Should Tesla Buyback Stock? + FSD Beta Release Notes, Wedbush, NHTSA (05.19.22)

➤ One of Tesla’s largest shareholders advocates for stock buyback, should Tesla do it? ➤ FSD Beta 10.12 release notes leak ➤ Wedbush reduces TSLA price target ➤ California mayor discloses massive Supercharging site ➤ NHTSA investigates Tesla crash in a California ➤ Twitter execs discuss possible acquisition ➤ Bill Gates declines to comment on Tesla again Twitter: https://www.twitter.com/teslapodcast Patreon: https://www.patreon.com/tesladailypodcast Tesla Referral: https://ts.la/robert47283 FSD 10.12 Release Notes: • Upgraded decision making framework for unprotected left turns with better modeling of objects' response to ego's actions by adding more features that shape the go/no-go decision. This increases robustness to noisy measurements while being more sticky to decisions within a safety margin. The framework also leverages median safe regions when necessary to maneuver across large turns and accelerating harder through maneuvers when required to safely exit the intersection. • Improved creeping for visibility using more accurate lane geometry and higher resolution occlusion detection. • Reduced instances of attempting uncomfortable turns through better integration with object future predictions during lane selection. • Upgraded planner to rely less on lanes to enable maneuvering smoothly out of restricted space. • Increased safety of turns with crossing traffic by improving the architecture of the lanes neural network which greatly boosted recall and geometric accuracy of crossing lanes. • Improved the recall and geometric accuracy of all lane predictions by adding 180k video clips to the training set. • Reduced traffic control related false slowdowns through better integration with lane structure and improved behavior with respect to yellow lights. • Improved the geometric accuracy of road edge and line predictions by adding a mixing/coupling layer with the generalized static obstacle network. • Improved geometric accuracy and understanding of visibility by retraining the generalized static obstacle network with improved data from the autolabeler and by adding 30k more videos clips. • Improved recall of motorcycles, reduced velocity error of close-by pedestrians and bicyclists, and reduced heading error of pedestrians by adding new sim and autolabeled data to the training set. • Improved precision of the "is parked" attribute on vehicles by adding 41k clips to the training set. Solved 48% of failure cases captured by our telemetry of 10.11. • Improved detection recall of far-away crossing objects by regenerating the dataset with improved versions of the neural networks used in the autolabeler which increased data quality. • Improved offsetting behavior when maneuvering around cars with open doors. • Improved angular velocity and lane-centric velocity for non-VRU objects by upgrading it into network predicted tasks. • Improved comfort when lane changing behind vehicles with harsh deceleration by tighter integration between lead vehicles future motion estimate and planned lane change profile. • Increased reliance on network-predicted acceleration for all moving objects, previously only longitudinally relevant objects. • Updated nearby vehicle assets with visualization indicating when a vehicle has a door open. • Improved system frame rate +1.8 frames per second by removing three legacy neural networks. Executive producer Jeremy Cooke Executive producer Troy Cherasaro Executive producer Andre/Maria Kent Executive producer Jessie Chimni Executive producer Michael Pastrone Executive producer Richard Del Maestro Executive producer John Beans Music by Evan Schaeffer Disclosure: Rob Maurer is long TSLA stock & derivatives