The release notes for Tesla FSD Beta V11.3 have been shared online. Observers from the electric vehicle community suggest that Tesla Full Self-Driving Beta 11.3 is rolling out to the company’s employee FSD Beta testers, at least for now.
The following are Tesla’s FSD Beta V11.3 release notes:
- Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta’s multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
- Added voice drive-notes. After an intervention, you can now send Tesla an anonymous voice message describing your experience to help improve Autopilot.
- Expanded Automatic Emergency Braking (AEB) to handle vehicles that cross ego’s path. This includes cases where other vehicles run their red light or turn across ego’s path, stealing the right-of-way.
- Replay of previous collisions of this type suggests that 49% of the events would be mitigated by the new behavior. This improvement is now active in both manual driving and autopilot operation.
- Improved autopilot reaction time to red light runners and stop sign runners by 500ms, by increased reliance on object’s instantaneous kinematics along with trajectory estimates.
- Added a long-range highway lanes network to enable earlier response to blocked lanes and high curvature.
- Reduced goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X. This was achieved by improving the dataset using heavier and more robust offline optimization, increasing the size of this improved dataset by 4X, and implementing a better architecture and feature space.
- Improved occupancy network detections by oversampling on 180K challenging videos including rain reflections, road debris, and high curvature.
- Improved recall for close-by cut-in cases by 20% by adding 40k autolabeled fleet clips of this scenario to the dataset. Also improved handling of cut-in cases by improved modeling of their motion into ego’s lane, leveraging the same for smoother lateral and longitudinal control for cut-in objects.
- Added “lane guidance module and perceptual loss to the Road Edges and Lines network, improving the absolute recall of lines by 6% and the absolute recall of road edges by 7%.
- Improved overall geometry and stability of lane predictions by updating the “lane guidance” module representation with information relevant to predicting crossing and oncoming lanes.
- Improved handling through high speed and high curvature scenarios by offsetting towards inner lane lines.
- Improved lane changes, including: earlier detection and handling for simultaneous lane changes, better gap selection when approaching deadlines, better integration between speed-based and nav-based lane change decisions and more differentiation between the FSD driving profiles with respect to speed lane changes.
- Improved longitudinal control response smoothness when following lead vehicles by better modeling the possible effect of lead vehicles’ brake lights on their future speed profiles.
- Improved detection of rare objects by 18% and reduced the depth error to large trucks by 9%, primarily from migrating to more densely supervised autolabeled datasets.
- Improved semantic detections for school busses by 12% and vehicles transitioning from stationary-to-driving by 15%. This was achieved by improving dataset label accuracy and increasing dataset size by 5%.
- Improved decision making at crosswalks by leveraging neural network based ego trajectory estimation in place of approximated kinematic models.
- Improved reliability and smoothness of merge control, by deprecating legacy merge region tasks in favor of merge topologies derived from vector lanes.
- Unlocked longer fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.
Here are the V11.3 release notes again if you haven't seen them. Very happy to see improvements in rain reflections as that was rare, but could give some insane errors #FSDBeta @elonmusk pic.twitter.com/ZIOcIhmUMd
— Dirty Tesla (@DirtyTesLa) February 20, 2023
Several longtime FSD Beta testers have pointed out some key improvements that would likely be very appreciated by users in V11.3. These include the systems’ improved handling through high speed and high curvature scenarios, as well as improvements to Automatic Emergency Braking (AEB). With the improvements in place, FSD Beta V11.3 would behave closer to a proper human driver.
Comments from longtime Tesla FSD Beta testers also suggest that V11.3 is still only being released for company employees for now. Considering Tesla’s past updates, it would not be surprising if the greater FSD Beta fleet gets the V11.3 update in the coming week or so. This is, of course, unless V11.3 ends up going the way of FSD Beta V11, which was released to employees in November but not to the greater fleet of FSD Beta testers.
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Elon Musk
Tesla director pay lawsuit sees lawyer fees slashed by $100 million
The ruling leaves the case’s underlying settlement intact while significantly reducing what the plaintiffs’ attorneys will receive.
The Delaware Supreme Court has cut more than $100 million from a legal fee award tied to a shareholder lawsuit challenging compensation paid to Tesla directors between 2017 and 2020.
The ruling leaves the case’s underlying settlement intact while significantly reducing what the plaintiffs’ attorneys will receive.
Delaware Supreme Court trims legal fees
As noted in a Bloomberg Law report, the case targeted pay granted to Tesla directors, including CEO Elon Musk, Oracle founder Larry Ellison, Kimbal Musk, and Rupert Murdoch. The Delaware Chancery Court had awarded $176 million to the plaintiffs. Tesla’s board must also return stock options and forego years worth of pay.
As per Chief Justice Collins J. Seitz Jr. in an opinion for the Delaware Supreme Court’s full five-member panel, however, the decision of the Delaware Chancery Court to award $176 million to a pension fund’s law firm “erred by including in its financial benefit analysis the intrinsic value” of options being returned by Tesla’s board.
The justices then reduced the fee award from $176 million to $70.9 million. “As we measure it, $71 million reflects a reasonable fee for counsel’s efforts and does not result in a windfall,” Chief Justice Seitz wrote.
Other settlement terms still intact
The Supreme Court upheld the settlement itself, which requires Tesla’s board to return stock and options valued at up to $735 million and to forgo three years of additional compensation worth about $184 million.
Tesla argued during oral arguments that a fee award closer to $70 million would be appropriate. Interestingly enough, back in October, Justice Karen L. Valihura noted that the $176 award was $60 million more than the Delaware judiciary’s budget from the previous year. This was quite interesting as the case was “settled midstream.”
The lawsuit was brought by a pension fund on behalf of Tesla shareholders and focused exclusively on director pay during the 2017–2020 period. The case is separate from other high-profile compensation disputes involving Elon Musk.
Elon Musk
SpaceX-xAI merger discussions in advanced stage: report
The update was initially reported by Bloomberg News, which cited people reportedly familiar with the matter.
SpaceX is reportedly in advanced discussions to merge with artificial intelligence startup xAI. The talks could reportedly result in an agreement as soon as this week, though discussions remain ongoing.
The update was initially reported by Bloomberg News, which cited people reportedly familiar with the matter.
SpaceX and xAI advanced merger talks
SpaceX and xAI have reportedly informed some investors about plans to potentially combine the two privately held companies, Bloomberg’s sources claimed. Representatives for both companies did not immediately respond to requests for comment.
A merger would unite two of the world’s largest private firms. xAI raised capital at a valuation of about $200 billion in September, while SpaceX was preparing a share sale late last year that valued the rocket company at roughly $800 billion.
If completed, the merger would bring together SpaceX’s launch and satellite infrastructure with xAI’s computing and model development. This could pave the way for Musk’s vision of deploying data centers in orbit to support large-scale AI workloads.
Musk’s broader consolidation efforts
Elon Musk has increasingly linked his companies around autonomy, AI, and space-based infrastructure. SpaceX is seeking regulatory approval to launch up to one million satellites as part of its long-term plans, as per a recent filing. Such a scale could support space-based computing concepts.
SpaceX has also discussed the feasibility of a potential tie-up with electric vehicle maker Tesla, Bloomberg previously reported. SpaceX has reportedly been preparing for a possible initial public offering (IPO) as well, which could value the company at up to $1.5 trillion. No timeline for SpaceX’s reported IPO plans have been announced yet, however.
News
Tesla already has a complete Robotaxi model, and it doesn’t depend on passenger count
That scenario was discussed during the company’s Q4 and FY 2025 earnings call, when executives explained why the majority of Robotaxi rides will only involve one or two people.
Tesla already has the pieces in place for a full Robotaxi service that works regardless of passenger count, even if the backbone of the program is a small autonomous two-seater.
That scenario was discussed during the company’s Q4 and FY 2025 earnings call, when executives explained why the majority of Robotaxi rides will only involve one or two people.
Two-seat Cybercabs make perfect sense
During the Q&A portion of the call, Tesla Vice President of Vehicle Engineering Lars Moravy pointed out that more than 90% of vehicle miles traveled today involve two or fewer passengers. This, the executive noted, directly informed the design of the Cybercab.
“Autonomy and Cybercab are going to change the global market size and mix quite significantly. I think that’s quite obvious. General transportation is going to be better served by autonomy as it will be safer and cheaper. Over 90% of vehicle miles traveled are with two or fewer passengers now. This is why we designed Cybercab that way,” Moravy said.
Elon Musk expanded on the point, emphasizing that there is no fallback for Tesla’s bet on the Cybercab’s autonomous design. He reiterated that the autonomous two seater’s production is expected to start in April and noted that, over time, Tesla expects to produce far more Cybercabs than all of its other vehicles combined.
“Just to add to what Lars said there. The point that Lars made, which is that 90% of miles driven are with one or two passengers or one or two occupants, essentially, is a very important one… So this is clearly, there’s no fallback mechanism here. It’s like this car either drives itself or it does not drive… We would expect over time to make far more CyberCabs than all of our other vehicles combined. Given that 90% of distance driven or distance being distance traveled exactly, no longer driving, is one or two people,” Musk said.
Tesla’s robotaxi lineup is already here
The more interesting takeaway from the Q4 and FY 2025 earnings call is the fact that Tesla does not need the Cybercab to serve every possible passenger scenario, simply because the company already has a functional Robotaxi model that scales by vehicle type.
The Cybercab will handle the bulk of the Robotaxi network’s trips, but for groups that need three or four seats, the Model Y fills that role. For higher-end or larger-family use cases, the extended-wheelbase Model Y L could cover five or six occupants, provided that Elon Musk greenlights the vehicle for North America. And for even larger groups or commercial transport, Tesla has already unveiled the Robovan, which could seat over ten people.
Rather than forcing one vehicle to satisfy every use case, Tesla’s approach mirrors how transportation works today. Different vehicles will be used for different needs, while unifying everything under a single autonomous software and fleet platform.