12.6.3 has been flawless on highway (0 intervention) for thousand of miles.

All came to a crash after it tried to navigate in the heavy snow storm:

1. Huge challenges determining the lanes in the fully snow-covered highway.

2. Even in Chill mode, it still tried to jump to 65+ miles per hour during the heavy storm (FSD degraded message popping up). Even after I manually reduced the max speed, system automatically reset back to the max speed within a few minutes.

3. FSD is great for experience driver -> not ready anytime for senior in the winter.

No fault for FSD though, it was a tough drive: many cars crashed on the side due to black ice, highway changes to 1 lane, extremely low visibility due heavy snow.

I’m looking forward to the new 13.2 version but I’m sure it will not be able to address snow storm anytime soon.

Still lots of work to be done before it’s fully functional in the North.

So I just did a 12 hour drive that mostly consisted of highway miles and my 2024 Integra’s lane assist and dynamic cruise control didn’t do so bad, if it wasn’t for the annoying “Steering required” every 30 seconds that made me put my hand on the wheel, it would have got me about 50% of what I am looking for. Of course I didn’t trust it to the point I never wasn’t paying attention, it was mostly a science experiment for me. But it had me thinking, we can’t be too far off when it comes to highway self driving, right?

So how many years away are we from having a gas vehicle car that can legit allow me to completely take my mind off the road and read a book, play a Nintendo switch, or even nap while my car does highway driving.

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https://www.tesla.com/careers/search/job/vehicle-operator-autopilot-229531

FSD (Supervised) v13 upgrades every part of the end- to-end driving network.​Includes:​

• 36 Hz, full-resolution Al4 video inputs
• Native Al4 inputs and neural network architectures
• 4.2x data scaling
• 5x training compute scaling (enabled by the Cortex cluster)
• Reduced photon-to-control latency by 2x
• Speed Profiles on both City Streets and Highways
• Start FSD (Supervised) from Park with the touch of a button
• Integrated unpark, reverse, and park capabilities
• Improved reward predictions for collision avoidance
• Improved camera cleaning
• Redesigned controller for smoother, more accurate tracking
• Dynamic routing around road closures, which displays them along an affected route when they are detected by the fleet

Upcoming Improvements:​

• 3x model size scaling
• 3x model context length scaling
• Audio inputs for better handling of emergency vehicles
• Improved reward predictions for navigation
• Improvements to false braking and slower driving in parking lots
• Support for destination options including pulling over, parking in a spot, driveway, or garage
• Efficient representation of maps and navigation inputs
• Improved handling of camera occlusions

Source: https://i.imgur.com/jOIX38O.png

I have often seen it suggested, in this sub, that the Tesla FSD feedback is just a Placebo Button and not actually used by Tesla FSD development and research.

Ok ,but what gives you that impression tho?

The vehicle makes a shutter sound on the outside every 10 seconds exactly, regardless of motion or weather. What is it?

Mobileye unveils a framework designed to deploy safe, self-driving systems at scale building upon two key principles:

• Overall mean time between failure of the system should be at least as good as human statistics. 
• The system should eliminate unreasonable risk, where the self-driving system provider is transparent about the boundary between reasonable and unreasonable risk. 

https://www.mobileye.com/blog/the-mobileye-safety-methodology-for-fully-autonomous-driving

Here is a link to the technical paper that goes into the details: https://static.mobileye.com/website/us/corporate/files/SDS_Safety_Architecture.pdf

Luminar says Tesla is biggest customer for its lidar sensors | Reuters

Hi!

I was curious if anyone knew any good information in the public domain about how accurate self driving car object detection systems are or where that accuracy comes from.

Specifically, when you look at object detection accuracy metrics on COCO (https://paperswithcode.com/sota/object-detection-on-coco), there are still a lot of mis-categorizations & missed objects even for the state of the art.

But generally speaking, it doesn't seem like waymo or anyone like that is frequently just failing to detect cars or pedestrians. I'm curious about why & how this works.

I would guess that this is due to some combination of the following:

(1) More annotated training data

(2) multiple sensor types

(3) tracking objects across time / video frames

(4) less categories of objects to detect

(5) better algorithms?

Does anyone know how much each of these matter or if there are other things going on that I'm missing b/c I'm not in the industry?

Thanks!