openpilot_comma/selfdrive/modeld/models

Neural networks in openpilot

To view the architecture of the ONNX networks, you can use netron

Driving Model (vision model + temporal policy model)

Vision inputs (Full size: 799906 x float32)

• image stream
  • Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
    • Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
      • Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
      • Channel 4 represents the half-res U channel
      • Channel 5 represents the half-res V channel
• wide image stream
  • Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
    • Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
      • Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
      • Channel 4 represents the half-res U channel
      • Channel 5 represents the half-res V channel

Policy inputs

• desire
  • one-hot encoded buffer to command model to execute certain actions, bit needs to be sent for the past 5 seconds (at 20FPS) : 100 * 8
• traffic convention
  • one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
• lateral control params
  • speed and steering delay for predicting the desired curvature: 2
• previous desired curvatures
  • vector of previously predicted desired curvatures: 100 * 1
• feature buffer
  • a buffer of intermediate features including the current feature to form a 5 seconds temporal context (at 20FPS) : 100 * 512

Driving Model output format (Full size: XXX x float32)

Refer to slice_outputs and parse_vision_outputs/parse_policy_outputs in modeld.

Driver Monitoring Model

• .onnx model can be run with onnx runtimes
• .dlc file is a pre-quantized model and only runs on qualcomm DSPs

input format

• single image W = 1440 H = 960 luminance channel (Y) from the planar YUV420 format:
  • full input size is 1440 * 960 = 1382400
  • normalized ranging from 0.0 to 1.0 in float32 (onnx runner) or ranging from 0 to 255 in uint8 (snpe runner)
• camera calibration angles (roll, pitch, yaw) from liveCalibration: 3 x float32 inputs

output format

• 84 x float32 outputs = 2 + 41 * 2 (parsing example)
  • for each person in the front seats (2 * 41)
    • face pose: 12 = 6 + 6
      • face orientation [pitch, yaw, roll] in camera frame: 3
      • face position [dx, dy] relative to image center: 2
      • normalized face size: 1
      • standard deviations for above outputs: 6
    • face visible probability: 1
    • eyes: 20 = (8 + 1) + (8 + 1) + 1 + 1
      • eye position and size, and their standard deviations: 8
      • eye visible probability: 1
      • eye closed probability: 1
    • wearing sunglasses probability: 1
    • face occluded probability: 1
    • touching wheel probability: 1
    • paying attention probability: 1
    • (deprecated) distracted probabilities: 2
    • using phone probability: 1
    • distracted probability: 1
  • common outputs 2
    • poor camera vision probability: 1
    • left hand drive probability: 1