DanCar
Active Member
Partial release notes:
Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta's multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
Improved Occupancy Network's recall for close by obstacles and precision in severe weather conditions with 4x increase in transformer spatial resolution, 20% increase in image featurizer capacity, improved side camera calibration, and 260k more video training clips (real-world and simulation).
Reduced the predicted velocity error of very close-by motorcycles, scooters, wheelchairs, and pedestrians by 63.6%. To do this, we introduced a new dataset of simulated adversarial high speed VRU interactions. This update improves autopilot control around fast-moving and cutting-in VRUs.
Improved creeping profile with higher jerk when creeping starts and ends.
Improved control for nearby obstacles by predicting continuous distance to static geometry with the general static obstacle network.
Reduced vehicle "parked" attribute error rate by 17%, achieved by increasing the dataset size by 14%. Also improved brake light accuracy.
Improved clear-to-go scenario velocity error by 5% and highway scenario velocity error by 10%, achieved by tuning loss function targeted at improving performance in difficult scenarios.
Improved detection and control for open car doors.
Improved smoothness through turns by using an optimization-based approach to decide which road lines are irrelevant for c[unreadable] given lateral and longitudinal acceleration and jerk limits as we[unreadable] vehicle kinematics.
Improved stability of the FSD UI visualizations by optimizing ethernet data transfer pipeline by 15%.
Improved recall for vehicles directly behind ego, and improve precision for vehicle detection network.
Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta's multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
Improved Occupancy Network's recall for close by obstacles and precision in severe weather conditions with 4x increase in transformer spatial resolution, 20% increase in image featurizer capacity, improved side camera calibration, and 260k more video training clips (real-world and simulation).
Reduced the predicted velocity error of very close-by motorcycles, scooters, wheelchairs, and pedestrians by 63.6%. To do this, we introduced a new dataset of simulated adversarial high speed VRU interactions. This update improves autopilot control around fast-moving and cutting-in VRUs.
Improved creeping profile with higher jerk when creeping starts and ends.
Improved control for nearby obstacles by predicting continuous distance to static geometry with the general static obstacle network.
Reduced vehicle "parked" attribute error rate by 17%, achieved by increasing the dataset size by 14%. Also improved brake light accuracy.
Improved clear-to-go scenario velocity error by 5% and highway scenario velocity error by 10%, achieved by tuning loss function targeted at improving performance in difficult scenarios.
Improved detection and control for open car doors.
Improved smoothness through turns by using an optimization-based approach to decide which road lines are irrelevant for c[unreadable] given lateral and longitudinal acceleration and jerk limits as we[unreadable] vehicle kinematics.
Improved stability of the FSD UI visualizations by optimizing ethernet data transfer pipeline by 15%.
Improved recall for vehicles directly behind ego, and improve precision for vehicle detection network.
