Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Other

Toyota Motor Eng & Mfg North America, Inc.

Mcmaster University

Toyoda Jidosha Kabushiki Kaisha

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

Kentaro Oguchi

Takamasa Higuchi

Seyhan Ucar

Baik Hoh

Emrah Akin Sisbot

Rui Guo

Hao Yang

Onur Altintas

Yashar Zeiynali Farid

Xuewei Qi

Preeti Jayagopi Pillai

Shalini Keshavamurthy

Yongkang Liu

Veeraganesh Yalla

Rahul Parundekar

Seyhan Uçar

Abdul Rahman Kreidieh

Haritha Muralidharan

BaekGyu Kim

Hirokazu Nomoto

Roger D Melen

Liu Liu

Yusuke Nakano

Veera Ganesh Yalla

Toru Nakanishi

Eric Martinson

Dustin Harber

Lewis Tseng

Tomohiro Matsuda

Kareem Metwaly

Daisuke Hiroki

Shojiro Takeuchi

Derek Stanley Caveney

Sergei S Avedisov

John F Daly

Adrian Lombard

Satoshi Nagashima

Paul Li

Yutaka Takaoka

David Frank Russell Weir

John Mark Agosta

Kruti Vekaria

Akansel Cosgun

Anup Shrikantrao Deshpande

Joseph Djugash

Ganesh Yalla

Yuta Kataoka

Babajide Ayinde

Hao Sun

Wenjie Luo

Fangying Zhai

Yusuke Hara

Ippei Nishitani

Akio Orii

Kathy Jang

Xiaofei Cao

Emrah A Sisbot

Shripad Gade

A method for detecting objects that are likely to fall from vehicle cargo includes obtaining first data of a cargo of a first vehicle captured at a first time and second data of the cargo captured by a second vehicle in an environment of the first vehicle at a second time after the first time, determining a first distance between two key points of the first data or a first shape constructed by key points of the first data and a second distance between two key points of the second data or a second shape constructed by key points of the second data, and detecting a movement of the cargo based on a comparison of the first distance and the second distance or a comparison of the first shape and the second shape.

Methods and systems for a detection of objects that are likely to fall from vehicle cargo

Systems and methods are provided for programmatically determining traffic management operations in response to conflicts on a roadway. For example, the systems and methods may determine a first traffic management instruction for a first vehicle for a first conflict on a roadway; access characteristics of the first vehicle associated with the first management instruction in response to the first conflict; determine a second conflict occurring after and connected to the first conflict; using the characteristics of the first vehicle, determine a second traffic management instruction for the first vehicle for the second conflict; and provide the second traffic management instruction to the first vehicle.

Traffic management after an initial conflict event

Systems, methods, and non-transitory computer-readable medium for estimating a position of a tail of a vehicle queue are provided. The methods include determining an initial estimated position of a tail of a vehicle queue comprising a plurality of vehicles relative to a beginning of a road section using vehicle sensors, estimating distance between a preceding vehicle and the tail of the vehicle queue based on driving data of the preceding vehicle, estimating a position of the tail based on the estimated distance and distance between an ego vehicle and the preceding vehicle, and estimating a final position of the tail based on the initial estimated position and the estimated position of the tail.

Systems, methods, and non-transitory computer-readable mediums for estimating a position of a tail of a vehicle queue

Systems and methods are provided for generating a real-time lane-level traffic map based on outbound sensor information. In particular, some embodiments generate lane-level traffic maps using radar sensor information. The populated real-time lane-level traffic maps are shared to other vehicles and may be used by autonomous vehicle systems to maneuver the vehicle.

System and method to generate a lane-level traffic map using vehicle sensor data

An example operation includes one or more of directing a group of connected vehicles to occupy one or more lanes approaching an intersection and notifying one of the connected vehicles to exit the group using another of the one or more lanes prior to the intersection.

Extending EV range through connected vehicles

Systems and methods are provided for activating mitigation strategies to reduce or eliminate the stop-and-go traffic. Mitigation strategies can be applied to reduce stop-and-go traffic or prevent a control vehicle from experiencing stop-and-go traffic. The control vehicle can predict when to apply mitigation strategies based on sensor data that can be applied to generate a trajectory for the control vehicle or a preceding vehicle.

Vehicle-based stop-and-go mitigation system

A system for providing information to navigate to a parking space preferred by an operator of a vehicle can include a processor and a memory. The memory can store a preliminary operations module and a communications module. The preliminary operations module can include instructions that cause the processor to obtain, for the operator: (1) a first model for a first duration between a commencement of a parking space identification process and a first threshold and (2) a second model for a second duration between the first threshold and a second threshold. The communications module can include instructions that cause the processor to communicate, to a component of the vehicle for an action, information to navigate to the parking space. The parking space can be identified, in response to a current time being within: (1) the first duration, by the first model and (2) the second duration, by the second model.

Providing information to navigate to a parking space preferred by an operator of a vehicle

Systems, methods, and other embodiments described herein relate to estimating the origins of abnormal driving through observed driving patterns. In one embodiment, a method includes detecting, using parallel computations, abnormal classifications associated with a subject vehicle and nearby vehicles according to driving patterns derived from observation data, the abnormal classifications being associated with exceeding a position range in the observation data. The method also includes estimating an origin of abnormal driving with happens-before analysis according to the abnormal classifications, and the abnormal driving is associated with deviations in a traffic flow associated with the subject vehicle and the nearby vehicles. The method also includes controlling the subject vehicle using a driving command according to the origin.

Systems and methods for estimating the origins of abnormal driving

Systems, methods, and other embodiments described herein relate to improving identification of available parking spaces in congested zones. In one embodiment, a method includes, responsive to receiving trajectory information about movement of a searching vehicle, estimating a search time spent by the vehicle seeking parking in a location. The method includes aggregating, when the search time satisfies a time threshold, the search time with observed times associated with additional entities parking in the location. The method includes updating a parking map according to the observed times when the observed times satisfy a parking threshold indicating a lack of available parking in the location. The parking map identifies active regions where parking is presently lacking. The method includes providing the parking map with the active regions to identified vehicles proximate to locations associated with the active regions.

Systems and methods for activation of intelligent parking availability functions

A method according to some embodiments includes sensing, by a sensor set of the ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method further includes classifying a type of the remote vehicle based on the sensor data. The method further includes retrieving a behavior profile for the type of the remote vehicle that identifies criteria for the type of the remote vehicle and classifications of abnormal behavior and normal behavior based on the criteria. The method further includes comparing the behavior of the remote vehicle to the behavior profile for the type of the remote vehicle. The method further includes determining that the behavior of the remote vehicle is normal based on the comparing.

Detection of abnormal driving based on behavior profiles

The disclosure includes embodiments for a phantom system. A method according to some embodiments is executed by an onboard vehicle computer. The method includes generating a model of a driving behavior of a target vehicle based on sensor data describing the driving behavior and a roadway environment. The method also includes determining a graphic of a phantom vehicle based on the model. The method also includes causing an electronic display of an ego vehicle to depict the graphic as an element of a graphical user interface that depicts the graphic and an image of a remote vehicle that is following the ego vehicle. For example, the method includes generating graphical data describing the graphical user interface including the graphic and the image and providing the graphical data to the electronic display to cause the electronic display to depict the graphical user interface including the graphic and the image.

Generating and depicting a graphic of a phantom vehicle

System, methods, and other embodiments described herein relate to predicting trajectories of multiple vehicles using graphs and multiple decoding models. In one embodiment, a method includes computing, using an encoding model, a graph having a geographic map and vehicle features associated with a plurality of vehicles in an area according to characteristics, prior trajectories, and spatiotemporal interactions. The method also includes processing, using the encoding model, updates for the geographic map and the vehicle features separately in association with encoded features of neighboring vehicles. The method also includes decoding, using a probability model and a regression model, the geographic map and the vehicle features to output estimated trajectories for the plurality of vehicles.

Systems and methods for predicting trajectories of multiple vehicles

Systems and methods are provided for detecting when a vehicle engages in anomalous driving behavior and managing nearby vehicles to mitigate risk to the nearby vehicles due to the anomalous driving behavior. According to some embodiments, the methods and systems comprise receiving an indication that an anomalous driving behavior of a first vehicle is detected, and identifying a plurality of vehicles within a geographic area of the first vehicle based on receiving the indication. The methods and systems further include generating an arrangement for the plurality of vehicles based on the detected anomalous driving behavior, determining a target speed for each of the plurality of vehicles based on the determined arrangement and a current speed of each of the plurality of vehicles, and communicating control messages to each of the plurality of vehicles. The control messages comprising the target speed to distribute the plurality of vehicles into the determined arrangement.

Systems and methods to manage drivers under abnormal driving

Systems and methods are provided for estimating the energy range of an electric vehicle, including updating an energy consumption estimation model based on the location of a vehicle or based on the current time. One embodiment comprises obtaining a route of a vehicle and deploying an energy consumption estimation model to provide an energy consumption estimate along the route from a first location to a destination. In response to determining that the vehicle enters a subsequent location, the energy consumption estimation model is updated with a location model received from an edge/cloud server, to estimate an energy range along the route through the subsequent location.

Customized electrical vehicle range estimation system based on location and driver

A system for determining an identification of an occupant within a vehicle can include a processor, a communications interface, and a memory. The memory can store an information reception module, an identification prediction module, and an actuation module. The information reception module can cause the processor to receive, via the communications interface, from at least one sensor, and at a time when a source of a propulsion force for the vehicle is in an off state, at least one signal having information about at least one detectable characteristic associated with the occupant within the vehicle. The identification prediction module can cause the processor to produce, based on the information, a prediction of the identification of the occupant. The actuation module can cause the processor to cause, in response to a production of the prediction, a setting of a component of the vehicle to have a specific value.

Determining an identification of an occupant within a vehicle

Growing community of inventors

Menlo Park, CA, United States of America

Kentaro Oguchi