How Tesla's Autopilot team moved away from a rules-based approach for the upcoming FSD 12 to a “neural network planner” trained on 10M+ Tesla car video clips
Tesla’s autonomy effort has repeatedly been framed against ambitious delivery timelines, with earlier coverage describing shifting self-driving targets and engineering concerns around its autonomous-driving push. The FSD 12 change is therefore consequential as a change in the technical route, not merely a feature update.
The move also arrives amid lawsuits over Tesla’s self-driving marketing, making the distinction between a new development methodology and demonstrated autonomous capability especially important.
First-order effects
Tesla’s Autopilot organization must shift planning work from explicitly written driving rules toward training, testing and iterating a model on its large video corpus.
FSD 12’s progress becomes more dependent on data quality, model evaluation and edge-case validation than on expanding hand-coded planner logic.
Second-order effects
Tesla’s fleet-video collection becomes a more central input to its autonomy strategy, raising the strategic value of its integrated vehicle, data and software stack.
The approach increases the burden on Tesla to show that model behavior is robust across rare and safety-critical driving situations; a methodological shift alone does not resolve existing scrutiny of FSD claims.
Third-order effects
If neural planners prove dependable, autonomous-driving development could shift further from rules engineering toward data-and-evaluation operations, favoring companies able to connect deployed vehicles with rapid model iteration.
The same shift may make independent safety assessment harder, because learned planning behavior is less directly traceable than explicit rules; that would heighten the importance of measurable validation standards.
The trend: This is one data point in the migration of autonomy stacks from modular, hand-authored logic toward data-trained end-to-end driving systems.
@Techmeme — Any indication on what effect this major change in approach will have on when Musk believes FSD will be fully ready for self control. — I'm laughing as I type this. — “Self control.” — Not rules but study of billions of driving views that somehow get built and…
“Here's what happens when we move from rules-based to network-path-based. The car will never get into a collision if you turn this thing on, even in unstructured environments.” — Never say never. [embedded post]
Interesting insights into FSD12 @Tesla and @elonmusk by @WalterIsaacson. Is miles/intervention the right KPI? Why not weight by severity of safety risk? Traffic laws are not hard-coded? Discuss! https://www.cnbc.com/...
Elon Musk's new AI-powered self-driving system for Tesla cars is based on a radical new concept that he believes will not only totally transform autonomous vehicles but also be a quantum leap toward artificial general... #AI #Selfdrivingcars #Tesla https://www.cnbc.com/...
Check out this intriguing excerpt from Walter Isaacson's new biography of Elon Musk, exploring the fascinating world of AI for cars. Discover how Tesla's latest version of FSD has learned to drive by analyzing billions of video frames. Read more here: https://www.cnbc.com/...
Seems like good progress; but rather than let AI get away with violating the same rules as human drivers (rolling stops at stop signs) seems like there's a chance to really improve traffic safety
Elon to his auto pilot engineer: We should do a James Bond-style demonstration for V12 where there are bombs exploding on all sides and a UFO is falling from the sky while the car speeds through without hitting anything https://www.cnbc.com/...
Interesting detail about Tesla's switch to end-to-end neural network for FSD v12: “Musk latched on to a key fact the team had discovered: The neural network did not work well until it had been trained on at least a million video clips.” * Simply put, the neural network needed a..…