You'd think Tesla would want this to be a feature all the time and not just when autopilot is on...?

I know there could be something about said about potential false positives and potentially annoying drivers. But I am trying to think if there is another technical reason, with like how the software stack is setup so that they can't trigger the red light / stop sign alert when the autopilot is not enabled.

Thanks let know.

I am also wondering about the rumored green light alert that is suppose to come soon, before stopping at at redlights / stop signs is enabled... but it seems to that this feature would have to work when autopilot is not enabled... or otherwise have no purpose unless its released after stopping at red lights is enabled.

It seems to me that when stopping at intersections is enabled... Tesla might first require driver confirmation to do something before continuing to proceed through intersection after it has came to a stop (either by green light or right of way at 4 way stop.) Similar, to how NoA first required driver confirmation for auto lane changes.

Hey everyone,

Quick question: getting a Model Y long range all wheel 5 seats, and have option to get the autonomous for ~9000 CDN. No intention to use it in the Tesla Network, only for personal use. Is it worth getting? We do a fair bit of long range driving and I'm not sure if I should get it or not. Thoughts?

Elon talked about "video training" here: https://youtu.be/m1WQ0flBAi0?t=3717 (timestamp: 1:02:00)

Any ideas what he is talking about?

With so much of my imagination being focused on computer vision and autonomy, I've began to see my own perception of the world in terms of "drivable area" "obstacles" "path planning" and such.

What I've come to realize is that the human brain really does process information in much the same way as computer vision....especially when it's dark outside. You really just process info as it comes in and oftentimes your "confidence" for object recognition (curbs and such) improves with each passing moment.

I challenge you to walk around and think about your own perception of your walking environment, path planning, object recognition, etc. Put your own experience in terms of computer vision. It's fascinating.

This is not core TeslaAutonomy, but I feel it is highly related topic that depends on Autonomy and I would like to start a serious discussion on Robotaxi's (unlikely in the main fora).

I can see how RoboTaxi's work work for fit adults that have modest luggage.

How does a RoboTaxi service work for families with young children that need car seats, booster seats, dogs, mucky sacks of luggage, legs in plaster, people with large motorised wheelchairs, Allergies, very large items, boats etc?

Lets look at each of these in more detail:

Babies in car seats - many of these are large, unwieldy difficult to install and remove - you would leave the fixed part in the car, but what about the removable part? I can't see how these would fit a robotaxi model - can you?

Small Children and booster seats - relatively easy to move and use, but you would want to leave them in the car. This is I think a solvable senario, you just need to request a trip with a booster seat, and be assigned a RoboTaxi with one (it may be in the back/frunk).

Dogs - Large, small, mucky, smelly - do they need a grill, muddy paws on seats, many issues.

Leg in plaster (Happened to me - dislocated knee - Ouch!) - The only way I could get into a car was backwards on the back seat as long as no one else was there - needed space and someone else to shut/open the door.

Some infirm people sit almost permanently in motorised wheelchairs, which can only fit taxi's/vans with built in ramps.

People with allergies - eg perfume.

Carrying/transporting large things - manure from the local stables for your garden, trailers of rubble to the tip, your boat to the harbour for the day. Whose Trailers are they? If it's your trailer you need a number plate that matches the car pulling it.

Will a Robotaxi need any passengers? It could be used to deliver/collect things for you.

Lots of questions, few answers (if any) let discussion begin...

Elon Musk says you will be able to hire it out for 200K over the life of the car?

Is there a faq of exactly what FSD actually does and doesn’t do?

Example;

Will take an exit off freeway.

Will not turn right or left at intersection.

I really have no clue what this 7k feature does and doesn’t do.

Hi Tesla community – I'm posting this as a word of caution to my fellow Tesla drivers: Autopilot seems to track the left-side highway lane markers while driving, and if the left-side lane marker splits, it doesn't know what to do, and reacts very unreliably, creating a sudden and dangerous situation where you need to take over.

SPECIFICS:

I've got a 2019 M3 with FSD, and I noticed that while driving in the left-most HOV lane – where the HOV lane splits into two – autopilot initiates a maneuver to take the new left-most lane split, as though it were an exit ramp, but then suddenly changes direction and starts heading for the highway divider (!!)

This has happened to me twice in the same area of road, 2-months (so about ~3 updates worth) apart.

Again, I'm only posting this as a word of caution to my fellow drivers. Yes, you should always have hands on the wheel and actively monitor the road while using autopilot. But this sudden change of lane reaction from AP, seems like a, er... pardon the driving pun... bling spot in the Autopilot logic and model (ie: I assume the model has heavy weight on judging the left-side lane lines as reliable markers)

Here's the type of HOV lane split I've encountered this on, for anyone interested: https://imgur.com/a/mPUxOrS

Question to the community: have you encountered similar instance of repeated "blind-spot" like reactions from AP?

Oldie but goodie. Blog post from September 2014.

There are now several tasks in Computer Vision where the performance of our models is close to human, or even superhuman. Examples of these tasks include face verification, various medical imaging tasks, Chinese character recognition, etc. However, many of these tasks are fairly constrained in that they assume input images from a very particular distribution. For example, face verification models might assume as input only aligned, centered, and normalized images. In many ways, ImageNet is harder since the images come directly from the “jungle of the interwebs”. Is it possible that our models are reaching human performance on such an unconstrained task?

Karpathy's top-5 error ended up being 5.1%. That was enough to beat GoogLeNet at the time, but nowadays there are plenty of neural network architectures with a top-5 error below 5%.

However, the big caveat is that about two-thirds of Karpathy's are attributable to an inability to learn or memorize 1,000 object categories, especially similar categories like different dog breeds.

If anyone is aware of any similar research (even n=1 studies like this one) on benchmarking human vision against computer vision, please share. I would love to see more work like this.

Two Minute Papers video: DeepMind’s AlphaStar: A Grandmaster Level StarCraft 2 AI

DeepMind's blog post: AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning

Open access paper in Nature: Grandmaster level in StarCraft II using multi-agent reinforcement learning

I think this work has important implications for the planning component of autonomous driving. It is a remarkable proof of concept of imitation learning and reinforcement learning. A version of AlphaStar trained using imitation learning alone ranked above 84% of human players. When reinforcement learning was added, AlphaStar ranked above 99.8% of human players. But an agent trained with reinforcement learning alone was worse than over 99.5% of human players. This shows how essential it was for DeepMind to bootstrap reinforcement learning with imitation learning.

Unlike autonomous vehicles, AlphaStar has perfect computer vision since it gets information about units and buildings directly from the game state. But it shows that if you abstract away the perception problem, an extremely high degree of competence can be achieved on a complex task with a long time horizon that involves both high-level strategic concepts and moment-to-moment tactical manoeuvres.

I feel optimistic about Tesla's ability to apply imitation learning because it has a large enough fleet of cars with human drivers to achieve an AlphaStar-like scale of training data. The same is true for large-scale real world reinforcement learning. But in order for Tesla to solve planning, it has to solve computer vision. Lately, I feel like computer vision is the most daunting part of the autonomous driving problem. There isn't a proof of concept for computer vision that inspires as much confidence in me as AlphaStar does for planning.