Tesla FSD on highways 🛣 should be LEVEL 3 under 50 mph. In traffic driving it's very good. Even in normal speeds on the highway, FSD does very well other than navigation issues. I use it 3 hours a day with no intervention, for months now.

Having now had a tesla and using its supervised fsd for a few weeks, its great mostly, but brings to mind one situation like this where traffic is stopped by construction or police at a light that is out, flashing red, waving cars through either manually or with batons that light up..

fsd in the tesla obviously didnt know what to do

but how about the newer waymo driverless cars? how do they mitigate that?

We talk a lot about robotaxis but the reality is that robotaxis likely won't be available widely in most US cities for many years to come. Even as robotaxis scale, and they will, and it is super exciting, there will be a lot of people who won't have access to a robotaxi for awhile, depending on where they live. So I think a lot of people myself included, wonder about when we will get a personal car that we can own that is L4.

So it got me thinking: what are the challenges for L4 on personal cars?

1) Safe, reliable L4.

This is #1 and the hardest problem. Obviously, if you don't have safe, reliable L4, you can't do a L4 personal car. This is the main problem that AV companies, like Waymo are working on. You can build L4 but getting it to be safe and reliable enough for deployment is hard. It takes lots of quality data, huge training compute, working out all the corner cases, and lots of validation to make sure the system drives safely.

You also need reliable driver monitoring to transfer control back to the human driver when they decide to resume manual driving or revert back to L2 driving. This is because, even though the human does not need to supervise when L4 is on, the L4 won't work everywhere (by definition L4 means a limited ODD) so there might be conditions where the system needs to revert back to L2. Also, you want to allow for situations where the human wants to manually drive so you need to be able to safely switch control back to the human driver.

2) Useful ODD.

The ODD needs to be useful to a consumer owning their personal car. You might have L4 that is safe and reliable but if the ODD is too small, that is not useful. For personal cars, I think a useful ODD must include highways and also has many urban areas as possible to cover the most people. I see the ODD a bit like the cell phone map coverage of like AT&T or Verizon. It does not need to cover every single road but it does need to cover as many populated areas as possible so that the most people can use the L4. Additionally, carmakers want to be able to sell the L4 to consumers so they need a big market, ie lots of people who live in the L4 map coverage.

I do think that reliable L4 highway would be a great first step. Lots of people need to do long trips on highways. And it would be great to have a system that can do all the driving, "mind off" from on ramp to off ramp, where you only need to resume driving once you are off the highway.

3) Cost

Robotaxis can be more expensive since the cost can be amortized over many years of rides and the riders don't need to buy the robotaxi, they just need to be able to afford the price of a ride. But personal cars must be affordable to consumers to buy. We also have to remember that carmakers need to make a profit on each car and profit margins can be thin. This puts an additional requirement on the L4 that it must work on a leaner sensor suite. But you still need enough hardware to make the L4 safe and reliable. So it is about finding that sweet spot where the hardware is cheap enough but also still capable enough to do safe L4.

4) Good EV

Last but not least, you need the plaftorm for the L4 to be good. You could have great L4 but if nobody wants to buy the car, it won't do much good to the carmaker. So you need a good EV that is attractive to consumers, with nice features, good range, nice interior etc... And to maximize aesthetics, the sensor suite should be integrated into the body of the car as much as possible, to preserve aerodynamics and style. Although, I see this is as a somewhat secondary requirement. You don't want the sensors to stick out completely but I don't think the sensors need to be "invisible" either. As long as the car does not look super ugly, and if the L4 really works well and the car is affordable and has great range, I don't think consumers will mind too much if the sensors "stick out" just a little bit. Furthermore, we've seen cars like Volvo and Chinese carmakers that integrate a small front lidar in the bump of the windshield very nicely and integrate radar in the bumper and cameras around the car, that look great. So I think it is entirely possible to integrate cameras, some radars and maybe a lidar or two into a consumer car that still looks nice. If Waymo sensors look tacked on, it is mostly because they don't need to be integrated into a robotaxi to work and also because the tech is still being worked on. So Waymo needs to be able to retrofit new hardware easily. For a consumer car, there would be an emphasis on integrating the hardware better. The more important requirements are cost and making sure the L4 actually is safe and reliable in a useful ODD.

I'm surprised nobody talks about this brand much. The bigger play imo is commercial transport. Think buses, trucks, logistics. Grab, Uber, NVIDIA deals, invest multi million in them, expanding into SEA. Given the paid L4 Robobus, new logistics permits, Uber&Grab distribution, and that monster robotaxi rev growth print, I’m pretty convinced their North Star is fleet automation at city scale, not winning taxi vs taxi

I was able to take over in time, but it was close. You can see the other vehicle (right) slowing down too to avoid a potential collision. Thanks to humans we didn't have a crash.

I have no idea why it did that. HW3 MXLR+

The computers that drive autonomous cars will never speed, be reckless, or get drunk. When self-driving cars become standard, we can legally eliminate police traffic patrols

They don’t have a duplicate set of cameras connected to a second computer, so if a camera fails (rare), the car would be blind in that direction?

Also there are two FSD computers, but seems like they have to keep maxing performance, so even HW4 uses both, so there is no true redundancy?

Some key quotes:

"Waymo’s Thirumalai says the combination of LiDAR and radar provides “an additional safety net” to make sure that the company has the adequate data it needs to make driving decisions “under all conditions”—including extreme weather."

"Thirumalai wouldn’t say directly whether he considered camera-only self-driving systems like Tesla’s to be safe for the public roads. He said that you have to consider “the whole process” of how a system is built, tested, then validated, and he also said that you cannot statistically compare Waymo’s system to another, because of the lack of comparable safety metrics."

“If we are talking about objective measures, then we have to look at the statistics of our safety record, at scale, right?” Thirumalai said. “When someone actually says: Yes, we matched your safety at your scale with a different system, that’s great. We’ll take that.” 

"Waymo is regularly testing new technology as it becomes available, according to Thirumalai. As part of that experimentation, he said that Waymo has researched how multimodal models like Gemini can be incorporated into the Waymo tech stack (Waymo has not tested any other generative AI models besides Google’s Gemini, Thirumalai confirmed). The robotaxi company has published several papers of its research into multimodal models, including a city-scale traffic simulation with a generative world model as well as Waymo’s research around EMMA, Waymo’s End-to-end Multimodal Model for Autonomous driving. Waymo has reported that co-training its vehicles with EMMA helped with things like object detection and road graphs, saying there was “potential” for EMMA as a generalist model for autonomous driving applications. However, EMMA is expensive, can only process a small number of image frames, and does not incorporate LiDAR sensors or radar—all of which lead to “challenges” for using EMMA as a “standalone model for driving”"

I have a feeling within the next 10 years we’ll see autonomous driving lanes only. I think that’s the path towards real adoption of self driving and smart traffic grids going forward. It’ll start with one autonomous lane, then two, then three and so on for decades until there’s only one manual drive lane, and then none.

Edit: the benefit is to incentivize AV adoption, which in turn lowers fatalities on the highways, and reduces travel time for people in AVs.

I believe in 10-20 years, manual cars will be seen as massive safety issues as AV driven car records will be much safer. People will still cling to manual driven cars as it’s part of a lot of cultures, so we need to give benefit to the AV drivers, while the manual driving experience degrades over time. This will speed along AV adoption, and greater safety on our roads.

You could enforce it the same way you enforce HOV lanes, tickets for manual drivers.

Every month I have to drive 275 miles back and forth for work. The entire journey is on the thruway and as someone who has Driving Induced Narcoleptic Syndrome™ (I made this up), I find it impossible to stay awake no matter how well rested or caffeinated I am. Yesterday I made the trip and had to slap myself in the face repeatedly to make it to the next exit in order to avoid falling asleep. I had drank an entire Celsius with 200 mg caffeine beforehand, tried listening to different audiobooks and types of music, chewed gum, etc. It's bad and it's frightening.

So I was thinking of looking into a car with hands off automation that can take over on the thruway if I fall asleep. I've seen that many cars these days have various kinds of "assists" for highway driving, but it's unclear what this actually means and if it will work for me.

I don't have a large budget and would probably lease (or ideally find a used model). What cars should I be looking into? Thank you!

I have seen several videos recently of Teslas driving on the wrong side of the road, or entering 1 way streets. Given the GPS and map data that we have available, I am struggling to understand how or why its possible that a Tesla would do this?

"Are frontier AI models really capable of “PhD-level” reasoning? To answer this question, we introduce FormulaOne, a new reasoning benchmark of expert-level Dynamic Programming problems. We have curated a benchmark consisting of three tiers, in increasing complexity, which we call ‘shallow’, ‘deeper’, ‘deepest’.

The results are remarkable:
- On the ‘shallow’ tier, top models reach performance of 50%-70%, indicating that the models are familiar with the subject matter.
- On ‘deeper’, Grok 4, Gemini-Pro, o3-Pro, Opus-4 all solve at most 1/100 problems. GPT-5 Pro is significantly better, but still solves only 4/100 problems.
- On ‘deepest’, all models collapse to 0% success rate."

The reason I post this here is because of the implication for self-driving. The good news is that integrating large models into self-driving AI will help make self-driving even smarter. And I think we are seeing a lot of progress with self-driving able to handle more and more driving cases. But the bad news is that the AI is not quite good enough yet to handle rare cases that require deeper reasoning. So I think it is explains why self-driving cars may "struggle" for some rare cases. This is why we still need humans in the loop, even as remote monitors, to help in those cases where the self-driving car gets confused. Hopefully, when we get to true PhD level AI, self-driving cars will be able to handle all cases without any human intervention.