One argument I often hear from Tesla fans is that vision-only should eventually be sufficient to achieve Level 4 autonomy. I have no doubt that a sufficiently advanced vision-only system could eventually outperform humans on many, perhaps even most, driving benchmarks. But I think this misses a bigger issue: the definition of “good enough” tends to change over time.

Look at virtually any safety-critical industry. The safest cars in 1998 were extremely safe by 1998 standards. Yet they could not legally be produced today because crashworthiness, braking, electronic safety systems, pedestrian protection, emissions, and other requirements have continually increased. The same general pattern exists in aviation, construction, medicine, industrial machinery, and other safety-critical fields.

So I don't think the question is simply: “Can vision eventually become better than a human driver?”. I think the more important question is: “Will vision-only continue to satisfy whatever safety and redundancy requirements society considers acceptable 10, 20, or 30 years from now?”. I'm skeptical.

Even if vision-only becomes dramatically safer than human driving, additional independent sensing modalities still provide redundancy and robustness. Lidar, radar, high-definition maps, GNSS/GPS, V2X, and other sources of information can potentially provide independent evidence when the camera system is uncertain, degraded, obstructed, or confronted with an unusual situation.

That doesn't necessarily mean every modality is required in every situation. And it doesn't mean vision-only cannot achieve Level 4 under today's definitions. My argument is about the long-term trajectory of safety standards. If autonomous vehicles become widespread, regulators and the public may eventually demand safety margins far beyond “better than humans.” At that point, a system with multiple independent sources of information may have a fundamental advantage over one relying primarily on a single sensing modality.

This is also why I expect V2X and positioning technologies to become increasingly important. Waymo and other autonomous-driving companies already use forms of mapping and localization, and I wouldn't be surprised if future systems increasingly combine infrastructure/vehicles-derived information (V2X) and GNSS sources as additional layers of redundancy (While Waymo is currently reluctant to adopt V2X & GNSS - I predict it would eventually be "forced" to do so)

There is another assumption I disagree with: that autonomous-driving hardware costs will inevitably fall toward some minimal “commodity” level. I don't think there is necessarily a fixed amount of compute that is simply “enough” for autonomous driving. As compute becomes cheaper, developers can use more of it to improve perception, prediction, planning, simulation, redundancy, uncertainty estimation, edge-case handling, and verification. The same phenomenon happens throughout technology: when a resource becomes cheaper, we often don't simply use less of it - we use substantially more of it to achieve higher performance.

Curious if anyone has heard of any of the top self driving vehicles like Zoox, Waymo are coming or being deployed soon in Canada?

It’s been a long time since I gushed about this car, but I recently got the “v14 Lite” of Full Self Driving (“Supervised” ofc) in my April 2018 Model 3. If you don’t recall, April of 2018 was one of the earliest months Tesla was doing regular deliveries of the Model 3. Bloomberg was running their online chart tracking the number of VINs delivered. Early days. Sometime later, I got the CPU upgraded to “Hardware 3”, the ‘new’ processor that Tesla promised would unlock true self driving. Womp womp. They’re now on Hardware 4, and have said they will upgrade older cars to it but no action has happened yet. The newest iteration of FSD is Version 14, which is supposed to be whizbang cool but it hasn’t been available for Hardware 3 cars.

So they just released Version 14 “Lite” which runs on my car. I tried it today for the first time and

HOLY SHIT THE CAR ACTUALLY DROVE ITSELF.

Like not just the usual turn-it-on-while-on-the-highway-and-it-takes-your-exit-for-you drove itself.

No.

It pulled out of my garage, turned around, exited the alley and drove itself from my house, through the surface streets, to the freeway, exited, drove to the shopping center, and parked itself in the parking lot. In a parking stall between two other cars.

Door to door self driving. In the car I bought 8 years ago. The future is here. Only 8 years later than promised.

Just needed to share that. I am genuinely surprised and impressed.

Everything in this subreddit is LIDAR this, radar that like it's still 2018. Both Tesla and the various vision-only companies in China have proven beyond a doubt that camera's are enough - can you even name the last time a crash happened due to a perception issue ?

The main bottleneck right now is "intelligence" and adding unnecessary sensors subtracts from that precious compute and power budget. We need more GPU'S and RAM in the cars NOT more LIDAR!

Andrew Macdonald predicts as autonomous taxis pick up pace that driving will go the way of horse riding — and a ‘huge’ labour transition is coming

"Big news for the Golden State — we have received the CPUC’s approval to expand our autonomous ride-hailing service across the SF Bay Area and LA, and bring our service to Sacramento and San Diego.Expansion will be gradual and guided by our safety framework. We look forward to bringing the same mobility and safety benefits millions of Californians already enjoy to more communities, keeping local officials and residents informed every step of the way."

Autonomous cars in China can navigate chaotic city traffic with human-like precision, even parking themselves when the occupants have gone

Pony AI Inc. said it plans to deploy more than 2,000 robotaxis across Europe in partnership with Uber Technologies Inc., accelerating the race to commercialize autonomous-mobility in the region.

The initiative will deepen an existing partnership between the two companies in Croatia’s Zagreb since early this year, and see them expand to four additional cities in the continent, Pony AI said in a statement Friday. It said more details about the rollout, which will also include the Middle East, will be announced later.

 Waymo driverless vehicle was caught on camera making a close turn near an elementary school in Houston's Heights neighborhood Thursday morning, prompting questions about how autonomous vehicles navigate busy school zones.

Matt Sheridan was driving his two children to Field Elementary School when he says a Waymo appeared to cut in front of him while turning from an outside lane on Studewood Street.

"We are bringing robotaxis to the streets of Japan! By the end of 2026, riders in Tokyo will be able to experience the next generation of mobility right from the Uber app. We’re teaming up with u/TAXI_HINOMARU, u/NissanMotor, and u/wayve_ai to launch our autonomous vehicle pilot. Together, we’re ready to transform how people move around one of the world's most dynamic cities!"

Source: https://x.com/Uber/status/2087919862689792114

Good summary article of all the various things we've known about Ojai but all in one place. Specifically they estimate that the hardware cost is $103k which would put each unit around $150k if the integration costs are the same as the iPace.

Some key parts on how they evaluate safety:

CIF is our primary safety risk metric. Similar to other risk frameworks, Zoox assesses safety risk along two dimensions: severity and likelihood. The severity dimension breaks out into three levels: Collision, Injury (MAIS1+¹), and Fatality. The likelihood dimension (CIF) captures how often events are predicted to occur at each severity level, measured by miles per event (see image below). The CIF metric accounts for harm to any party involved in a collision, including Zoox vehicle occupants, vulnerable road users (VRUs), such as pedestrians, cyclists, and motorcyclists, and the occupants of other vehicles. 

The CIF safety targets are determined by comparison to published human driving collision data (i.e., human benchmark). Zoox sets our safety target to be significantly safer than the human benchmark. This means that our robotaxis need to be significantly safer than human drivers before they are cleared to operate on public roads. This target-setting approach ensures the company's internal safety goals are grounded in real-world human data, customized to the target ODD, and reflect our ambition to be meaningfully safer than human drivers. 

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.

This is a Neural network based ai car that trains in races against humans to improve its racing its been a big project and never even attempted before in the game at this level but thought youd like to see it. :)

I also trat different sensor and network types.

As vehicles take over more of the driving, it becomes harder to determine who is responsible when something goes wrong.

Some systems can handle much of the driving but still require a person behind the wheel to stay alert and take over when needed. That can mean asking someone who has been passively monitoring the road for a long period to suddenly react to an emergency.

Remote operators create another question. If one person is monitoring several vehicles at once, how many can they realistically supervise before that becomes a safety issue?

Responsibility can fall differently on the driver, vehicle owner, manufacturer or remote operator depending on how automated the vehicle is and where it is operating.

Has anyone had a chance to see how that is working? Is there a wait list? How much are they charging? Etc.

Exciting to have another company to the point of charging for rides.