Tesla Released A Brand New Full Self-Driving Beta update with improved object detection

Tesla has begun to deploy a new Full Self-Driving Beta software upgrade (version 10.6) to its fleet, which includes enhanced object detection.

It’s the latest stage in a long process to upgrade the FSD package and delivers on a promise made to customers that have been repeatedly postponed.

Tesla is gradually releasing Full Self-Driving Beta (FSD Beta), an early version of its self-driving software that is now being tested by a fleet of Tesla owners chosen by the firm and through its “safety test score.”

The software allows the vehicle to drive independently to a destination specified in the navigation system, but the driver must stay alert and ready to take control at all times.

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Despite its name, it is still considered a level 2 driver-assist system because the duty resides with the driver rather than Tesla’s technology. It’s been a “two steps forward, one step back” type of program, with certain updates resulting in regressions in driving skills.

In previous upgrades, the carmaker has provided additional information regarding the modifications to driving habits in the release notes.

Tesla is now distributing a new version of the Full Self-Driving Beta to owners in the United States with a safety score of 98 or higher.

Tesla wrote about the changes in version 10.6 in the release notes:

  • Improved stopping position at unprotected left turns while yielding to oncoming objects, using object predictions beyond the crossing point.
  • Improved object detection network architecture for non-VRUs (e.g. cars, trucks, buses). 7% higher recall. 16% lower depth error and 21% lower velocity error for crossing vehicles.
  • New general static object network with 17% precision improvements in high curvature and nighttime cases.
  • A new visibility network with 18.5% less means relative error.
  • Improved comfort when offsetting for objects that are cutting out of your lane.
  • Allow more room for longitudinal alignment during merges by incorporating modelling of merge region end.

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