Abstract: A method for mitigating hazards to access to passenger vehicles. The method includes detecting, with one or more sensors, a hazardous condition in an area proximate a vehicle. A processor may calculate a safety metric corresponding to the hazardous condition and analyze the safety metric relative to a predetermined threshold. A vehicle occupant may be automatically notified of the hazardous condition in the event the safety metric satisfies the predetermined threshold. A corresponding system is also claimed herein.

Abstract: A computing system can crop an image based on a width, height and location of a first vehicle in the image. The computing system can estimate a pose of the first vehicle based on inputting the cropped image and the width, height and location of the first vehicle into a deep neural network. The computing system can then operate a second vehicle based on the estimated pose.

Abstract: A method for mitigating hazards to access to passenger vehicles. The method includes detecting, with one or more sensors, a hazardous condition in an area proximate a vehicle. A processor may calculate a safety metric corresponding to the hazardous condition and analyze the safety metric relative to a predetermined threshold. A vehicle occupant may be automatically notified of the hazardous condition in the event the safety metric satisfies the predetermined threshold. A corresponding system is also disclosed and claimed herein.

Abstract: The present invention extends to methods, systems, and computer program products for validating gesture recognition capabilities of automated systems. Aspects include a gesture recognition training system that is scalable, efficient, repeatable, and accounts for permutations of physical characteristics, clothing, types of gestures, environment, culture, weather, road conditions, etc. The gesture recognition training system includes sensors and algorithms used to generate training data sets that facilitate more accurate recognition of and reaction to human gestures. A training data set can be scaled from both monitoring and recording gestures performed by a humanoid robot and performed by animated humans in a simulation environment. From a scaled training data set, autonomous devices can be trained to recognize and react to a diverse set of human gestures in varying conditions with substantially improved capabilities.