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Paper

  • Title: Arguing Machines: Perception-Control System Redundancy and Edge Case Discovery in Real-World Autonomous Driving
  • Authors: Lex Fridman, Benedikt Jenik, Bryan Reimer
  • Link: https://arxiv.org/abs/1710.04459
  • Tags: Neural Network, self-driving
  • Year: 2017

Summary

  • What

    • They present a method to detect hard edge cases in datasets of self-driving cars.
    • These are cases that might lead to accidents.
    • The method is based on running two models simultaneously and measuring their disagreement with each other.

  • How

    • They use two different models:
      • Tesla-AD: Proprietary autopilot from Tesla.
      • CNN: Their own CNN, trained on a Tesla dataset (images from car's perspective + steering & acceleration annotation; 420 hours of driving). The network's architecture is similar to the one in the NVIDIA paper.
    • Their considered five different inputs for their own CNN:
      • M5: RGB images (same as in the NVIDIA paper)
      • M4: Images of edges in each color channel (i.e. three edge maps). (No detailed explanation on how the edges were detected.)
      • M3: Grayscale images, from t-20, t-10 and t (where t is the current frame).
      • M2: Grayscale difference images t - t-10 (i.e. difference between the current frame and the one 10 frames ago), t - t-5, t - t-1.
      • M1: Grayscale difference images t-20 - t-30, t-10 - t-20, t - t-10.
      • Visualization:
        • inputs
    • Training
      • They split the data into subgroups, each being defined up by the steering wheel angle. E.g. all example with an angle in the range [-10, 10] end in one group.
      • They select equally from all groups and get 100k training and 50k validation images.
      • This prevents the model from only training on examples showing straight driving.
    • Disagreement
      • Their intention is to find hard example / edge cases.
      • They do this by measuring the disagreement between the models (Tesla-AD and CNN).
      • To do that, they first clip the predicted angles to the range [-10, 10]. Then they normalize the result to the range [-1, 1].
      • They sum the differences of these predictions over a one second window (30 examples).
      • If the sum exceeds a threshold delta, they consider the models to be in disagreement and the example to be an edge case.
      • They use delta=10.

  • Results

    • Input M1 performed best, followed by M2, M3, M4 and M5 (in that order). (Measured via MAE of CNN predictions vs. ground truth annotations.)
    • They can predict with 90% accuracy whether there will be a disengagement in the next 5 seconds (i.e. human driver took control in the dataset).