Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Other

Ziran Wang

Kyungtae Han

Prashant Tiwari

Rohit Gupta

BaekGyu Kim

Yanbing Wang

Yongkang Liu

Zhenyu Shou

Rui Guo

Hiromitsu Kobayashi

Zhouqiao Zhao

Onur Altintas

John K Lenneman

Miles J Johnson

Sergei S Avedisov

Ahmed Hamdi Sakr

Chase Violetta

Jianyu Su

Toshinori Esaka

Runjia Du

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 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

A speed limiter for a vehicle can be personalized. The speed limiter can be caused to be activated. A user can provide a user input on a user interface. The user input can include a speed limiter personalization setting. In response to the user input, the speed limiter personalization setting can be caused to be implemented.

Personalized speed limiter

A method may include receiving training data comprising a time series of gaps between an ego vehicle and one or more lead vehicles at a plurality of time steps, embedding the training data into a fixed-length sequence, inputting the fixed-length sequence into a Transformer-RNN model comprising a Transformer component and an RNN component, wherein the transformer component applies attention to each data point of the fixed-length sequence based on a fixed number of previous inputs, and training the Transformer-RNN model, using the training data, to output a predicted gap at a future time step based on an input sequence of gaps.

Method and system for personalized car following with transformers and RNNs

In an example, a method determines a reference vehicle traveling in a first lane. The first lane merges with a second lane at a merging point. The method can determine a merging plan for a merging vehicle traveling in the second lane to merge into the first lane based on a vehicle position, and in some cases the speed, of the reference vehicle in the first lane. The method can overlay, via a display device, a virtual target in a field of view of a roadway environment of a driver of the merging vehicle based on the merging plan of the merging vehicle.

Virtualized driver assistance

Systems and methods of cooperative autonomous driving which maximize objectives of a global traffic environment are provided. In particular, a cooperative controller may control multiple connected vehicles within a traffic environment. In certain embodiments, the cooperative controller may assign each connected vehicle a traffic role, based in part on their situation within the traffic environment. In some embodiments, this traffic role may also be based on a 'priority level' for the connected vehicle which corresponds to a desired travel time. Once traffic roles have been assigned, the cooperative controller may control each connected vehicle according to a driving policy associated with its assigned traffic role.

Cooperative artificial intelligent assisted driving

A method comprises determining a vectorized representation of positions of road agents and road geometry based on sensor data from a vehicle, inputting the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network, predicting one or more road agent trajectories at one or more future time steps based on an output of the transformer network, predicting an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps, and causing the vehicle to perform the predicted acceleration at the one or more future time steps.

Method and system for personalized car following with transformers

Systems, methods, and computer program products that are configured to identify or otherwise detect the presence of bacteria, classify the identified or detected bacteria, and also predict the growth of the classified bacteria on various touchable surfaces within a vehicle passenger cabin or compartment. Such systems, methods, and computer program products are configured to identify/detect, classify, and predict the presence and/or growth of bacteria, and transmit one or more alerts, warnings, and/or reports to vehicle owners, service providers, and/or occupants based on the identification/detection, classification, and prediction.

Detection, classification, and prediction of bacteria colony growth in vehicle passenger cabin

A rest stop recommendation system monitors driving behavior of a driver and stores information indicating the driving behavior as historical data, determines, based at least in part on the historical data, a driver tiredness state, a continuous driving length preference, and a refueling pattern preference, determines a time to recommend a rest stop based at least in part on the driver tiredness state, the continuous driving length preference, and the refueling pattern preference, extracts, from a map database, a plurality of rest stops within a predetermined radius of a position of the vehicle, determines rest stop characteristic preferences based at least in part on the historical data, selects one or more potential rest stops from among the plurality of rest stops based at least in part on the rest stop characteristic preferences, and presents the one or more potential rest stops to the driver in a recommendation.

Rest stop recommendation system

Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Systems and methods for predicting driver visual impairment with artificial intelligence

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

Student-T process personalized adaptive cruise control

A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

Hybrid deterministic override of probabilistic advanced driving assistance systems (ADAS)

A cooperative driving system includes a processor and a memory having one or more modules. The modules cause the processor to determine a route that includes a road segment to a destination for an ego vehicle based on (1) an ego vehicle operating parameter that has information regarding a preferred operation of an ego vehicle by an occupant, and (2) a location of a like vehicle with respect to the ego vehicle when the ego vehicle is traveling on at least a portion of the route. The one or more modules also cause the processor to determine a road-segment-level decision for the ego vehicle for the road segment based on the ego vehicle operating parameter, communicate with the like vehicle near the road segment to negotiate the road-segment-level decision to generate a negotiated road-segment-level decision, and maneuver the ego vehicle inside the road segment using the negotiated road-segment-level decision.

Cooperative driving system and method

Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.

Subconscious big picture macro and split second micro decisions ADAS

Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

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San Jose, CA, United States of America

Ziran Wang