Modeld readme replaced with driving.h

selfdrive/modeld/models/README.md

@@ -24,63 +24,7 @@ To view the architecture of the ONNX networks, you can use [netron](https://netr
 
 
 ### Supercombo output format (Full size: 6472 x float32)
-* **plan**
-  * 5 potential desired plan predictions : 4955 = 5 * 991
-    * predicted mean and standard deviation of the following values at 33 timesteps : 990 = 2 * 33 * 15
-      * x,y,z position in current frame (meters)
-      * x,y,z velocity in local frame (meters/s)
-      * x,y,z acceleration local frame (meters/(s*s))
-      * roll, pitch , yaw in current frame (radians)
-      * roll, pitch , yaw rates in local frame (radians/s)
-    * probability[^1] of this plan hypothesis being the most likely: 1
-* **lanelines**
-  * 4 lanelines (outer left, left, right, and outer right): 528 = 4 * 132
-    * predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
-      * y position in current frame (meters)
-      * z position in current frame (meters)
-* **laneline probabilties**
-  * 2 probabilities[^1] that each of the 4 lanelines exists : 8 = 4 * 2
-    * deprecated probability
-    * used probability
-* **road-edges**
-  * 2 road-edges (left and right): 264 = 2 * 132
-    * predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
-      * y position in current frame (meters)
-      * z position in current frame (meters)
-* **leads**
-  * 2 hypotheses for potential lead cars : 102 = 2 * 51
-    * predicted mean and stadard deviation for the following values at 0,2,4,6,8,10s : 48 = 2 * 6 * 4
-      * x position of lead in current frame (meters)
-      * y position of lead in current frame (meters)
-      * speed of lead (meters/s)
-      * acceleration of lead(meters/(s*s))
-    * probabilities[^1] this hypothesis is the most likely hypothesis at 0s, 2s or 4s from now : 3
-* **lead probabilities**
-  * probability[^1] that there is a lead car at 0s, 2s, 4s from now : 3 = 1 * 3
-* **desire state**
-  * probability[^1] that the model thinks it is executing each of the 8 potential desire actions : 8
-* **meta** [^2]
-  * Various metadata about the scene : 80 = 1 + 35 + 12 + 32
-    * Probability[^1] that openpilot is engaged : 1
-    * Probabilities[^1] of various things happening between now and 2,4,6,8,10s : 35 = 5 * 7
-      * Disengage of openpilot with gas pedal
-      * Disengage of openpilot with brake pedal
-      * Override of openpilot steering
-      * 3m/(s*s) of deceleration
-      * 4m/(s*s) of deceleration
-      * 5m/(s*s) of deceleration
-    * Probabilities[^1] of left or right blinker being active at 0,2,4,6,8,10s : 12 = 6 * 2
-    * Probabilities[^1] that each of the 8 desires is being executed at 0,2,4,6s : 32 = 4 * 8
-
-* **pose** [^2]
-  * predicted mean and standard deviation of current translation and rotation rates : 12 = 2 * 6
-    * x,y,z velocity in current frame (meters/s)
-    * roll, pitch , yaw rates in current frame (radians/s)
-* **recurrent state**
-  * The recurrent state vector that is fed back into the GRU for temporal context : 512
-
-[^1]: All probabilities are in logits, so you need to apply sigmoid or softmax functions to get actual probabilities
-[^2]: These outputs come directly from the vision blocks, they do not have access to temporal state or the desire input
+Read [here](https://github.com/commaai/openpilot/blob/90af436a121164a51da9fa48d093c29f738adf6a/selfdrive/modeld/models/driving.h#L236) for more.
 
 
 ## Driver Monitoring Model