Massachusetts Institute of Technology

Toyota Research Institute, Inc.

Felix Maximilian Naser

Guy Rosman

Daniela Rus

Sertac Karaman

Igor Gilitschenski

Alexander Amini

William T Freeman

Gregory W Wornell

Fredo Durand

Antonio Torralba

Christina Liao

The present disclosure provides systems and methods to detect occluded objects using shadow information to anticipate moving obstacles that are occluded behind a corner or other obstacle. The system may perform a dynamic threshold analysis on enhanced images allowing the detection of even weakly visible shadows. The system may classify an image sequence as either 'dynamic' or 'static', enabling an autonomous vehicle, or other moving platform, to react and respond to a moving, yet occluded object by slowing down or stopping.

Systems and methods of detecting moving obstacles

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.

Infrastructure-free NLoS obstacle detection for autonomous cars

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Cambridge, MA, United States of America

Felix Maximilian Naser