Intel Corporation

Anthony Kyung Guzman Leguel

David Gomez Gutierrez

Rafael De La Guardia Gonzalez

Jose I Parra Vilchis

Leobardo E Campos Macias

Rodrigo Aldana Lopez

A method for calculating a time to contact of an autonomous vehicle, the method comprising: obtaining a plurality of event data an image, wherein the event data is associated with a pixel associated with a change in light intensity; determining a reference signal frequency associated with a transmitted light; identifying a select event data from the plurality of event data, wherein the light frequency associated with the select event data is substantially the same as the reference signal frequency; determining an object based on the select event data, wherein the object is fully enclosed by a bounding box comprising coordinates of a rectangular border; calculating a distance between a set of coordinates of the bounding box closest to the autonomous vehicle and the autonomous vehicle; and calculating the time to contact between the set of coordinates of the bounding box and the autonomous vehicle.

Methods and devices for detecting objects and calculating a time to contact in autonomous driving systems

Techniques are disclosed to facilitate, in autonomous vehicles, the robust detection and classification of objects in a scene using a static sensors in conjunction with event-based sensors. A trained system architecture may be implemented, and the fusion of both sensors thus allows for the consideration of scenes with overexposure, scenes with underexposure, as well as scenes in which there is no movement. In doing so, the autonomous vehicle may detect and classify objects in conditions in which each sensor, if operating separately, would not otherwise be able to classify (or classify with high uncertainty) due to the sensing environment.

Robust object detection and classification using static-based cameras and events-based cameras

Techniques are disclosed to facilitate cooperative mapping for safe and efficient trajectory planning and collision avoidance by allowing nearby agents to share contextual information. The described techniques also function to extend the mapping range of a single agent by leveraging observations made by multiple agents. Furthermore, the techniques as described herein function to reduce uncertainty in trajectory planning by allowing agents to 'see' behind occlusions, thus taking advantage of observations made by neighboring agents from different points of view. An efficient hardware implementation of the system is also presented that leverages the methodologies as discussed herein.

Cooperative mapping for autonomous vehicles, robots or multi-agent systems

In one embodiment, a distributed oscillator computes a first error value based on a first state value for the distributed oscillator and first state values for other distributed oscillators, and computes a second error value based on a second state value for the distributed oscillator and second state values for the other distributed oscillators. The distributed oscillator computes a new first state value for the distributed oscillator based on the first error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator, and computes a new second state value for the distributed oscillator based on the second error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator. The distributed oscillator transmits the new first state value and the new second state value to the other distributed oscillators.

Growing community of inventors

Guadalajara, Mexico

Anthony Kyung Guzman Leguel