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 Engineering & Manufacturing N.a. Inc.

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

Kentaro Oguchi

Takamasa Higuchi

Seyhan Ucar

Emrah Akin Sisbot

Baik Hoh

Rui Guo

Hao Yang

Onur Altintas

Yashar Zeiynali Farid

Xuewei Qi

Yongkang Liu

Preeti Jayagopi Pillai

Shalini Keshavamurthy

Veeraganesh Yalla

Rahul Parundekar

Seyhan Uçar

Haritha Muralidharan

Abdul Rahman Kreidieh

BaekGyu Kim

Hirokazu Nomoto

Tomohiro Matsuda

Roger D Melen

Sergei S Avedisov

Liu Liu

Yusuke Nakano

Veera Ganesh Yalla

Eric Martinson

Toru Nakanishi

Dustin Harber

Lewis Tseng

Kareem Metwaly

Xiaofei Cao

Daisuke Hiroki

Derek Stanley Caveney

Shojiro Takeuchi

Divya Sai Toopran

John F Daly

Paul Li

Adrian Lombard

Satoshi Nagashima

David Frank Russell Weir

Yutaka Takaoka

John Mark Agosta

Kruti Vekaria

Akansel Cosgun

Yusuke Hara

Hao Sun

Akio Orii

Kathy Jang

Emrah A Sisbot

Ippei Nishitani

Yuta Kataoka

Ganesh Yalla

Joseph Djugash

Anup Shrikantrao Deshpande

Shripad Gade

Fangying Zhai

Wenjie Luo

Babajide Ayinde

A method may include receiving driving data from a plurality of vehicles within a sampling region. The driving data may include speeds, positions, and lane change activity of the plurality of vehicles. The method may further include determining speeds of the plurality of vehicles based on the driving data. The method may further include performing cluster analysis on the speeds of the plurality of vehicles. The method may further include determining whether the speeds of the plurality of vehicles comprise more than one cluster based on the cluster analysis. Upon determination that the speeds of the plurality of vehicles comprise more than one cluster, the method may include determining that lane-level traffic is present within the sampling region.

Detecting lane-level traffic using connected vehicle data without lane identification information

Systems and methods are provided for correcting vehicle position estimations are provided. In an example implementation, the systems and methods disclosed herein detect a first landmark and determining a first position of the detected first landmark in a first section of an environment using one or more sensors of a vehicle while the vehicle travels in the first section, estimate a position of the vehicle in a second section of the environment, and update the estimated position of the vehicle in the second section according to the first position of the detected first landmark in the first section of the environment. In an illustrative example, the environment can be an indoor environment, such as, but not limited to, a multi-floor parking structure where the first section a floor and the second section is another floor.

Systems and methods for correcting vehicle position estimations based on landmarks detected in an environment

Systems and methods described herein relate to configuring a hierarchical traffic management system. One embodiment receives, at a cloud server, a request from an application, the request including requirements and implicating configuration of a hierarchical traffic management system; determines, based on information received from one or more connected vehicles in an area of interest pertaining to the request, a minimum density of connected vehicles in the area of interest to meet the requirements; calculates an optimum number of connected vehicles to assign to a section manager in the hierarchical traffic management system; calculates the size of a section of the roadway to be managed by the section manager in the area of interest based, at least in part, on the minimum density of connected vehicles and the optimum number of connected vehicles; and activates the hierarchical traffic management system in accordance with the calculated size of the section.

Systems and methods for configuring a hierarchical traffic management system

A method for planning lane-change actions considering string stability in a target lane is provided. The method includes learning a car following model based on training data related to a region, obtaining information about vehicles in a target lane in the region, simulating movements of the vehicles in the target lane responsive to an ego vehicle moving into the target lane based on the learned car following model and the information about the vehicles, determining whether the vehicles in the target lane will be string stable responsive to the ego vehicle moving into the target lane based on the simulated movements of the vehicles in the target lane, and instructing the ego vehicle to change lanes from a current lane to the target lane in response to determining that the vehicles in the target lane will be string stable responsive to the ego vehicle moving into the target lane.

Systems and methods for planning lane-change actions considering traffic stability in target lane

A parking assist system for a vehicle includes a processor and a memory communicably coupled to the processor. The memory stores instructions that when executed by the processor cause the processor to detect a parking space for a vehicle. The instructions further cause the processor to identify one or more parking parameters related to parking the vehicle in the parking space. The parking parameters include a point-of-interest associated with the parking space. The instructions further cause the processor to determine a suitability of the parking space based on the one or more parking parameters. Determining the suitability of the parking space includes predicting a point-of-interest-related action to be performed by an occupant after parking the vehicle in the parking space.

Parking assist system

Systems and methods are provided for programmatically verifying an origin of abnormal driving. Some examples of abnormal driving may include aggressive driving (e.g., tailgating, cut-in lane, etc.), distracted driving (e.g., swerving, delayed reaction, etc.), and reckless driving (e.g., green light running, lane change without signaling, etc.). For example, the systems and methods may receive an identification of a second vehicle performing abnormal driving from an ego vehicle; initiate a verification process of the identification of the abnormal driving; access driving data associated with an origin of the abnormal driving, wherein the driving data includes the ego vehicle and the second vehicle; using the driving data, determine a confirm or deny decision regarding the identification of the second vehicle from the ego vehicle; and provide the confirm or deny decision.

Verification of the origin of abnormal driving

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

Growing community of inventors

Menlo Park, CA, United States of America

Kentaro Oguchi