Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

A method of non-line-of-sight (NLoS) obstacle detection for an ego vehicle is described. The method includes capturing a sequence of images over a period with an image capture device. The method also includes storing the sequence of images in a cyclic buffer. The method further includes registering each image in the cyclic buffer to a projected image. The method includes performing the registering by estimating a homography H for each frame of the sequence of images to project to a view point of a first frame in the sequence of images and remove motion of the ego vehicle in the projected image. The method also includes enhancing the projected image. The method further includes classifying the projected image based on a scene determination. The method also includes issuing a control signal to the vehicle upon classifying the projected image.

Researchers

Daniela Rus / Guy Rosman / Sertac Karaman / Felix Naser / Igor Gilitschenski / Alexander Amini / Christina Liao

Departments: Dept of Electrical Engineering & Computer Science, Department of Aeronautics and Astronautics, Computer Science & Artificial Intelligence Lab
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Industrial Engineering & Automation: Autonomous Systems
Impact Areas: Advanced Materials

  • infrastructure-free nlos obstacle detection for autonomous cars
    United States of America | Granted | 11,010,622
  • non-line of sight obstacle detection
    United States of America | Published application

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