DSHR's Blog: More On Robotaxis

Tuesday, August 11, 2026

More On Robotaxis

Cybercab<br>Liam Denning reports on Tesla's quarterly results flop in Someone Call Tesla a Robotaxi, or SpaceX, Quick. He notes that:

The curated list of investor questions that gets teed up ahead of these affairs had indicated some brewing discomfort about the big issue: Tesla’s lack of progress on its long-promised mass rollout of robotaxis. Musk duly tried to finesse this by pointing out that the "constraint" is safety, with Tesla trying to scale as fast as possible "while trying to ensure that we do not harm anyone."

It is good that Tesla is "trying to ensure that we do not harm anyone" but they need to try much harder. In last February's Tesla's Not-A-Robotaxi Service I quoted Fred Lambert:

By the company’s own numbers, its "Robotaxi" fleet crashes nearly 4 times more often than a normal driver, and every single one of those miles had a safety monitor who could hit the kill switch. That is not a rounding error or an early-program hiccup. It is a fundamental performance gap.

But I went on to point out that Lambert was making the wrong comparison:

However badly, Tesla is trying to operate a taxi service. So it is misleading to compare the crash rate with "normal drivers". The correct comparison is with taxi drivers. The New York Times reported that:

In a city where almost everyone has a story about zigzagging through traffic in a hair-raising, white-knuckled cab ride, a new traffic safety study may come as a surprise: It finds that taxis are pretty safe.

So are livery cars, according to the study, which is based on state motor vehicle records of accidents and injuries across the city. It concludes that taxi and livery-cab drivers have crash rates one-third lower than drivers of other vehicles.

A law firm has a persuasive list of reasons why this is so. So Tesla's "robotaxi" is actually 6 times less safe than a taxi.

In any rational jurisdiction, six times worse than the competition even with a safety driver would get the regulators to force Tesla back to the drawing board.

The New York Times article was the best I could find at the time but it was from 2006. We now have much better and more recent data. Follow me below the fold for one of the reasons why Tesla's numbers are bad, very probably even worse than six, and even some criticims of Waymo's numbers.

On the earnings call:

Tesla even got its head of AI software, Ashok Elluswamy, to tout another safety statistic, saying that Tesla’s robotaxis have thus far driven 380,000 miles unsupervised with "zero notable incidents." This, in his telling, validates the company’s cameras-only approach to sensors, without the need for lidar, radar and other safety technologies recommended by "so-called experts." Besides Tesla getting to define what "notable" means, however, 380,000 miles is, in robotaxi terms, like just getting to the end of your driveway. It equates to less than 0.2% of the "rider-only," or unsupervised, miles that Waymo LLC had already racked up through March.

Source<br>Fred Lambert reported on how one "so-called expert", Waymo co-CEO Dmitri Dolgov, responded in Waymo CEO explains why Tesla’s camera-only self-driving falls short:

Dolgov made the comments in a talk at Y Combinator’s Startup School, walking through the lessons Waymo has learned building its driver over close to two decades. He put the sensor question on the table plainly: "there’s been a long-standing debate about what kind of sensors do you actually need for autonomous driving."

His answer draws the line that camera-only advocates tend to skip right past. "Humans of course can drive with just eyes, so there’s that proof of existence," he said. "If the goal were to just approximately match human performance or to build an assist product, that’s a very reasonable way to go."

Then the catch. If you’re targeting full autonomy and strongly superhuman performance, he said, "you find that weak sensing just leads to a safety curve that flattens out way too early."

Ojai, Dllu, CC BY-SA 4.0<br>Dolgov explained Waymo's multi-modal approach:

Waymo uses three sensing types, and Dolgov spent real time on why. Cameras give you high resolution and color, but they’re passive, and they degrade in darkness and glare. Lidar directly measures the 3D structure of the world. Radar punches through fog, rain, and snow, and reads velocity directly with Doppler. Lidar and radar are active sensors, so they see just as well in pitch darkness or straight into a blinding sunset.

This isn’t redundancy for its own sake. "These different sensing modalities, they’re not backups to each other," Dolgov said. Each one runs its own encoder, and the data fuses into a single view of the world that he says is "vastly superior to what you get with any one sensor."

He showed cases where multiple modes were essential:

A dust storm in Phoenix, where the camera sees almost nothing while lidar cleanly picks out a pedestrian at the...