Tesla releases new Full Self Driving beta software with a lot of improvements

• Upgraded decision making framework for unprotected left turns with better modeling of objects’ response to ego’s actions by adding more features that shape the go/no-go decision. This increases robustness to noisy measurements while being more sticky to decisions within a safety margin. The framework also leverages median safe regions when necessary to maneuver across large turns and accelerating harder through maneuvers when required to safely exit the intersection.

• Improved creeping for visibility using more accurate lane geometry and higher resolution occlusion detection.

• Reduced instances of attempting uncomfortable turns through better integration with object future predictions during lane selection.

• Upgraded planner to rely less on lanes to enable maneuvering smoothly out of restricted space.

• Increased safety of turns crossing traffic by improving the architecture of the lanes neural network which greatly boosted recall and geometric accuracy of crossing lanes.

• Improved the recall and geometric accuracy of all lane productions by adding 180,000 video clips to the training set.

• Reduced traffic control related false slowdowns through better integration with lane structure and improved behavior with respect to yellow lights.

• Improved the geometric accuracy of road edge and line predictions by adding a mixing/coupling layer with the generalized static obstacle network.

• Improved geometric accuracy and understanding of visibility by retraining the generalized static obstacle network with improved data from the auto labeler and by adding 30,000 more video clips.

• Improved recall of motorcycles, reduced velocity error of close-by pedestrian and bicyclists, and reduced heading error of pedestrians by adding new sim and auto-labeled data to the training set.

• Improved precision of the “is parked” attribute on vehicles by adding 41,000 clips to the training set. Solved 48% of failure cases captured by our telemetry of 10.11.

• Improved detection recall of faraway crossing objects by regenerating the dataset with improved versions of the neural networks used in the auto labeler, which increased data quality.

• Improved offsetting behavior when maneuvering around cars with open doors.

• Improved angular velocity and lane-centric velocity for non-VRU objects by upgrading it into network predicted tasks.

• Improved comfort when lane changing behind vehicles with harsh deceleration by tighter integration between lead vehicles future motion estimate and planned lane change profile.

• Increased reliance on network-predicted acceleration for all moving objects, previously only longitudinally relevant objects.

• Updated nearby vehicle assets with visualization indicating when a vehicle has a door open.

• Improved system frame rate +1.8 frames per second by removing three legacy neural networks.