Just some history on Waymo’s rollout and promises when comparing to other AV companies. Waymo way over promised and under delivered.

However, no need to trash Waymo…it’s expected, this is a very hard problem…if this problem is solved by anyone in 10-20 years total, that is incredible.

Just context so we can be informed and judge all AV companies similarly…especially in this subreddit.

Just want to find some info about this topic

China is the country most prepared for self-driving cars, according to a new report.

The new study by the B2B car company eCarsTrade analyzed robotaxi deployment, road infrastructure, legal frameworks, and public sentiment across 15 countries to see which nations are actually putting driverless vehicles on the road.

Since July 2021, Waymo has been involved in a total of 4 accidents that resulted in "serious injuries that required hospitalization or emergency treatment" and 1 that involved a fatality of a human and an animal, according to NHTSA data.

Based on the reports' descriptions, it's very clear that Waymo was not at fault for any of these.

(Note: There's also some other incidents that involved animal injuries or deaths, but NHTSA categorizes the severity of incidents based on human injuries only.)

SUV rear-ends stopped vehicle behind stopped Waymo at high speed, one passenger in the human-driven car and animal declared dead (Jan 2025, San Francisco, CA)

On January [XXX], 2025 at 6:07 PM PT a Waymo Autonomous Vehicle (Waymo AV) operating in San Francisco, CA was in a collision involving a passenger vehicle near the intersection of [XXX] and [XXX].

The Waymo AV, which had no occupants, was traveling northwestbound in the middle of three lanes on [XXX] and came to a stop in a queue of traffic. Shortly after, a passenger vehicle came to a stop behind the Waymo AV. While the Waymo AV and the other passenger vehicle were stopped, an SUV approached from behind at an extreme rate of speed and made contact with the passenger vehicle behind the Waymo AV, which then made contact with the rear bumper of the Waymo AV. According to the San Francisco Police Department, the Waymo AV then began to rotate clockwise and, as it was rotating, the front of the SUV made contact with the passenger side of the Waymo AV. The rear of the Waymo AV then made contact with the rear driver side corner of another passenger car that had just begun to proceed straight on northbound [XXX]. According to the San Francisco Police Department, at least two other vehicles were involved in the crash and one of the occupants of the vehicles involved in the crash and a domestic animal were declared deceased at the scene. At the time of the impact, the Waymo AVs Level 4 ADS was engaged in autonomous mode. The Waymo AV, the passenger vehicle behind the Waymo AV, and the SUV sustained severe damage. The extent of the damage to the other three vehicles is currently unknown. Waymo received notice that five passengers in four of the vehicles involved sustained injuries of varying severity.

SUV departs roadway, hits fire hydrant, utility bollard, and street light, then reenters road in front of Waymo (Feb 2025, Phoenix, AZ)

On February [XXX], 2025 at 3:39 AM MT a Waymo Autonomous Vehicle (""Waymo AV"") operating in Chandler, Arizona was in a collision involving an SUV on [XXX].

The Waymo AV was traveling northbound on [XXX] in the left lane towards the intersection of [XXX]. An SUV traveling northbound in the right lane passed the Waymo AV on the right and continued into the dedicated right turn lane as it approached [XXX] Street. The SUV crossed into the intersection with W. Flint Street from the dedicated right turn lane and continued traveling straight onto the far-side sidewalk. The SUV then departed the roadway, striking a fire hydrant and a utility bollard before making contact with a street light. The impact with the street light resulted in the SUV coming to a stop and re-entering the roadway on [XXX] in the Waymo AV's path of travel. The rear side of the SUV made contact with the front passenger side corner of the Waymo AV. At the time of the impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode. Both vehicles sustained damage. The driver of the SUV was transported to a local hospital for treatment. One of the two passengers in the Waymo AV reported minor injuries but refused medical treatment. Both passengers, who had been seated in the rear of the Waymo AV, were not belted at the time of the collision, having had buckled their belts behind them.

A human-driven car crossed a double yellow line and hit an SUV, causing the SUV to hit the Waymo (Oct-2024, San Francisco, CA)

On October [XXX], 2024 at 8:52 AM PT a Waymo Autonomous Vehicle (""Waymo AV"") operating in San Francisco, California was in a collision involving a SUV on [XXX] at [XXX].

The Waymo AV came to a stop in a queue of traffic for a red traffic light in the rightmost lane of the two eastbound lanes on [XXX] at the intersection with [XXX]. A passenger car traveling west on [XXX] crossed the double yellow line and made contact with an SUV that was alongside the Waymo AV in the left lane of eastbound [XXX]. The impact caused the passenger side of the SUV to make contact with the driver side of the Waymo AV. At the time of the impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode. All three vehicles sustained damage.

Human rear-ends Waymo at high speed (May 2024, Los Angeles, CA)

On May [XXX], 2024 at 12:58 AM PT a Waymo Autonomous Vehicle (""Waymo AV"") operating in Los Angeles, CA was in a collision involving a passenger car on eastbound [XXX] between [XXX] and [XXX].

The Waymo AV was traveling with a test driver present behind a box truck in the number 3 lane of eastbound [XXX] near the [XXX] when a passenger car traveling at a high rate of speed approached the Waymo AV from behind. The passenger car partially entered the number 2 lane as the front right corner of the passenger car made contact with the rear left corner of the Waymo AV. The passenger car then made contact with the center median and came to a stop. The Waymo AV was transitioned into manual mode, and the test driver stopped the Waymo AV to the right-hand shoulder. At the time of the impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode, and a test driver was present (in the driver's seating position). Both vehicles sustained damage.

Waymo enters intersection after light turns green, human driven-car runs red light and hits Waymo then hits pedestrians (Nov 2023, San Francisco, CA)

On November [XXX], 2023 at 10:43 PM PT a Waymo Autonomous Vehicle (Waymo AV) operating in San Francisco, California was in a collision involving a passenger car on [XXX] at [XXX].

The Waymo AV was traveling northbound in the left lane on [XXX] and stopped at a red light at the intersection with [XXX] alongside a passenger vehicle in the right lane. After the light turned green, both the Waymo AV and the adjacent passenger car proceeded into the intersection. While in the intersection, a passenger car traveling west on [XXX] ran the red light and the front left corner and left side of this vehicle made contact with the front right of the Waymo AV and the front of the adjacent passenger car. After impact, the vehicle that ran the red light struck pedestrians that had been standing on the sidewalk on the northwest corner of the intersection. At the time of the impact, the Waymo AVs Level 4 ADS was engaged in autonomous mode. All three vehicles sustained damage and were towed from the scene.

When California allowed Autonomous Vehicle testing on their roads, they required quarterly data reports right from the start in 2018. I plotted some of data of the last 12 months, in which only Waymo is still testing under this program.

1. Waymo has scaled up Passenger Miles Traveled (PMT) over 3 fold to 4.75 million monthly.
2. To enable that, their Vehicle Miles Traveled (VMT) has also increased over 3 fold, to 6.57 million.

This means their cars drive more than a mile to transport one passenger a mile. But why exactly? It's a combination of:

1. About 70% of their paid trips are ordered by a single passenger.
   
2. About 4.6% of their trips in cancelled, most often by the passenger.
   
3. ~56% of vehicle miles travelled contains one or more passengers, ~44% of miles the AVs are empty.

Together, this results in an average occupancy of ~0.75 for Waymo in California. Noticeably, this number has been quite stable through the PMT and VMT scale-up. For comparison, in the Netherlands the average occupancy is currently ~1.3. This means a Waymo currently needs about ~75% more vehicle miles to deliver the same number of passenger miles as regular cars currently do.

Dive into the data yourself: https://www.cpuc.ca.gov/regulatory-services/licensing/transportation-licensing-and-analysis-branch/autonomous-vehicle-programs/quarterly-reporting

The report also says "System malfunctions are rare; hardware or software failures accounted for less than 2% of incidents where the autonomous vehicle was found to be at fault."

We'll see what happens once they're on the highways and there's more of them on the roads.

Where does a driverless robotaxi go between rides? A new study — Staging at the Curb: Evaluating the impacts of shared automated vehicle fleet operations under curb usage restrictions — finds that letting AVs consume curb space might actually be the optimal outcome.

Researchers modeled 1,700 shared automated vehicles serving 68,000 daily San Francisco trips. Tying together Autofleet’s commercial fleet simulator, synthetic ridehail demand from Replica and curb availability data from INRIX, they compared several operating models: continuous circling, staging only at free non-residential curbs, access to all legally available curbs and overnight staging.

Long time interest in tech - in fact, I was a tech journalist for a couple years, with a focus on certain types of autonomy.

I have studied NVDA and their system and it seems very impressive. I know a number of companies are partnering with them, but it sounds like Mercedes is the first to actually release a car using their lower level of system (so-called L2++ or whatever)...

Quite often on Reddit, someone will come out of the woodwork and say "yes, source: I am an airliner painter" - for example, when that subject is discussed. Same with other trades.

So I am wondering if anyone has deep level knowledge regarding this first Mercedes and the NVDA system? I have seen the videos and read the marketing, but it doesn't seem to address some questions...like

Is this vehicle using most all of the computing power on-board to achieve its function?

I know the NVDA system is designed for OTA updates, but without understanding the unused computing power (if in fact, there is any), it's hard to picture what the vehicle might be able to do as the system progresses. I am certain that even software-only tweaks could still upgrade it, but still...it would be great to know about the potential of the hardware and sensors.....not only as-is, but can they be easily upgraded? That would depend on where the weakest link in the chain was, etc.......

So, does anyone have more of the inside scoop on these systems?
TIA.

I’m starting to get genuinely concerned that a massive chunk of the AV industry is betting the future of Level 5 autonomy on a fundamentally flawed architecture.

Right now, the hype is entirely focused on scaling probabilistic, end-to-end deep learning. We are basically training models to act like autoregressive text generators, but instead of guessing the next word, they are guessing the most statistically likely steering angle and acceleration based on massive datasets of human driving.

But here is the brutal reality: driving a 4,000-pound piece of metal at 65 mph cannot be treated as a statistical guessing game. When a pure probabilistic model encounters a bizarre, out-of-distribution edge case, it hallucinates. And in this industry, a hallucination means a fatal crash.

If we ever want regulators and the public to trust true L5 systems, the architecture has to shift from "guessing" to "proving". I've been reading up on the push away from autoregressive networks toward constraint-solving architectures, specifically Energy-Based Models. The philosophy makes infinitely more sense for robotics: instead of just blindly outputting a predicted path, the model searches for a state that mathematically satisfies strict, non-negotiable constraints (e.g , physical boundaries, stopping distance, zero-collision vectors).

It treats safety as a rigid mathematical rule, not just a high probability.

Are we eventually going to hit an asymptotic wall with current end-to-end neural nets where they simply can't solve the long tail of edge cases? Do you think the major players (Waymo, Cruise, Tesla) will be forced to pivot to constraint-solving/EBM architectures to finally cross the L5 finish line?

I asked this question to a panel on self-driving and the response was, “yes”, unanimously, “to the extent possible”. But, to what extent is it possible?

The idea:

An “exam” consisting of virtual driving scenarios that can be run in simulation and which complements existing safety and testing requirements. An automated driver should demonstrate good performance on this test to be considered ready to be deployed in the wild.

On the technical details:

Closed-loop, neural simulators are getting good to the point that we could start to leverage them for sensor simulation of various driving scenarios. This would allow us to generate the inputs across various sensor configurations, enabling standardization of the scene despite differences between generations of AV technology.

Researchers are also working on the sourcing of scenarios from various video and textual databases of accidents. Risks that occur in the real world can be added to the collection of tests in the hopes of preventing their reoccurrence in the future.

There are also various scenario generation and analysis methods for automatically processing and producing interesting, safety-critical scenarios.

Scenario languages for expressing the behaviors of the various traffic participants in the scene exist as well, although they are at varying stages of development (for example, it is not apparent that ASAM OpenSCENARIO 2.x has an official model of execution, but it appears people are working on it).

Transferring to the real world:

That said, I’m sure there’s lots of policy and social or economic factors at play that would hold this kind of idea back.

My naive thought is that more broadly, this could be a great opportunity for the autonomous vehicle industry to serve as a role model for how society as a whole should regulate and govern emerging technologies. For example, building this third-party infrastructure between policy makers, citizens, and AV developers can encourage and borrow from responsible approaches to AI safety.

What are your thoughts?

“The industry is teaching vehicles to understand the world around them,” writes Amir Hever, CEO and Co-founder of UVeye, “but even the right driving decision can fail when it is based on the wrong assumptions about the machine carrying it out.”

The video's audio is Mandarin, but includes English subtitles.

A long interview with Trent Victor on his background, how Waymo measures safety, their ethos, and some of their next moves.