Robert Boschgmbh

Ravi Kumar Satzoda

Stefan Weissert

Naveen Ramakrishnan

Jayanta Kumar Dutta

Ryan Burt

Filipe Goncalves

Krishnan Bharath Navalpakkam

Yumi Kondo

Alexander Hirsch

Carlos Eduardo Cunha

Simon Geisler

A system and method is disclosed that may employ a set of ensemble methods that comprise a plurality of machine learning algorithms or statistical algorithms for hazard detection. The system and method may combine images, stereo and context information using a plurality of deep learning algorithms to accurately detection hazards. The system and method may incorporate a depth channel to the red, green, and blue (RGB) channels of an image to create a four-channel RGBD image. The system and method may also overlay an RGB image with a color-map of the depth channel. The system and method may further concatenate regions of interest (ROI) to the RGB channels of an image. Lastly, the system and method may incorporate an auxiliary semantic segmentation decoder for a multi-task learning event on a drivable space.

Hazard detection ensemble architecture system and method

A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an 'activity vector.' Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.

System and method for detecting abnormal passenger behavior in autonomous vehicles

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Sunnyvale, CA, United States of America

Ravi Kumar Satzoda