Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Other

Toyota Motor North America, Inc.

Toyota Motor Eng. & Manuf. North America. Inc.

Shalini Keshavamurthy

Kentaro Oguchi

Seyhan Ucar

BaekGyu Kim

Baik Hoh

Hao Yang

Rui Guo

Onur Altintas

Haritha Muralidharan

Eduard J VanderSmitte

Jacob Ryan Morrow

Ippei Nishitani

Wenjie Luo

Fangying Zhai

A system for automatic scheduling of vehicles includes a network access device configured to receive scheduling information corresponding to at least one travel request and including a user-selected starting location and a user-selected destination location. The system further includes a memory configured to store map information including road information and preselected vehicle stops. The system further includes a processor coupled to the network access device and the memory. The processor is designed to identify available vehicles to transport passengers. The processor is further designed to determine routes for the available vehicles to travel, the routes including vehicle stops and being determined based on the scheduling information, the road information, and the preselected vehicle stops. The processor is further designed to control the network access device to transmit the identified routes to the available vehicles.

Smart placement of mobility as a service (MAAS) transit vehicles

A method for deploying of a backup roadside unit (RSU) vehicle to provide on-demand RSU services is described. The method may include determining, by a server, a resource specification to deploy a selected backup RSU vehicle to a requested site. The determining of the resource specification may be performed by the server in response to a scene description received from a requestor. The method also includes sending a dispatch instruction to the selected backup RSU vehicle to deploy the selected backup RSU vehicle to the requested site. In this method, a hardware/software capability of the selected backup RSU vehicle is matched to the resource specification determined by the server.

System and method of using a vehicle as a backup roadside unit (RSU)

A method determines a topology modification for a geographical area; determines a set of coverage region(s) in the geographical area that are adjacent to a target coverage region, the target coverage region being associated with a first regionalized server configured to communicate with vehicular traffic in the target coverage region, the set of coverage region(s) being respectively associated with a set of second regionalized server(s), each second regionalized server being configured to communicate with vehicular traffic in a corresponding coverage region of the set of coverage region(s); determines that a region count of the set of coverage region(s) satisfies a region count threshold; responsive to determining that the region count of the set of coverage region(s) satisfies the region count threshold, determines a morphing portion for each coverage region in the set of coverage region(s); and morphs the set of coverage region(s) based on the corresponding morphing portions.

Managing regionalized vehicular communication

A sensor control system for a vehicle includes one or more processors and a memory communicably coupled to the one or more processors. The memory stores a zone of interest determination module including computer-readable instructions that when executed by the one or more processors cause the one or more processors to determine if at least one localized zone of interest resides within a preliminary zone of interest of the vehicle. The memory also stores a sensor control module including computer-readable instructions that when executed by the one or more processors cause the one or more processors to control one or more operational parameters of at least one sensor so as to, if at least one localized zone of interest resides within the preliminary zone of interest, bring at least a portion of the at least one localized zone of interest into a of field view of the at least one sensor.

Connectivity-enabled traffic-aware supplemental sensor control for informed driving

An example method determines a plurality of traffic sections for a geographical traffic area, each traffic section including a road segment and vehicle(s) traveling on the road segment; monitors a responsive vehicle rate for a first traffic section of the plurality of traffic sections; and assigns, based on the responsive vehicle rate of the first traffic section, the first traffic section to one of a first section server dedicated to manage the first traffic section and a remote management server capable of managing the plurality of traffic sections of the geographical traffic area, the first section server comprising computing device(s) of responsive vehicle(s) in the first traffic section.

Hierarchical computing architecture for traffic management

The disclosure includes embodiments for managing an anomaly and anomaly-affected entities. In some embodiments, a method includes receiving anomaly data describing an occurrence of the anomaly in a roadway environment. The method includes determining an influence region of the anomaly in the roadway environment. The method includes determining a set of anomaly severity indices associated with a set of sub-regions within the influence region. The method includes managing a group of anomaly-affected entities within the influence region based on the set of anomaly severity indices.

Managing anomalies and anomaly-affected entities

System, methods, and other embodiments described herein relate to transporting an object into and out of a vehicle using an arm mounted to the vehicle. In one embodiment, a method includes detecting a plurality of objects in an environment of the vehicle, and identifying at least one object for transportation. The method includes determining at least one attribute for the at least one object, and determining a transport plan of how to move the at least one object into the vehicle based on the determined at least one attribute. The method includes, based on the transport plan, controlling the arm to move from a retracted position inside the vehicle to an extended position outside the vehicle, to engage the at least one object, move from the extended position to the retracted position, and disengage the at least one object to move the at least one object into the vehicle.

Systems and methods for transporting an object into and out of a vehicle

A method of improving efficiency of a vehicle behavior controller using a traffic state estimation network is described. The method includes feeding an input of a feature extraction network of the vehicle behavior controller with a sequence of images. The sequence of images include a highway section and corresponding traffic data. The method also includes disentangling an estimated behavior of a controlled ego vehicle. by the traffic state estimation network. The traffic state estimate network disentangles the estimated of the controlled ego vehicle from extracted traffic state features of the input provided by the feature extraction network. The method further includes selecting an action to adjust an autonomous behavior of the controlled ego vehicle according to the estimated behavior of the controlled ego vehicle.

Efficiency improvement for machine learning of vehicle control using traffic state estimation

Anomalous events in a driving environment can be detected and/or validated by leveraging inherent human behavior. A notification of the anomalous event can be received from an initial connected entity in the driving environment. In response, an inquiry can be sent to connected entities spatiotemporally related to the initial connected entity as to whether any person associated with the connected entities is exhibiting an inherent human behavior. When a threshold level of responses to the inquiry are received from the connected entities indicating that an inherent human behavior has been detected, the one or more connected entities can be caused to upload driving scene data for at least a time when the inherent human behavior by the occupant of the connected vehicle was detected.

Anomalous event detection and/or validation using inherent human behavior

A system for estimating depth using a monocular camera may include one or more processors, a monocular camera, and a memory device. The monocular camera and the memory device may be operably connected to the one or more processors. The memory device may include an image capture, an encoder-decoder module, a semantic information generating module, and a depth map generating module. The modules may configure the one or more processors to executed by one or more processors cause the one or more processors to obtain a captured image from the monocular camera, generate a synthesized image based on the captured image wherein the style transfer module was trained using a generative adversarial network, generate, a feature map based on the synthesized image, generate semantic information based on the feature map, and generate a depth map based on the feature map and the semantic information.

System and method for estimating depth using a monocular camera

An example method receives sensor data of a vehicle at a localized server associated with a particular geographic region that has one or more roadway segments. The vehicle is located in the particular geographic region. The method analyzes the sensor data to determine one or more of a localized lane option and a localized path option for navigating the vehicle and provides the one or more of the lane and the path for navigating the vehicle to a centralized server executing a route planer. The centralized server has data covering a plurality of geographic regions. The method determines a centralized route option including one or more of the localized lane option and the localized path option and provides the centralized route option including the one or more of the localized lane option and the localized path option to a guidance selector of a navigation application of the vehicle for processing.

Growing community of inventors

Sunnyvale, CA, United States of America

Shalini Keshavamurthy