Tesla To Automatically Close Your Windows If It Starts Raining |

Ugh. That moment you look out and see the pouring rain flowing down the window pane nearby. You bite your lip as you remember that you left the windows open on your Tesla.

It was so hot…You had vented the windows via the Tesla app to help prevent your glass roof from irradiating your interior into molten lava. Now the rain must be drenching your interior, soaking your seats and floor mats. A scramble to open the app brings a welcome surprise.

Somehow, your Tesla knew to close its own windows. Has your car developed self-aware Artificial Intelligence? No, not yet at least.

Tesla is added a new helpful function: automatic window closing with rain forecasts.

Announced on Twitter by Elon Musk on May 30th, 2022, Tesla now appears willing to tie meteorological forecasts to car actions.

While weather-based actions have been part of API services like IFTTT, this marks the first time Tesla has planned to use weather data to trigger an action taken by a car.

Tesla appears to be heavily invested in weather prediction lately, as witnessed by their use of weather data in the new range prediction models announced in update 2022.16.

The new energy usage model factors in predicted weather conditions when calculating range and energy usage.

In addition, Chinese drivers receive air quality index numbers in their status bar. However, these features do not affect the state of the car.

The ability for Tesla vehicles to automatically perform certain functions based on predictive weather certainly offers additional opportunities to use weather data.

Future use of weather data could theoretically include automatically changing a driver’s acceleration mode to ‘Chill’ when snow and ice are forecasted.

Or, a Tesla equipped with a HEPA filter could activate Bioweapon Defense mode on Orange Air Quality days to screen out pollen and other contaminants.

The possibilities for using weather data to modify car settings are seemingly endless.

Lions, Tigers, and Bears, Oh My! While Dorothy’s list of animals are not included in Elon Musk’s latest tweet regarding animal recognition by Tesla’s Neural Network, dogs, cats, and horses are included.

Speaking to Tesla’s object recognition abilities, it appears that Tesla is preparing to release an update that includes new visualizations of various animals that may stray across the car’s path.

As far as animal recognition, Teslas currently only visualize dogs, but it will often display a dog even if it's another animal.

This is due to a lack of training, where the car can't yet differentiate between a dog, a horse or a cat. It can be easily fixed by training their NN by adding and categorizing photos of addition animals.

It looks like we'll soon get new animal visualizations. The most obvious may be animals that are regularly found near streets, such as deer, horses, possums or other creatures.

While automotive competitors have made gains in developing electric vehicles after years of lagging behind Tesla, one cannot deny that Tesla’s neural networks are cutting edge.

These neural networks give Tesla the ability to gather live data from over one million participating vehicles. They utilize these vast datasets of real-world data to train their AI algorithms to identify objects that may be a hazard to drivers.

Tesla claims that "a full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train."

While users have noticed drastic improvements in the car’s ability to recognize various car types (as seen by improved visualizations in 2022.16), the likely addition of new animals is an interesting addition.

With FSD Beta 10.12, Tesla added numerous visualization updates. Will more animals be added in FSD Beta 10.13, which is expected in about two weeks?

While too early to tell, it remains exciting that the objection recognition abilities of Teslas continue to improve.

The car knows that something is there, just doesn’t know that they’re horses yet, but it will. Dogs, cats and many other animals will also be recognized.

Tesla registered a new high resolution radar unit with the U.S. Federal Communications Commission (FCC). Tesla's intended use for these radar units is unknown, but it appears they are intended for imaging, similar to how LiDAR uses lasers to map surroundings.

While Tesla's FSD software has used radar in the past, Elon Musk has a rather unfavorable stance on LiDAR. For the purposes of autonomous driving, Musk sees LiDARs as a "fool's errand." Yet, people have been spotting Tesla prototypes with LiDAR sensors since last year.

The latest instance of Tesla using LiDAR comes from Twitter user @ManuelRToronto who spotted a LiDAR-mounted Tesla in downtown Toronto, only a few weeks after the release of FSD Beta in Canada.

He took a video of the LiDAR-mounted Model Y with manufacturer license plates from California.

@elonmusk Spotted in Toronto. A @Tesla Model Y with #LiDAR and CA Manufacturing (MFG) Plates. It cut me off!, I guess it doesn’t have FSD or it may be camera shy, what’s the deal ? @NotATeslaApp pic.twitter.com/FYOdjFA1gq

There has been no official announcement from Tesla about these LiDAR-mounted vehicles, but it is safe to assume that Tesla will not use LiDAR on any production vehicles.

Tesla is likely using LiDAR to help train their machine learning algorithms, using it as the ground truth when checking for accuracy. Unlike cameras, LiDAR captures extremely accurate 3D depth measurements.

LiDAR could be used to train these algorithms to accurately interpret depth by relying on precise, non-interpretive LiDAR sensors.

Cameras are limited to 2D data combined with computer vision algorithms to interpret 3D depth. The downside to this approach is that it requires a computational process versus having precise 3D depth measurement from a LiDAR sensor.

However, LiDAR sensors are expensive; the benefit of relying solely on cameras is that it makes Teslas much more affordable.

LiDAR can't be the only sensor used in a vehicle since it can only build a wireframe 3D environment. Without cameras it wouldn't be able to read traffic signs, traffic lights or anything that doesn't have depth.

Vehicles with LiDAR also rely on camera data and fuse the two outputs of the two sensors together to build a virtual representation of the real world.

Musk believes that self-driving cars should navigate the world in the same manner as human drivers. Since humans use their eyes and brain to navigate three-dimensional space, cars with cameras and enough computational power should be able to achieve the same thing.

"Humans drive with eyes and biological neural nets, so it makes sense that cameras and silicon neural nets are the only way to achieve a generalized solution to self-driving, " says Elon Musk.

While other self-driving initiatives like Google's Waymo have taken a LiDAR approach, Tesla is outpacing the competition using vision-only, machine learning and the network effect of over 100,000 vehicles in the FSD Beta program.

Tesla's vision-only approach has become smart enough that adding radar data gives the system more information than it needs and disorients the FSD software.

LiDAR and radar may be helpful in training the FSD software, but Tesla wants to avoid using multiple sensors with potentially conflicting perceptions that would overwhelm the system.

With Tesla's recent patent of a high resolution radar, and the recent reported use of LiDAR, it is possible that Tesla's Robotaxi will employ radar and/or LiDAR in order to reach full automation.

Tesla's FSD software could eventually be segmented into consumer and commercial self-driving vehicles with consumer vehicles reaching conditional autonomy (L2/L3) using vision-only and commercial vehicles reaching full autonomy (L4/L5) with the help of radar or potentially even LiDAR sensors.

Commercial robotaxis could even be multi-sensor (cameras, radar, LiDAR), costing much more, while consumer self-driving vehicles would be vision only and more affordable.

Only very high resolution radar is relevant

View the release notes for the upcoming version 2022.16.1.1.

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