- 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 improving 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 VRUs 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 tuning 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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