These are outstanding numbers

Chinese robotaxi operators Pony AI and WeRide face sustained operating losses through 2028 as domestic fleet utilization lags Waymo’s rate of more than 58%, Bloomberg Intelligence said. 

The gap also widens the break-even timeline for Chinese operators relative to Waymo, BI said, adding that the profitability of the Chinese operators is dependent on domestic fleet density reaching critical mass — a threshold neither company is expected to hit before the latter half of the decade. Waymo’s utilization was flat even as it expanded its fleet size, and only jumped as trip length increased. 

Munich is emerging as a robotaxi hub in Germany. Following Minga, Waymo, and Mobileye, the Italian startup Niulinx is now also coming to Munich. In a LinkedIn post by Country Manager Germany Tim Neuroth, the start of test drives is announced, and applications are being sought for several open positions for test engineers and safety drivers. The location will be in Munich/Garching, with an immediate start date.

The Fiat 500 will be used as the test vehicle, which makes perfect sense for a robotaxi in the city

Mercedes expands the partnership with NVIDIA despite recently signing with Wayve. Clearly now pursuing a dual track approach.

Do you agree with such a move? What if the cars on its right started to proceed through the intersection?

October 2026 NHTSA SGO Automated Driving System crash data was recently published, covering 114 new reports from August 18, 2026 to September 15, 2026.

"Crash" means involved in a physical impact with a road user or property, from 3rd Amended SGO.

Of the top three in the list, Waymo's average reported pre-crash speed for new crashes this month was 6.94 mph, Zoox's was 6.07 mph, and Tesla's was 1.90 mph. Some of Tesla's were actually below 0.5 mph, which Tesla reports as 1 mph, so that's an overestimate.

Waymo's 72 reports included 9 with minor injuries and 1 with unknown injuries involving hospitalization; five of the 10 injury accidents were when the Waymos were properly stopped in traffic. 62 of the 72 crashes were with Jaguar I-PACEs, and 10 of the 72 with Zeekr RTs ("Ojais"). 30 of the 72 crashes occurred when the Waymo was stopped, and 17 when the Waymo was traveling 5 mph or less. 3 of the 72 crashes occurred mid-ride with unbelted passengers. Selected incidents:

• A Waymo was in the rightmost of 3 freeway exit lanes, when a vehicle in the center lane swerved into a vehicle in the left lane, then spun out into the Waymo's lane, causing a crash while the Waymo was traveling 23 mph, resulting in minor injuries WITH hospitalization.
• A Waymo turned into a left turn lane with standing water in Tempe AZ and stopped, when another vehicle rear-ended it. (Waymo reported both that it was stopped at the time of impact, and that its pre-crash speed was 8 mph, so one is probably a mistake). The driver and passenger in the other vehicle alleged minor injuries.
• A Waymo entered an intersection when a vehicle in a perpendicular lane ran a red at high speed, traveling at a high speed toward a red light; the Waymo slowed to 18 mph but was hit, and its passenger alleged minor injuries.
• A Waymo Ojai in Detroit drove into a pothole and had to be towed.
• A Waymo in Berry Hill, TN driving in a partially paved alley filled with debris was damaged by the debris and had to be towed.
• A Waymo Ojai in Philadelphia entered an intersection as a bus ran a red; the Waymo braked and was backing up in autonomous mode when the bus turned and hit it.
• A Waymo in a Scottsdale parking lot making a multipoint turn backed into a dumpster.
• A Waymo parking in a Los Angeles alley reversed at an angle into a dumpster.
• A Waymo in a Tempe parking lot reversed at an angle into a bollard.
• A Waymo in a Tempe parking lot reversed at an angle into a bollard.
• A Waymo in a Tempe parking lot reversed at an angle into a bollard a month after the first two, with location description of all three sounding the same.
• A Waymo in SF dropping off passengers partially in an active traffic lane was struck as a passenger opened its left rear door.
• A Waymo in SF dropping off passengers in an active traffic lane next to bollards separating it from a bike lane was hit by a right-turning bus.
• A Waymo in Atlanta picking up passengers partially in an active traffic lane was struck by a passing pickup.
• A Waymo in Los Angeles merging after a canceled drop-off in an active traffic lane was struck by a car to its left that then struck two other vehicles; its driver was transported from the scene for medical treatment.
• A Waymo in Los Angeles was stopped in traffic when the vehicle in front of it reversed, so the Waymo reversed to avoid them, but then the vehicle in front's open door struck another vehicle and the Waymo.
• A Waymo in SF stopped before an intersection for a fire truck, then stopped in the intersection to yield to a second fire truck when a pickup rear-ended the Waymo.
• A Waymo in FL driving 56 mph in the leftmost Interstate lane made contact with a passing motorcycle that was driving in both the leftmost and second-most-left lanes.
• A Waymo in AZ driving 45 mph was rear-ended by a vehicle trying to pass on its right.
• A Waymo in AZ driving 41 mph hit a vehicle that turned left from a parking left access road into the Waymo's lane.
• A Waymo in Los Angeles was driving 33 mph straight through an intersection in the right lane, when an SUV in the second-most-right lane tried to make a right turn across the Waymo's path, injuring the Waymo passenger.
• A Waymo in San Antonio was driving 13 mph straight through an intersection in the right lane, when an SUV in the second-most-right lane tried to make a right turn across the Waymo's path.
• A Waymo in SF driving 22 mph made contact between undercarriage and a metal sheet placed on a road, and had to be towed away.

Zoox's 14 collision involvements included 2 with minor injuries, 1 with unknown injuries, and 1 with dog injuries. They included 7 Toyota Highlanders with in-vehicle safety drivers in CA, TX, FL, or NV, and 7 Zooxes with no driver/operator in Las Vegas or SF.

1. Safety driver took over to enter parking garage and hit overhead clearance bar, causing an injury.
2. Stopped Zoox rear-ended, injuring safety driver's back.
3. Driverless Zoox braked hard for unleashed dog, then resumed and hit it at 10 mph. Didn't know until dog's owner contacted Zoox.
4. Driverless Zoox hit vehicle at 9 mph that braked hard for jay-walking pedestrian, after it abruptly changed into Zoox's lane.
5. Stopped Zoox side-swiped in hit-and-run.
6. Stopped Zoox side-swiped in hit-and-run.
7. Stopped Zoox rear-ended in hit-and-run.
8. Stopped Zoox rear-ended after hard-brake stop in intersection for emergency vehicle.
9. Stopped Zoox rear-ended at intersection; "camera footage showed that the driver of Vehicle 2 was not looking ahead before or at the time of the collision."
10. Stopped Zoox backed into by box truck that was parallel parking, but the truck driver denied it and refused to share information.
11. Stopped Zoox backed into by tractor-trailer as safety driver took over.
12. Safety driver took over and hit vehicle that suddenly began high speed U-Turn crossing through Zoox's lane. Autonomy system was braking when disengaged.
13. Safety driver took over in a cul-de-sac and reversed into a pole at 3 mph.
14. Safety driver took over and hit flying plywood from lead vehicle at 40 mph.

Tesla's 10 collision involvements reported no injuries. 9 were Model Ys, 1 was a Cybercab (Cybercabs joined Robotaxi fleet Sept 3).

1. Remote operator hit utility pole on right and dumpster on left in an alley.
2. Remote operator hit a parked car at 0.40 mph trying to navigate between vehicles.
3. Supervised Cybercab drove into large, water-filled pothole at 5 mph.
4. Driverless Robotaxi hit utility meter in an alley.
5. Driverless Robotaxi hit guardrail entering alley trying to navigate between obstacles.
6. Driverless Robotaxi hit chain blocking parking lot entrance at 3 mph.
7. Driverless Robotaxi hit chain with octagonal sign blocking parking lot exit at 4 mph.
8. Driverless Robotaxi with right of way (no stop sign) hit by cross-traffic pickup that stopped for stop sign then errantly proceeded into intersection. Robotaxi slowed from 27 mph to 3 mph and steered to side before impact.
9. Stopped driverless Robotaxi rear-ended at intersection.
10. Stopped driverless Robotaxi rear-ended at intersection.

MOIA's 4 VW "ID.Buzz" collision involvements reported no injuries. All had safety drivers.

1. MOIA hit curb at 7 mph making right turn in Beverly Hills, CA.
2. MOIA hit curb at 10 mph making left turn in Orlando, FL.
3. MOIA hit curb at 11 mph making right turn in Austin, TX.
4. MOIA hit curb at 9 mph making left turn. in Orlando, FL.

Motional's 3 Hyundai "Ioniq 5" crash involvements in Las Vegas had 2 with minor injuries. All had safety drivers.

1. Motional hit curb and palm at 4 mph trying "S curve" around stopped delivery truck.
2. Motional turning right hit e-bike that was behind Motional then tried passing on right, causing minor injury.
3. Safety driver took over to slow Motional due to sudden braking of lead vehicle, but was rear-ended at it slowed to 5 mph.

Avride's 2 Hyundai "Ioniq 5" crash involvements in Dallas had no reported injuries. Both had safety drivers.

1. Stopped Avride rear-ended at intersection.
2. Stopped Avride rear-ended mid-lane-change at intersection.

Gatik AI's 2 Isuzu FTR box truck crash involvements in Arizona had no reported injuries. Both crashes had both in-vehicle and remote supervision.

1. Gatik "lawfully" within its lane "struck by a tree branch" protruding into the lane.
2. Gatik "lawfully" within its lane made contact between sideview mirror and a street sign mounted in the median.

May Mobility's 2 crash involvements had 1 reported minor injury. Both had safety drivers.

1. Stopped May Mobility Sienna rear-ended at intersection.
2. Stopped May Mobility Sienna rear-ended at intersection.

Waabi is testing Peterbilt tractor-trailers in Texas. Waabi's 2 crash involvements had 1 with minor injuries reported. Both had safety drivers.

1. Waabi's trailer struck by pickup weaving in and out of its lane. Waabi was traveling 57 mph.
2. Waabi's trailer struck by oversized load that attempted to pass the Waabi. Waabi was traveling 60 mph. Control had recently been taken over by safety driver for an unrelated reason. Both the Waabi safety driver and test engineer sustained injuries.

Aurora's Peterbilt tractor-trailer was slowing at a border checkpoint in New Mexico, and 2 seconds after its safety driver took over, it was rear-ended while traveling 34 mph by a tractor-trailer, resulting in minor injuries.

TORC is testing Freightliner tractor-trailers in Texas with safety drivers. One was making a wide left turn in a U-turn lane, when a car drove onto the striped shoulder to try to illegally pass it on the left, and the Freightliner hit it at 11 mph. No injuries were reported.

L2++ system arriving by 2030, using Nvidia's Hyperion platform + Alpamayo software

Sounds similar to the early days of FSD Beta...

Can't say I didn't try to warn them...

https://www.reddit.com/r/uberdrivers/s/qPSjld44OX

Almost every comment and vote is that AVs are a joke, riders don't want them, and they are unsafe, and not profitable...lol

Heads in the sand sadly.

Some sightings of several Lucid-Nuro-Uber engineering vehicles in Mountain View around the Nuro HQ.

A few days ago, rumors were circulating that VW was going to kick Mobileye out of their self-driving unit MOIA and bring British maker Wayve into their efforts. Well, now we got the first footage of a MOIA vehicle with Wayve technology on board - see the zoom to the logo.

Spotted by Fabian Vihl on Autobahn A8 at a rest station.

Saw one of the Cybercab test cars running around the twin cities MN metro the other day (looked like it was doing validation loops near 35W and 494). It’s exciting to see them testing out here instead of just cruising around sunny Austin or California, but winter in the Upper Midwest is about to throw the ultimate edge cases at that perception stack.

There’s that famous observation about how Inuit languages have dozens of distinct words for different forms of snow and ice. To an autonomous neural network, that's not trivia—it's an engineering requirement. "Snow" can never be a single class label:
Black ice & the bridge problem: Powder, slush, packed granular snow, and sheer glaze look wildly different, and their friction coefficient can swing from 0.85 down to 0.15 in a 15-foot stretch. Cameras can spot white snowdrifts easily, but harmless wet asphalt and black ice look virtually indistinguishable under flat, overcast skies.
Bridges freeze first because sub-freezing air flowing under and over the deck strips convective heat hours before the ground-insulated roadway drops. A camera can’t visually spot that drop in grip; the stack has to infer it in milliseconds from high-frequency motor torque and wheel-speed delta before the friction circle collapses.
Rut-following vs. legal lanes: Once we get an unplowed 3–4 inches, road infrastructure ceases to exist. Painted lane lines, rumble strips, and curbs disappear. Humans don’t follow legal lanes during a storm anyway—we follow the dirty ruts carved out by plows and semi-trucks, which usually straddle lane boundaries. If a planner forces map-defined lane centering, it steers straight into a high-drag snowbank. Plus, windblown snowdrifts create "phantom curbs" that confuse occupancy networks.
Regen dynamics on slick patches: Single-pedal driving is second nature on dry pavement, but aggressive regen on an unexpected ice patch can snap the driven axle loose before traditional stability control even catches up. Blending instantaneous motor back-torque with mechanical ABS when friction drops off a cliff is a massive controls problem.
Collecting edge-case data in California and Texas is easy mode. If an autonomous stack can navigate an unplowed, brine-slicked Minneapolis interchange at rush hour in January with zero interventions, it can drive anywhere on Earth.
Curious how you guys think they'll tackle the lens cleaning side of things long term.

Has English subtitles. Pretty impressive and shows the advantages of having 4 different sensor types vs a camera only approach.

Tesla is flexing its manufacturing ability - 300 cars in a matter of weeks, matching Waymos numbers in Austin.

It's going to be really interesting to see how this race plays out. Waymo has a more advanced tech stack and a lot more experience operating a ride hail product, while Tesla can scale manufacturing really quickly.

The Waymo Insights team is looking for vehicle testers in all of our launched and upcoming markets. The next round of testing will be the first week of December and we require availability for 1-2 days of 6-hour tests. Testers will ride around in Waymos and log realtime feedback on the quality of their experience. This is a great opportunity for early-mid career UXRs, product testers, and analytics professionals. If you're interested please take a look at the Google form and apply.

This is a bit seperate but I feel like an almost bigger missed opportunity for Tesla was not using lidar for the millions of vehicles on the road for ground-truthing instead of the few calibration vehicles that it currently operates. The amount of detailed data it would generate would be staggering.

This is a bit different from the usual discussion in this sub. Which I think is a good thing.

Wondering if there were any "off-the-shelf" sensing products that I, as a regular consumer, not a company employee or anything special, can buy.

I drive a lot at night, and I already use a Comma, but, I'm interested in layering additional FCW into my car.

I've looked into the Livox HAP unit and mounting it on my roof and plugging it into a laptop and having Astra spin up something that could monitor the lidar returns.

I've also looked into salvaged radars from cars, even salvaged Valeo Scala lidars from Audi A8s and stuff.

Wondering if anyone could speak authoritatively on what's available and what's easy to plug into a regular laptop. I think the main limitation with salvaged components off cars is communicating with those sensors.

I keep being approached by students doing their bachelor or master thesis and always coming with the same kind of topics on AV: one is around "How would people react to/trust AVs?" or "How to fool AVs". Both are valid topics, but not for the 100th time, and especially not, when your work is just a survey with people who've never experienced AVs.

What topics for academic research on AVs are some that you would like to see?

Here are some examples that I would like to see:

• Modeling the effect of shared AVs on parking and space use in cities.
• How would shared AVs integrated in and influence public transport, when considering you can have from one- to 20-seaters as AVs available?
• How often would shared AVs have to be replaced?
• New traffic rules for AVs, including delivery vehicles (Who has priority in narrow spaces?)
• How much road infrastructure such as signs and traffic lights could be removed and money saved from streets with a fully autonomous traffic?
• ...

Researchers at Chemnitz University of Technology studied how pedestrians responded to an automated shuttle approaching at different speeds, operating in automated, manual and tele-operated modes.

Initial findings showed that operating mode had little effect on participants’ reported experience. Higher speeds slightly reduced perceived safety and comfort.

A separate study varied the shuttle’s braking profile and final stopping position. Both influenced how pedestrians perceived the interaction, suggesting that people respond to how a vehicle slows down and where it stops, beyond simply whether it yields.

These are preliminary findings from a campus shuttle setting, with further testing needed across other vehicles and road scenarios.