Tesla Rolls Out FSD Beta v11.4 with Significant Driving Improvements

Tesla has begun rolling out Full Self-Driving (FSD) Beta v11.4 to employees. The new update includes a number of driving improvements. This is the first FSD build on the 2023 branch, and vehicles on the 2023.2-2023.6 branches will be eligible.

Tesla has begun rolling out FSD Beta v11.4 with many thoughtful and important improvements that affect the driving experience. According to Teslascope/Twitter, this is the first FSD build on the 2023 branch, and vehicles on the 2023.2-2023.6 branches will be eligible. The update includes improvements to vehicle response to pedestrians, near VRUs, and turn performance in dense unstructured city environments. In addition, it includes an improved lane guidance module, geometric consistency between lane, line, road edge and restricted space detections, recall for partial cut-ins, and precision for false positive cut-ins due to lane changes into adjacent lanes. The update includes improvements to understanding for when to use bus lanes and when to avoid them, speed control during lane changes, and long-range path blockage detection and control on city streets.

FSD Beta v11.4 added a new Vision Speed network to infer the typical driving speed on a given road. It mitigates hydroplaning risk by making maximum allowable speed in Autopilot proportional to the severity of the detected road conditions. And it improves developer productivity with better code diagnostics and C++20 features.

Full release notes:

• Improved the decision to assert or yield for pedestrians at more crosswalks by evaluating multiple possible futures in the joint space of ego's actions and the pedestrian's response.
• Improved ego‘s behavior near VRUs by measuring their probability of intersecting ego's path, based on their kinematic data, and preemptively decelerating when the estimated risk is high.
• Improved turn performance in dense unstructured city environments. Examples of improved cases include: turning when the turn lane is blocked by parked cars and avoiding turning into bus lanes.
• Improved lane guidance module to feed in long range routing "hints" to the network for which lanes ego needs to be in to reach its destination. Also significantly improved per—lane routing type autolabeler. These changes combined resolved 64% of all interventions caused by bad routing type.
• Improved geometric consistency between lane, line, road edge and restricted space detections by re-training our networks on the same dataset with the latest version of our "lane guidance" module, and by using a common features space to predict line, road edge and restricted space.
• Improved recall for partial cut-ins by 39% and precision for false positive cut-ins due to lane changes into adjacent lanes by 66%, resulting in a 33% reduction in overall lane-changing prediction error. This was accomplished by further increasing our auto-labeled fleet dataset by 80k clips, improving the accuracy of the auto-labeling algorithm, and tuning the distribution of training supervision.
• Improved understanding for when to use bus lanes and when to avoid them, by updating the lane type detection network and improving map-vision fusion.
• Improved speed control during lane changes through better consideration of upcoming navigation deadlines, required back-to-back lane changes and presence of a vehicle behind ego.
• Added new Vision Speed network to infer the typical driving speed on a given road. This is used to limit the maximum allowed speed in environments such as parking lots and residential roads.
• Mitigated hydroplaning risk by making maximum allowable speed in Autopilot proportional to the severity of the detected road conditions. In extreme cases, Autopilot may use the wetness of the road, tire spray from other vehicles, rain intensity, tire wear estimation or other risk factors that indicate the vehicle is near the handling limit of the surface to warn the driver and reduce speed.
• Improved long-range path blockage detection and control on city streets. Ego will now be able to perform lane changes due to upcoming path blockages earlier.
• Improved developer productivity with better code diagnostics and C++20 features by upgrading compiler to clang-16. This also improved photon-to-control vehicle response latency by 2%.

The release notes can be found here. https://t.co/EGHVAIVDXX

— Teslascope (@teslascope) April 17, 2023

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