Honda Motor Co., Ltd.

Other

Jiawei Huang

David Francis Isele

Xintian Li

Huckleberry Febbo

Dexin Li

Kikuo Fujimura

Yi-Ting Chen

Simon John Baker

Bernd Heisele

Yan Lu

Mahmut Demir

Thang Lian

An autonomous vehicle capable of trajectory prediction may include a first sensor, a second sensor, a processor, a trajectory planner, a low-level controller, and vehicle actuators. The first sensor may be of a first sensor type and may detect an obstacle and a goal. The second sensor may be of a second sensor type and may detect the obstacle and the goal. The processor may perform matching on the obstacle detected by the first sensor and the obstacle detected by the second sensor, model an existence probability of the obstacle based on the matching, and track the obstacle based on the existence probability and a constant velocity model. The trajectory planner may generate a trajectory for the autonomous vehicle based on the tracked obstacle, the goal, and a non-linear model predictive control (NMPC). The low-level controller may implement the trajectory for the autonomous vehicle by driving vehicle actuators.

Trajectory planner

A system and method for providing a comprehensive trajectory planner for a person-following vehicle that includes receiving image data and LiDAR data associated with a surrounding environment of a vehicle. The system and method also include analyzing the image data and detecting the person to be followed that is within an image and analyzing the LiDAR data and detecting an obstacle that is located within a predetermined distance from the vehicle. The system and method further include executing a trajectory planning algorithm based on fused data associated with the detected person and the detected obstacle.

System and method for providing a comprehensive trajectory planner for a person-following vehicle

The present invention discloses fiducial marker systems or tag systems and methods to detect and decode a tag. In one aspect, a tag comprises four corners. Two upper corners are interconnected to form a detection area. Two lower corners are interconnected to form another detection area. The detection areas are interconnected by a path. The path divides the space between the detection areas into two coding areas. In another aspect, a tag comprises four corners. The four corners are interconnected by multiple paths. The multiple paths divide the space defined by the four corners into multiple coding areas.

Robust fiducial marker for flexible surfaces

A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

System and method for providing online multi-LiDAR dynamic occupancy mapping

The present invention discloses fiducial marker systems or tag systems and methods to detect and decode a tag. The tag comprises a plurality of cells arranged in a grid pattern. The plurality of cells forms a square or rectangular shape. Each cell is in square or rectangular shape and has either black, white, or gray grayscale. Any two cells that share a common borderline should have different grayscales; and the four corner cells of the tag are all in black. The method for detecting and decoding such a tag comprises detecting a plurality of connected cells in an image, determining whether the plurality of connected cells in the image have all three grayscales, determining whether total number of cells in the plurality of connected cells is greater than a predetermined threshold, determining whether any two cells in the plurality of connected cells that share a common borderline in the image have different grayscales, and decoding the tag based on the grayscales of the plurality of connected cells in the image.

Fiducial marker based on blocks of different grayscales

A calibration system and method for Light detection and Ranging (LiDAR)-camera calibration is provided. The calibration system receives a plurality of images that includes a calibration pattern and a plurality of point cloud data (PCD) frames that includes the calibration pattern. The calibration system extracts a first normal to a first plane of the calibration pattern in a first PCD frame of the received plurality of PCD frames and further extracts a second normal to a second plane of the calibration pattern in a first image frame of the received plurality of image frames. The calibration system computes a transform between the extracted first normal and the extracted second normal and based on the computed transform, calibrates the LiDAR sensor with the camera.

LiDAR-camera calibration

Systems and methods for performing instance segmentation. A memory stores instructions for executing processes for performing instance segmentation and a processor configured to execute the instructions. The processes include: generating a learning objective that uses pair-wise relationships between pixels in an input image; sampling pixels in each object instance to determine whether the sampled pixels are within a same object instance; training a neural network using the learning objection, wherein the neural network is configured to make pixel-wise predictions and to assign a cluster index to each pixel of the input image, with each pixel cluster being an object instance; performing graph coloring to assign a color to each object instance, with adjacent object instances having different colors; performing connected component extraction to recover each object instance based on the graph coloring; and generating a rendered image having the assigned color applied to each object instance.

Systems and methods for performing instance segmentation

The present disclosure relates to methods and systems for monocular localization in urban environments. The method may generate an image from a camera at a pose. The method may receive a pre-generated map, and determine features from the generated image based on edge detection. The method may predict a pose of the camera based on at least the pre-generated map, and determine features from the predicted camera pose. Further, the method may determine a Chamfer distance based upon the determined features from the image and the predicted camera pose, optimize the determined Chamfer distance based upon odometry information and epipolar geometry. Upon optimization, the method may determine an estimated camera pose.

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San Jose, CA, United States of America

Jiawei Huang