公开(公告)号：US20220161825A1

公开(公告)日：2022-05-26

申请号：US17103301

申请日：2020-11-24

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Scott Rad ,  Hyukseong Kwon ,  Rajan Bhattacharyya

IPC: B60W60/00 ,  B60W40/06

Abstract: A system for interactive hypothesis estimation of multi-vehicle traffic for autonomous driving is provided. The system includes a sensor upon a host vehicle providing data regarding an operating environment of the host vehicle and a computerized device. The computerized device is operable to monitor the data from the sensor, identify a road surface based upon the data, and identify a neighborhood object based upon the data. The computerized device is further operable to determine a pressure score for the neighborhood object based upon a likelihood that the neighborhood object will conflict with the host vehicle based upon the road surface and the neighborhood object, selectively track the neighborhood object based upon the pressure score, and navigate the host vehicle based upon the tracking of the neighborhood object.

公开(公告)号：US20220126861A1

公开(公告)日：2022-04-28

申请号：US17082193

申请日：2020-10-28

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Kenji Yamada ,  Kyungnam Kim ,  Rajan Bhattacharyya

IPC: B60W60/00 ,  G06N20/00

Abstract: A control system of the autonomous vehicle may generate multiple possible behavior control movements based on the driving goal and the assessment of the vehicle environment. In doing so, the method and system selects one of the best behavior control, among the multiple possible movements, and the selection is based on the quantitative grading of its driving behavior.

公开(公告)号：US10409279B2

公开(公告)日：2019-09-10

申请号：US15420946

申请日：2017-01-31

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Hyukseong Kwon ,  Youngkwan Cho ,  Rajan Bhattacharyya ,  Michael J. Daily

IPC: G05B1/00 ,  G05D1/00 ,  G06N7/00 ,  H04L29/08 ,  G06N5/02

Abstract: A system and method is taught for data processing in an autonomous vehicle control system. Using information is acquired from the vehicle, network interface, and sensors mounted on the vehicle, the system can perceive situations around it with much less complexity in computation without losing crucial details, and then make navigation and control decisions. The system and method are operative to generate situation aware events, store them, and recall to predict situations for autonomous driving.

公开(公告)号：US11724720B2

公开(公告)日：2023-08-15

申请号：US17487809

申请日：2021-09-28

Applicant: GM Global Technology Operations LLC

Inventor： Amit Agarwal ,  Hyukseong Kwon ,  Rajan Bhattacharyya ,  Michael J. Daily ,  Gavin D. Holland

IPC: B60W60/00 ,  B60W50/00

CPC classification number: B60W60/0027 ,  B60W50/0098 ,  B60W2050/006 ,  B60W2554/4042 ,  B60W2554/4043 ,  B60W2554/4044 ,  B60W2556/60 ,  B60W2720/10 ,  B60W2720/12

Abstract: A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

公开(公告)号：US11654933B2

公开(公告)日：2023-05-23

申请号：US17094255

申请日：2020-11-10

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Tiffany J. Hwu ,  Iman Mohammadrezazadeh ,  Michael J. Daily ,  Rajan Bhattacharyya

IPC: B60W60/00 ,  G06N3/04 ,  B60W30/12

CPC classification number: B60W60/0011 ,  B60W30/12 ,  G06N3/04 ,  B60W2552/10 ,  B60W2555/20 ,  B60W2556/40

Abstract: An ego vehicle includes decider modules and a grader module coupled to a resolver module. The decider modules generate trajectory decisions at a current time, generate a current two-dimensional slice of a flat space around the ego vehicle, generate future two-dimensional slices of the flat space by projecting the current two-dimensional slice of the flat space forward in time, and generate a three-dimensional state space by stacking the current two-dimensional slice and the future two-dimensional slices. The grader module generates rewards for the trajectory decisions based on a recent behavior of an ego vehicle. The resolver module selects a final trajectory decision for the ego vehicle from the trajectory decisions based on the three-dimensional state space and the rewards. The current two-dimensional slice includes a current ego vehicle location and current neighboring vehicle locations. The future two-dimensional slices include future ego vehicle locations and future neighboring vehicle locations.

公开(公告)号：US11524703B2

公开(公告)日：2022-12-13

申请号：US17103301

申请日：2020-11-24

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Scott Rad ,  Hyukseong Kwon ,  Rajan Bhattacharyya

IPC: B60W60/00 ,  B60W40/06

Abstract: A system for interactive hypothesis estimation of multi-vehicle traffic for autonomous driving is provided. The system includes a sensor upon a host vehicle providing data regarding an operating environment of the host vehicle and a computerized device. The computerized device is operable to monitor the data from the sensor, identify a road surface based upon the data, and identify a neighborhood object based upon the data. The computerized device is further operable to determine a pressure score for the neighborhood object based upon a likelihood that the neighborhood object will conflict with the host vehicle based upon the road surface and the neighborhood object, selectively track the neighborhood object based upon the pressure score, and navigate the host vehicle based upon the tracking of the neighborhood object.

公开(公告)号：US11364904B2

公开(公告)日：2022-06-21

申请号：US16365059

申请日：2019-03-26

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Amir M. Rahimi ,  Aashish N. Patel ,  Rajan Bhattacharyya

IPC: B60W30/095 ,  G05D1/02 ,  B60W30/09

Abstract: Embodiments include methods, systems, and computer readable storage medium for a method for providing path-planning guidance by resolving multiple behavioral predictions associated with operating a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path planning guidance based on training data using and fused hypotheses and/or decisions associated with the training data. The method further includes determining, by a processor, a location of the vehicle on a map containing a road network, and determining, by the processor, whether one or more agents exist within a predetermined range of the vehicle. The method further includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more agents. The method further includes controlling, by the processor, operation of the vehicle using the output trajectory.

公开(公告)号：US20220089183A1

公开(公告)日：2022-03-24

申请号：US17026522

申请日：2020-09-21

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Tiffany J. Hwu ,  Rajan Bhattacharyya ,  Michael J. Daily ,  Kyungnam Kim

IPC: B60W60/00 ,  G01C21/00 ,  B60W30/12 ,  B60W10/04 ,  G01C21/34 ,  G06N3/08 ,  G06N3/04

Abstract: A virtual lane estimation system includes a memory device, a sensor and a computer. The memory device is configured to store a road map that corresponds to a portion of a road ahead of a vehicle. The sensor is configured to observe a plurality of trajectories of a plurality of neighboring vehicles that traverse the portion of the road. The computer is configured to initialize a recursive self-organizing map as a plurality of points arranged as a two-dimensional grid aligned with the road map, train the points in the recursive self-organizing map in response to the trajectories, generate a directed graph that contains one or more virtual lanes through the road map in response to the points trained to the trajectories, and generate a control signal that controls navigation of the vehicle through the portion of the road in response to the virtual lanes in the directed graph.

公开(公告)号：US20200310448A1

公开(公告)日：2020-10-01

申请号：US16364262

申请日：2019-03-26

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Kenji Yamada ,  Rajan Bhattacharyya

IPC: G05D1/02 ,  G05D1/00 ,  G06K9/00

Abstract: Embodiments include methods, systems and computer readable storage medium for a method for behavioral path planning guidance for a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path-planning guidance based on training data and one or more output trajectories generated from a plurality of predictive models and a plurality of input variables. The method includes determining, by a processor, a location of the vehicle on a map containing a road network and determining, by the processor, whether one or more objects exist within a predetermined range of the vehicle. The method includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more objects. The method includes controlling, by the processor, operation of the vehicle using the output trajectory.

公开(公告)号：US20200307574A1

公开(公告)日：2020-10-01

申请号：US16365059

申请日：2019-03-26

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Amir M. Rahimi ,  Aashish N. Patel ,  Rajan Bhattacharyya

IPC: B60W30/095 ,  B60W30/09 ,  G05D1/02

Abstract: Embodiments include methods, systems, and computer readable storage medium for a method for providing path-planning guidance by resolving multiple behavioral predictions associated with operating a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path planning guidance based on training data using and fused hypotheses and/or decisions associated with the training data. The method further includes determining, by a processor, a location of the vehicle on a map containing a road network, and determining, by the processor, whether one or more agents exist within a predetermined range of the vehicle. The method further includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more agents. The method further includes controlling, by the processor, operation of the vehicle using the output trajectory.