Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Albert-ludwigs-universitat Freiburg

Albert-ludwigs-universitaet Freiburg

Wolfram Burgard

Adrien David Gaidon

Rares A Ambrus

Mark Van Loock

Bastian Steder

Takashi Yamamoto

Guy Rosman

Yoshiaki Asahara

Pavel V Tokmakov

Nick Fishwick

Tim Caselitz

Jörg Röwekämper

Michael Ruhnke

Giorgio Grisetti

Michael Krawez

Andreas Buehler

Gian Diego Tipaldi

Jugesh Sundram

A method for simulating, at an instant, the illumination of an indoor scene observed by a camera and being illuminated by outdoor light, the method comprising: a first preliminary phase including: obtaining a reflectance map of the scene elaborating normalized radiosity maps for the contribution of the sky and the ground, a second phase carried out within a first given duration from the instant including: obtaining the position of the sun, obtaining a normalized radiosity map for the contribution of the sun, a third phase, including: acquiring an image of the scene, determining brightness scale parameters for the ground, the sky, and the sun. The disclosure also proposes tracking the camera position.

Method for simulating the illumination of an indoor scene observed by a camera illuminated by outdoor light and system

A method for behavior cloned vehicle trajectory planning is described. The method includes perceiving vehicles proximate an ego vehicle in a driving environment, including a scalar confidence value of each perceived vehicle. The method also includes generating a bird's-eye-view (BEV) grid showing the ego vehicle and each perceived vehicle based on each of the scalar confidence values. The method further includes ignoring at least one of the perceived vehicles when the scalar confidence value of the at least one of the perceived vehicles is less than a predetermined value. The method also includes selecting an ego vehicle trajectory based on a cloned expert vehicle behavior policy according to remaining perceived vehicles.

Method and system for behavioral cloning of autonomous driving policies for safe autonomous agents

A method for tracking an object performed by an object tracking system includes encoding locations of visible objects in an environment captured in a current frame of a sequence of frames. The method also includes generating a representation of a current state of the environment based on an aggregation of the encoded locations and an encoded location of each object visible in one or more frames of the sequence of frames occurring prior to the current frame. The method further includes predicting a location of an object occluded in the current frame based on a comparison of object centers decoded from the representation of the current state to object centers saved from each prior representation associated with a different respective frame of the sequence of frames occurring prior to the current frame. The method still further includes adjusting a behavior of an autonomous agent in response to identifying the location of the occluded object.

System and method for tracking occluded objects

An action to be performed by a vehicle in response to conflicting input signals can be determined. A sensor signal and an accelerator pedal signal can be received. The sensor signal can indicate a possible need to reduce a speed of the vehicle. The accelerator pedal signal can indicate that an accelerator pedal has been set to a position to increase the speed. An existence of first and second conditions can be determined. The first condition can be that a receipt of the accelerator pedal signal occurred concurrently with a receipt of the sensor signal. The second condition can be that a manner in which the accelerator pedal was set to the position is a better match to a brake pedal usage profile than to an accelerator pedal usage profile. In response to the existence of the first and the second conditions, a brake can be caused to be actuated.

Determining an action to be performed by a vehicle in response to conflicting input signals

A method and a system for calibrating a network of multiple range finders are disclosed. In an embodiment, the method includes performing a measurement with the range finders during a movement of an object through an area covered by the network, identifying, for each range finder, data sets of the measurement associated with the moving object, based on a static analysis of the respective range finder, determining, for each pair of overlapping range finders, an estimated relative transformation between respective poses of the pair, based on the identified data sets of the pair, determining an initial maximum likelihood configuration of poses of all of the range finders based on the estimated relative transformations of each pair and iteratively determining sets of point correspondences of the identified data sets and if a distance of the points in the pair is below a threshold, the threshold being redefined decreasingly for each iteration.

Method and system for calibrating a network of multiple horizontally scanning range finders

To carry out satisfactory object recognition in a short time. An object recognition method in accordance with an exemplary aspect of the present invention is an object recognition method for recognizing a target object by using a preliminarily-created object model. The object recognition method generates a range image of an observed scene, detects interest points from the range image, extracts first features, the first features being features of an area containing the interest points, carries out a matching process between the first features and second features, the second features being features of an area in the range image of the object model, calculates a transformation matrix based on a result of the matching process, the transformation matrix being for projecting the second features on a coordinate system of the observed scene, and recognizes the target object with respect to the object model based on the transformation matrix.

Object recognition method, object recognition apparatus, and autonomous mobile robot

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Denzlingen, Germany

Wolfram Burgard