Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Rohit Gupta

Kyungtae Han

Ziran Wang

Prashant Tiwari

Yanbing Wang

Benjamin P Austin

William Patrick Garrett

Amr Abdelraouf

Philip Joseph Babian

John F Daly

Adrian Lombard

Satoshi Nagashima

Paul Li

Rebecca L Kirschweng

Zhouqiao Zhao

Ali C Eren

Roger D Melen

Akila C Ganlath

Dawei Chen

Nejib Ammar

Qi Chen

Hazem Abdelkawy

Runjia Du

Dianne O Tykoski

A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.

Personalized vehicle lane change maneuver prediction

A method may include learning reward functions for a plurality of first vehicles based on first vehicle data associated with the plurality of first vehicles using inverse reinforcement learning, associating each vehicle of the plurality of first vehicles with one cluster among a plurality of clusters based on the reward functions, determining a centroid reward function for each of the clusters based on the reward functions associated with each cluster, performing a comparison between second vehicle data associated with a second vehicle and the first vehicle data, determining a vehicle among the plurality of first vehicles having associated first vehicle data that is most similar to the second vehicle data based on the comparison, associating the second vehicle with the cluster associated with the determined vehicle, and controlling operation of the second vehicle based on the centroid reward function of the cluster associated with the second vehicle.

Method and system for modeling personalized car-following driving styles with model-free inverse reinforcement learning

An example operation includes one or more of determining an event has occurred at a location, responsive to a level of the event being above a threshold, determining vehicles heading toward the location that can display information on an exterior portion of the vehicles, requesting to display the information on the vehicles, and responsive of an approval to the requesting, sending the information to the vehicles to display.

Authorization for vehicle display

Systems, methods, and other embodiments described herein relate to identifying and assisting pedestrians identified as experiencing impaired decision-making capabilities. In one embodiment, a method includes determining, from interaction data collected by a user device of a pedestrian, an interaction characteristic of the pedestrian. The method also includes classifying the pedestrian as in an impaired decision-making state based on the interaction characteristic of the pedestrian deviating from baseline interaction data. The method further includes producing a pedestrian assistance countermeasure responsive to a determined impaired decision-making state for the pedestrian.

Systems and methods for detecting impaired decision-making pedestrians

Systems, methods, and other embodiments described herein relate to superimposing computer-generated exterior vehicle lights over asynchronous real-world exterior vehicle lights to reduce a sensory overload of a vehicle driver. In one embodiment, a method includes assessing a sensory environment of a vehicle driver. The method also includes determining that the vehicle driver is in a sensory-overloaded state based on 1) a characteristic of the sensory environment, 2) physiological sensor data for the vehicle driver, and 3) driving behavior sensor data. The method also includes superimposing an augmented reality (AR) overlay of computer-generated synchronized exterior vehicle lights over asynchronous real-world exterior vehicle lights viewed through an AR display device of the vehicle based on a determination that the vehicle driver is in the sensory-overloaded state.

Systems and methods for superimposing augmented reality vehicle lights in a vehicle display

Systems and methods are provided for learning a driving behavior during a cut-in cut-out event or other triggering event to create a profile that adjusts operation of the adaptive cruise control (ACC) component of the vehicle to mimic the preferences of the driver. The profile may be based on data collected during a previous cut-in cut-out event or other triggering event. The data may be transmitted to an adaptive cruise control system that uses the data as input to a machine learning model. Output of the machine learning model may update the profile for the driver that operates the vehicle in ACC during the cut-in cut-out event. When the vehicle is operating in ACC and an event is within a threshold value of the cut-in cut-out event occurs at a later time, the vehicle may apply rules defined in the profile.

Trigger event personalized adaptive cruise control (P-ACC)

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

An example operation includes one or more of receiving position data of an ego vehicle and of one or more surrounding vehicles of the ego vehicle via sensors of the ego vehicle while the ego vehicle and the surrounding vehicles are travelling along a road, predicting, via a machine learning model, a trajectory of the ego vehicle and trajectories of the one or more surrounding vehicles based on the position data, generating a routing instruction for the ego vehicle based on the predicted trajectory of the ego vehicle and the predicted trajectories of the one or more surrounding vehicles, and transmitting the routing instruction to a display associated with the ego vehicle.

Lane change assist for inexperienced driver

Embodiments of methods and systems include determining a driving maneuver to be performed by an ego vehicle based on a road geometry and a lane geometry, the ego vehicle surrounded by one or more surrounding vehicles, retrieving a trajectory model corresponding to the driving maneuver, encoding kinematic data of the surrounding vehicles relative to the ego vehicle to generate spatial relationships between the surrounding vehicles and the ego vehicle, predicting future trajectories of the surrounding vehicles based on the spatial relationships, the kinematic data, the road geometry, and the lane geometry, generating a collision coefficient based on the future trajectories and the driving maneuver, and when the collision coefficient is greater than a predetermined threshold value, determining an escape route for the ego vehicle, and causing the ego vehicle to autonomously operate based on the escape route.

Methods and systems for collision control of moving vehicles surrounded by vehicles

An active road surface maintenance system and method developed for connected vehicles with the aid of a mobility digital twin (MDT) framework. A method performed in a cloud-based digital space includes receiving data regarding a physical object from a physical space connected to a vehicle. The method also includes processing the data using machine learning to model road surface conditions, in which respective penalty values are assigned to corresponding road surfaces, a respective penalty value being higher the lower a condition of the corresponding road surface. The method also includes deriving instructions based on the modeled road surface conditions and the respective penalty values to guide actuation of the vehicle along a trajectory. The method further includes transmitting the instructions to the physical space connected to the vehicle to guide actuation of the vehicle.

Systems and methods for active road surface maintenance with cloud-based mobility digital twin

Systems, methods, and other embodiments described herein relate to improving the communication of a railroad crossing warning. In one embodiment, a method includes identifying a railroad crossing in a path of a road user. The method further includes in response to determining that a train is approaching the road user at the railroad crossing, determining whether a warning mechanism of the railroad crossing satisfies a crossing threshold that is based, at least in part, on a safety condition of the warning mechanism. Moreover, the method includes controlling a display of the road user to output a virtual railroad crossing bar on the display according to the safety condition responsive to determining that the warning mechanism satisfies the crossing threshold.

Providing a virtual railroad crossing warning

Systems, methods, and other embodiments described herein relate to synchronizing the presentation of vehicle exterior lights to reduce the sensory overload of a pedestrian. In one embodiment, a method includes assessing a sensory environment of a pedestrian. The method also includes determining that the pedestrian is in a sensory-overloaded state based on a characteristic of the sensory environment and sensor data indicative of a pedestrian state of mind. The method also includes synchronizing a presentation of a first vehicle exterior light and a presentation of a second vehicle exterior light based on a determination that the pedestrian is in the sensory-overloaded state.

Systems and methods for synchronizing the presentation of vehicle exterior lights

Systems and methods for selecting an edge node include a processor operable to send a request of bidding to edge nodes regarding performing a task for an inquiry vehicle, receive one or more bids from one or more bidding edge nodes of the edge nodes, wherein each bid includes a resource allocation offer and a price, calculate a preference value based on a weighted sum of preference factors for each bidding edge node, wherein the preference factors are determined based on the resource allocation offer, and the weight assigned to each preference factor is determined based on the task, select a performing edge node based on the preference values and the prices, and operate the inquiry vehicle based on a result of the task performed by the performing edge node.

Systems and methods for selecting edge nodes for task performance

System, methods, and other embodiments described herein relate to improving communication of user intent at a road crossing. In one embodiment, a method includes acquiring sensor data identifying a blood flow in a brain of a user, determining whether the user intends on crossing a street based, at least in part, on the sensor data, and in response to determining the user intends on crossing the street, communicating an intent to cross using the accessory.

Smart accessory for communicating an intention to cross a street

An example operation includes one or more of receiving an opt-in request to display information on a display of a vehicle configured to be viewed from outside of the vehicle, authenticating the request to allow displaying information on the display, determining information associated with the vehicle to be displayed on the display, determining misuse of the information displayed on the display based on a notification from another vehicle, and flagging the misuse.

Growing community of inventors

Santa Clara, CA, United States of America

Rohit Gupta