公开(公告)号：US20240294180A1

公开(公告)日：2024-09-05

申请号：US18178183

申请日：2023-03-03

Applicant: Honda Motor Co., Ltd.

Inventor： Shashank Kumar MEHROTRA ,  Kumar AKASH ,  Zhaobo K. ZHENG ,  Teruhisa MISU

IPC: B60W50/08

CPC classification number: B60W50/08 ,  B60W2050/0043 ,  B60W2050/0095 ,  B60W2530/13

Abstract: Systems and methods for determining trust across mobility platforms are provided. In one embodiment, a method includes receiving first mobility data for a first automation experience of a user with a first mobility platform. The method also includes receiving a swap indication for a second automation experience of the user with a second mobility platform after the first automation experience. The method further includes selectively assigning the first mobility platform to a first mobility category and the second mobility platform to a second mobility category different than the first mobility category. The method yet further includes calculating an estimated trust score for the second automation experience by applying a trust model based on the first mobility category, the second mobility category, and a sequence of the first automation experience and the second automation experience. The method includes modifying operation of the second mobility platform based on the estimated trust score.

公开(公告)号：US20220324490A1

公开(公告)日：2022-10-13

申请号：US17467159

申请日：2021-09-03

Applicant: Honda Motor Co., Ltd.

Inventor： Kumar AKASH ,  Teruhisa MISU ,  Xingwei WU

IPC: B60W60/00 ,  G06K9/00 ,  G05B13/02 ,  G06N3/04

Abstract: A system and method for providing an RNN-based human trust model that include receiving a plurality of inputs related to an autonomous operation of a vehicle and a driving scene of the vehicle and analyzing the plurality of inputs to determine automation variables and scene variables. The system and method also include outputting a short-term trust recurrent neural network state that captures an effect of the driver's experience with respect to an instantaneous vehicle maneuver and a long-term trust recurrent neural network state that captures the effect of the driver's experience with respect to the autonomous operation of the vehicle during a traffic scenario. The system and method further include predicting a take-over intent of the driver to take over control of the vehicle from an automated operation of the vehicle during the traffic scenario.

公开(公告)号：US20250136225A1

公开(公告)日：2025-05-01

申请号：US18494346

申请日：2023-10-25

Applicant: Honda Motor Co., Ltd.

Inventor： SIDNEY TAMMIE SCOTT-SHARONI ,  Shashank Kumar MEHROTRA ,  Miao SONG ,  Kevin Joel Gabriel SALUBRE ,  Kumar AKASH ,  Teruhisa MISU

IPC: B62J50/22 ,  G01C21/34

Abstract: A method and system for calculating route guidance for a personal transport device based on prosocial costs is described. In one embodiment, the method includes calculating, by a processor associated with the personal transport device, a plurality of routes from a first location to a second location and determining, by the processor, one or more prosocial factors associated with each route of the plurality of routes. The method also includes calculating, by the processor, a prosocial cost associated with each route of the plurality of routes. Based on the prosocial costs, the method further includes providing, by the processor, route guidance to a user of the personal transport device from the first location to the second location along a selected route from the plurality of routes and displaying the selected route on a display associated with the personal transport device.

公开(公告)号：US20240403630A1

公开(公告)日：2024-12-05

申请号：US18328407

申请日：2023-06-02

Applicant: Honda Motor Co., Ltd.

Inventor： Fatemeh KOOCHAKIGHERMEZCHESHME ,  Zhaobo ZHENG ,  Kumar AKASH ,  Teruhisa MISU

IPC: G06N3/08

Abstract: Siamese neural network (SNN) based adaptive driving style prediction may be achieved by calculating a first distance between the input data and a first class of a set of anchor data using a trained SNN, calculating a second distance between the input data and a second class of the set of anchor data using the trained SNN, and generating an adaptive driving style prediction based on the first distance and the second distance. The trained SNN may be trained based on two or more sensor signals received during a training phase, a distance-based loss for the two or more sensor signals from the training phase, and by back-propagating the distance-based loss.

公开(公告)号：US20230391366A1

公开(公告)日：2023-12-07

申请号：US17829664

申请日：2022-06-01

Applicant: Honda Motor Co., Ltd.

Inventor： Zhaobo K. ZHENG ,  Kumar AKASH ,  Teruhisa MISU

IPC: B60W60/00 ,  B60W50/10 ,  B60W40/08 ,  G06V20/58

CPC classification number: B60W60/0013 ,  B60W60/005 ,  B60W50/10 ,  B60W40/08 ,  B60W2540/229 ,  B60W2040/0872 ,  B60W2420/42 ,  B60W2540/221 ,  B60W2540/225 ,  G06V20/58

Abstract: A system and method for detecting a perceived level of driver discomfort in an automated vehicle that include receiving image data associated with a driving scene of an ego vehicle, dynamic data associated with an operation of the ego vehicle, and driver data associated with a driver of the ego vehicle during autonomous operation of the ego vehicle. The system and method also include analyzing the image data, the dynamic data, and the driver data and extracting features associated with a plurality of modalities. The system and method additionally include analyzing the extracted features and detecting the perceived level of driver discomfort. The system and method further include analyzing the perceived level of driver discomfort and detecting a probable driver takeover intent of the driver of the ego vehicle to takeover manual operation of the ego vehicle.

公开(公告)号：US20240190481A1

公开(公告)日：2024-06-13

申请号：US18077904

申请日：2022-12-08

Applicant: Honda Motor Co., Ltd.

Inventor： Kumar AKASH ,  Teruhisa MISU ,  Zhaobo K. ZHENG

IPC: B60W60/00

CPC classification number: B60W60/0054 ,  B60W60/001 ,  B60W2050/0075

Abstract: According to one aspect, systems and techniques for adaptive trust calibration may include usage of a driving style predictor, including a memory and a processor. The memory may store one or more instructions and the processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, such as receiving a current automated vehicle (AV) driving style, receiving an indication of an event and an associated event type, receiving an indication of a driver takeover, concatenating the current AV driving style and one or more of the event type or the driver takeover to generate an input, and passing the input through a neural network, which may include a gated recurrent unit (GRU), to generate a preference change associated with the AV driving style.

公开(公告)号：US20240043027A1

公开(公告)日：2024-02-08

申请号：US17883540

申请日：2022-08-08

Applicant: Honda Motor Co., Ltd.

Inventor： Zhaobo K. ZHENG ,  Teruhisa MISU ,  Kumar AKASH

IPC: B60W50/10 ,  G05B13/04 ,  G05B13/02

CPC classification number: B60W50/10 ,  G05B13/04 ,  G05B13/0265 ,  B60W2540/225 ,  B60W2540/221 ,  B60W2420/42 ,  B60W2050/0075

Abstract: According to one aspect, an adaptive driving style system may include a set of two or more sensors, a memory, and a processor. The set of two or more sensors may receive two or more sensor signals. The memory may store one or more instructions. The processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, including training a trust model using two or more of the sensor signals as input, training a preference model using the trust model and two or more of the sensor signals as input, and generating a driving style preference based on an adaptive driving style model including the trust model and the preference model.

公开(公告)号：US20220396287A1

公开(公告)日：2022-12-15

申请号：US17344119

申请日：2021-06-10

Applicant: Honda Motor Co., Ltd.

Inventor： Kumar AKASH ,  Teruhisa MISU

IPC: B60W60/00 ,  B60W40/09

Abstract: Aspects of adaptive trust calibration may include receiving a trust model for an occupant of an autonomous vehicle calculated based on occupant sensor data and a first scene context sensor data, and/or receiving a second scene context sensor data associated with an environment of the autonomous vehicle, determining an over trust scenario or an under trust scenario based on the trust model and a trust model threshold, and generating and implementing a human machine interface (HMI) action or a driving automation action based on the determination of the over trust scenario or the determination of the under trust scenario, and/or the second scene context sensor data.

公开(公告)号：US20220277165A1

公开(公告)日：2022-09-01

申请号：US17324453

申请日：2021-05-19

Applicant: Honda Motor Co., Ltd.

Inventor： Haibei ZHU ,  Teruhisa MISU ,  Sujitha Catherine MARTIN ,  Xingwei WU ,  Kumar AKASH

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  B60R16/023

Abstract: A system and method for improving driver situation awareness prediction using human visual sensory and memory mechanism that includes receiving data associated with a driving scene of a vehicle and an eye gaze of a driver of the vehicle. The system and method also include analyzing the data and extracting features associated with objects located within the driving scene and determining a situational awareness score that is associated a situational awareness of the driver with respect to each of the objects located within the driving scene. The system and method further include communicating control signals to electronically control at least one system of the vehicle based on the situational awareness score that is associated with each of the objects located within the driving scene.

公开(公告)号：US20250103048A1

公开(公告)日：2025-03-27

申请号：US18371753

申请日：2023-09-22

Applicant: Honda Motor Co., Ltd.

Inventor： Shashank Kumar MEHROTRA ,  Zahra ZAHEDI ,  Teruhisa MISU ,  Kumar AKASH

IPC: G05D1/02

Abstract: An automated vehicle (AV) is configured to perform automated travel, and includes vehicle sensors configured to detect a second vehicle in a surrounding environment of the AV. The AV includes at least one of a brake mechanism, an accelerator mechanism, a steering control, and a user interface configured to generate a user response to automated travel by the AV. The AV includes a computing device configured to identify an interaction between the AV and the second vehicle while executing an automated travel path, and receive user responses to automated travel by the AV. The computing device is configured to determine at least one aspect of wellbeing, trust, and satisfaction of the user riding the AV based on the user responses, and determine a learned optimal policy which increases the at least one aspect of wellbeing, trust, and satisfaction based on the user responses.