Toyota Research Institute, Inc.

Toyota Motor Engineering & Manufacturing North America, Inc.

Matthew Derry

Jacopo Serafin

Danil V Prokhorov

Michael R James

Kuan-Hui Lee

Yusuke Kanzawa

Systems and methods of tracking objects around an ego vehicle, using state estimation of the objects, where state estimation may include an estimate of a position, velocity, acceleration and bounding box size of the tracked object are disclosed. An object may be detected at one or more poses over time. For each pose, a first bounding box may be generated. The largest bounding box may be determined. For each pose, the first bounding box may be expanded to an expanded bounding box. The expanded bounding box may match the largest bounding box. For each pose, the center of the expanded bounding box may be determined. The object may be tracked according to the center of the expanded bounding box.

System and method for tracking objects using using expanded bounding box factors

Fusing online and mapped terrain estimates by using weighted grid cells that scales the values returned from online terrain and mapped terrain is disclosed. Previously mapped terrain data and online terrain data are fused and a grid having cells of a predetermined size is overlaid on the terrain map. Each cell may include terrain data based on weighted mapped terrain data and weighted online terrain data, where the weighting values for the mapped terrain data and for the online terrain data may be different. A fused terrain estimate may be a result of a weighted mean for each cell smoothed to reduce noise.

System and method for generating terrain maps

System and method for tracking objects using multi-edge bounding box factors

System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.

Systems and methods for multi-sensor fusion using permutation matrix track association

A method and device for effecting vehicle control is presented. The method includes receiving driving map data, which includes vehicle target data relative to a vehicle location data. The driving map data is processed to produce desired vehicle operational data, the desired vehicle operational data facilitates a vehicle to traverse a travel route. From the desired vehicle operational data vehicle, corresponding actuator control data is produced, and transmitted to effect vehicle control in either of the autonomous or driver-assisted modes.

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Ann Arbor, MI, United States of America

Matthew Derry