Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Akila C Ganlath

Prashant Tiwari

Nejib Ammar

Haoxin Wang

Onur Altintas

Siqi Huang

Takayuki Shimizu

BaekGyu Kim

John P Kenney

Kyungtae Han

Hongsheng Lu

Rohit Gupta

Rohit Kumar Gupta

Ahmed Hamdi Sakr

Paul Li

Qi Chen

Satoshi Nagashima

Xinyue Kan

Ziran Wang

A computing device and a method to generate a decentralized delivery scheme for multivehicle in an interested area may comprise the following steps. First, the computing device may receive map data, travel request data, and service vehicle data. Second, the computing device may determine one or more constraints based on the travel request data. Third, the computing device may abstract the travel request data, the service vehicle data, and the map data into a request-vehicle graph comprising nodes and edges. Fourth, the computing device may trim the request-vehicle graph into partial request-vehicle graphs for each service vehicle. Fifth, the computing device may encode the partial request-vehicle graphs through a graph neural network (GNN). Sixth, the computing device may train the GNN to predict actions for each service vehicle. Finally, the computing device may instruct the service vehicles to operate based on the actions.

Devices and methods of decentralized on-demand multivehicle delivery

A method includes receiving requests to form a vehicle platoon from a plurality of vehicles, determining terms of a contract for the plurality of vehicles to form the vehicle platoon comprising a lead vehicle and one or more follower vehicles, and transmitting terms of the contract to the plurality of vehicles. The terms of the contract comprise the lead vehicle receiving a first payment, at least one of the follower vehicle making a second payment, and each of the follower vehicles receiving vehicle data from the lead vehicle according to a unique service profile associated with each follower vehicle.

System and methods for platoon-leader-as-a-service

The disclosure includes embodiments for a medic system to respond to a medical condition of a vehicle occupant. A method according to some embodiments is executed by a processor. The method also includes determining, by the processor, that a driver of an ego vehicle is experiencing a debilitating medical condition. The method also includes overriding a protocol to decrease an autonomy level of the ego vehicle responsive to inattentiveness of the driver to a driving interface of the ego vehicle so that the driver can be inattentive to the driving interface and the autonomy level is not decreased. The method also includes modifying an operation of an autonomous driving system of the ego vehicle to increase the autonomy level of the ego vehicle to decrease a driving responsibility of the driver responsive to the debilitating medical condition.

Medical emergency detection in-vehicle caretaker

Systems, methods, and other embodiments described herein relate to reconstructing a dynamic scene using Neural Radiance Field (NeRF) technology. In one embodiment, a method includes receiving, from one or more sensors, a plurality of video clips of an environment. The method includes generating a second plurality of video clips based on the plurality of video clips, and reconstructing, using NeRF technology, a scene as a continuous function based on the second plurality of video clips.

Dynamic scene reconstruction using neural radiance field technology

Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.

Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles

A method is provided including receiving a planned route of a vehicle and a request to download content from a cloud server, the planned route traveling through an area covered by a plurality of edge servers, determining a state comprising possible connections between the vehicle and each of the plurality of edge servers at a plurality of time steps during the planned route, inputting the state to a trained model, the model being trained to output an action comprising a partition of the content across the plurality of edge servers that minimizes latency of transmission of the content from the cloud server to the vehicle via the plurality of edge servers, based on the state, and partitioning the content across the plurality of edge servers based on the action out by the trained mode.

Method and system for edge server selection for in-vehicle media content distribution with deep reinforcement learning

A server includes a controller programmed to obtain information about a first perception model installed in a vehicle. The controller is further programmed to determine a value of updating the first perception model. The controller is further programmed to determine whether the first perception model needs to be updated to a second perception model based on the value of updating the first perception model. The controller is further programmed to transmit the second perception model to the vehicle in response to determining that the first perception model needs to be updated to the second perception model.

Methods and systems for updating perception models based on geolocation features

A method includes capturing object data using a data capture device of a vehicle, determining that the captured object data includes a relevant object based on a user profile associated with an occupant of the vehicle, and executing a notification at the vehicle indicating a presence of the relevant object in response to determining that the captured object data includes the relevant object.

Object notification systems for identifying and notifying vehicles of relevant objects

An edge device can send a vehicle-specific alert to a connected vehicle, which is configured to communicate with the edge device. The edge device can receive observations of other vehicles on the road from connected vehicles and/or roadside units. The edge device can also receive driving history and/or other information about the other vehicles from a server. The edge device can classify the behavior of vehicles detected in the observations based on the observations, driving history, and/or other information. Based on the classified behavior, the edge device can determine whether and how the connected vehicle is impacted and, if impacted, send the vehicle-specific alert to the connected vehicle.

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Agua Dulce, CA, United States of America

Akila C Ganlath