Tesla started rolling out the latest FSD Beta release version 114.4 a few hours ago to original testers in the first round.
FSD Beta v11.4.4 (firmware version 2023.7.20) release notes mention several improvements in this release. The Tesla Autopilot software team mainly focused on vulnerable road users (VRUs/pedestrians, bicycles, pets, etc.), oncoming, and cut-in vehicles in this release.
After reading the following FSD Beta v11.4.4 release notes (below), we can understand that Tesla specially focused on rectifying issues that were frequently reported by beta testers.
For example, in FSD Beta v11.3.4 some testers reported issues with oncoming traffic and merging. In one of the previous versions, an FSD Beta tester also reported Tesla Autopilot’s confusion at a crosswalk. Tesla seemingly has taken this feedback seriously and tried to weed out issues in v11.4.4.
Tesla released this OTA software update at night time in North America, we will at least have to wait a day for test videos to be released by Tesla owners with FSD Beta access.
However, according to the latest stats by TeslaScope.com and TeslaFi.com, Tesla is rolling out this release fast. Currently, Tesla owners from the United States are getting this update. Maybe after the first round, Tesla will decide to roll it out to Canadian Tesla owners.
According to TeslaFi, 147 Tesla owners in the US have received FSD Beta 11.4.4 and TeslaScope reports 88 users. This brings the total to 235 Tesla vehicles on FSD Beta v11.4.4 (2023.7.20) but some users might be adding information on both websites, so the total figure should be taken with a grain of salt.
FSD Beta v11.4.4 (2023.7.20) Official Release Notes
- Improved short-deadline lane changes, to avoid going off-route, through better modeling of target lane vehicles to improve gap selection assertiveness.
- Improved offset consistency when controlling for static obstacles. Also improved smoothness when changing offset direction by adjusting speed more comfortably.
- Improved handling of oncoming cars on narrow unmarked roads by improved prediction of oncoming car’s trajectory and leaving enough room for them to pass before re-centering.
- Improved Occupancy Flow prediction from the Occupancy Network for arbitrary moving obstacles by 8%.
- Expanded usage of the new object ground truth autolabeler for the NonVRU detection model, improving distant vehicle recall and geometry precision for semi-trucks, trailers, and exotic vehicles.
- Improved VRU control by expanding planning scope to control gently for low-confidence detections that may interfere with ego’s path.
- Improved handling for VURs near crosswalks by predicting their future intent more accurately. This was done by leveraging more kinematic data to improve association between crosswalks and VRUs.
- Improved ego’s behavior near VRUs by turning their assumed kinematic properties and utilizing available semantic information to classify more accurately their probability of intersecting ego’s path.
- Improved Automatic Emergency Braking recall in response to cut-in vehicles and vehicles behind ego while reversing.
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