the information required, rendering them useless.
Reliability is also limited. I mentioned that we use components that are automotive grade, that we’re certain will work over the lifetime of our vehicles, in all the various conditions we require. Lidar isn’t there yet. Some of the units tend to spin themselves apart; others are stuck together with glue. Many will fall apart after three to six months of use.
Perhaps he is not talking to the right lidar supplier.
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