- Wayne Allyn Roo Wayne Root - Wayne Al
7 posts

- Wayne Allyn Roo Wayne Root - Wayne Al
@AllynAl95108
Watch@ RealAmericasVoice TV Sat Noon ET/9 AM PT & daily 6 pm ET on https://t.co/T6fmKIgKjW and https://t.co/rzVLp2BZJJ. Or go to: https://t.co/vb5XfKRVto

Just saw this post on FB: “I don't know who I need to tag but Tesla... if you're reading this... get ahold of me. I am a firm believer in Jesus Christ and I first in foremost believe that He truly saved my life on Tuesday afternoon... HOWEVER, more so than ever am now a FIRM advocate for the safely of Tesla!! I was hit head on by a drunk driver who plunged straight at. He was going so fast he was ejected from his vehicle (also not wearing a safety belt), but all of my airbags deployed and my phone automatically dialed 911... I was also to walk away with swelling, bruising, and a fractured sternum. The gentleman that struck me was in ICU in a trauma center. God is so good and merciful, and I'm thankful I was in a Tesla!!!” - Adrienne Rose Alvarado — 🥹 feeling emotional. Yes, it’s only Teslas for our family as well, the safest cars on earth that drive themselves. Thank you @elonmusk and the whole @Tesla team! 🙏


New release of FSD Supervised now starting to roll out This update brings 20% faster reaction time to further increase safety, among many other improvements Full release notes below Full Self-Driving (Supervised) v14.3 includes - Upgraded the Reinforcement Learning (RL) stage of training the FSD neural network, resulting in improvements in a wide variety of driving scenarios. - Upgraded the neural network vision encoder, improving understanding in rare and low-visibility scenarios, strengthening 3D geometry understanding, and expanding traffic sign understanding. - Rewrote the AI compiler and runtime from the ground up with MLIR, resulting in 20% faster reaction time and improving model iteration speed. - Mitigated unnecessary lane biasing and minor tailgating behaviors. - Increased decisiveness of parking spot selection and maneuvering. - Improved parking location pin prediction, now shown on a map with a (P) icon. - Enhanced response to emergency vehicles, school buses, right-of-way violators, and other rare vehicles. - Improved handling of small animals by focusing RL training on harder examples and adding rewards for better proactive safety. - Improved traffic light handling at complex intersections with compound lights, curved roads, and yellow light stopping – driven by training on hard RL examples sourced from the Tesla fleet. - Improved handling for rare and unusual objects extending, hanging, or leaning into the vehicle path by sourcing infrequent events from the fleet. - Improved handling of temporary system degradations by maintaining control and automatically recovering without driver intervention, reducing unnecessary disengagements. Upcoming Improvements - Expand reasoning to all behaviors beyond destination handling. - Add pothole avoidance. - Improve driver monitoring system sensitivity with better eye gaze tracking, eye wear handling, and higher accuracy in variable lighting conditions.






