Uncovering Big improvements to Tesla FSD Beta w/ James Douma #38 (Ep. 693)

I'm joined by James Douma as we examine the latest Tesla FSD Beta improvements.

Andrej Karpathy interview on @Lex Fridman : https://www.youtube.com/watch?v=cdiD-9MMpb0

Tesla FSD Beta 10.69.3 release notes: https://www.notateslaapp.com/software-updates/version/2022.36.15/release-notes

- Upgraded the Object Detection network to photon count video streams and retrained all parameters with the latest autolabeled datasets (with a special emphasis on low visibility scenarios).

Improved the architecture for better accuracy and latency, higher recall of far away vehicles, lower velocity error of crossing vehicles by 20%, and improved VRU precision by 20%.

- Converted the VRU Velocity network to a two-stage network, which reduced latency and improved crossing pedestrian velocity error by 6%.

- Converted the Non VRU Attributes network to a two-stage network, which reduced latency, reduced incorrect lane assignment of crossing vehicles by 45%, and reduced incorrect parked predictions by 15%.

- Reformulated the autoregressive Vector Lanes grammar to improve precision of lanes by 9.2%, recall of lanes by 18.7%, and recall of forks by 51.1%. Includes a full network update where all components were re-trained with 3.8x the amount of data.

- Added a new "road markings" module to the Vector Lanes neural network which improves lane topology error at intersections by 38.9%.

- Upgraded the Occupancy Network to align with road surface instead of ego for improved detection stability and improved recall at hill crest.

- Reduced runtime of candidate trajectory generation by approximately 80% and improved smoothness by distilling an expensive trajectory optimization procedure into a lightweight planner neural network.

- Improved decision making for short deadline lane changes around gores by richer modeling of the trade-off between going off-route vs trajectory required to drive through the gore region

- Reduced false slowdowns for pedestrians near crosswalk by using a better model for the kinematics of the pedestrian

- Added control for more precise object geometry as detected by general occupancy network.

- Improved control for vehicles cutting out of our desired path by better modeling of their turning / lateral maneuvers thus avoiding unnatural slowdowns

- Improved longitudinal control while offsetting around static obstacles by searching over feasible vehicle motion profiles

- Improved longitudinal control smoothness for in-lane vehicles during high relative velocity scenarios by also considering relative acceleration in the trajectory optimization

- Reduced best case object photon-to-control system latency by 26% through adaptive planner scheduling, restructuring of trajectory selection, and parallelizing perception compute. This allows us to make quicker decisions and improves reaction time.

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