Methods to copy human thinking

  1. Deep learning for scene danger ranking
  2. End2End 7sec ego-trajectory prediction
  3. Active learning from DNN mistakes

1. Deep learning for scene danger ranking

  • Overview

Motivation: Creating a machine with the skill of anticipating complex hazardous situations

Approach: DNN for hazard anticipation in every scene by embedding human experience

Function: Danger map for safe path planning with variable safety margins adjusted from level of danger

Applications: Autonomous driving, Urban assistant, Driving teacher

  • Description

  1. Golden standard of danger levels and location
    • Experiment designed by driving psychologists
    • Danger labels by driving instructors or by bio signals (ongoing) to avoid subjectivity. It enabled us to create a small dataset of scenes with level of hazard
  2. Train DNNs to perceive the world as drivers (object, contours, motion, prediction, saliency)
    • Incremental transfer learning of generic functions for reasoning about hazard in new scenes
    • Output dense map from few training pixels This approach enables hazard raking in every scene

  • Achievement

  • Feasibility to reproduce by DNN subjectively rated information: the first DNN of its kind
  • Multi-task fusion of transfer learning blocks was realized (FCN8s-Pyramid architecture) and analysis suggests that danger ranking benefits from transfer learning
  • Realizing the danger map with the function of prediction and anticipation of Hazards
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2. Safe vehicle trajectory prediction using deep neural networks and camera images

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3. Active learning from DNN mistakes

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