Toyota Motor Engineering & Manufacturing North America, Inc.

Yanbing Wang

Prashant Tiwari

Kyungtae Han

Ziran Wang

Rohit Gupta

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 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)

Systems, methods, and other embodiments described herein relate to generating a notification about a risk of a target vehicle toppling over due to wind. In one embodiment, a method includes determining a wind force of the wind at a location of the target vehicle and determining one or more characteristics of the target vehicle. The method includes determining whether there is a risk of the target vehicle toppling over based on the wind force of the wind and the one or more characteristics of the target vehicle. The method includes generating a notification about the risk.

Vehicular topple risk notification

Systems and methods for protecting a vehicle at an intersection are disclosed herein. One embodiment detects that the vehicle is stopped at the intersection at one of a first position and a second position; detects that a driver of the vehicle is pressing on an accelerator pedal of the vehicle; delays acceleration of the vehicle automatically by a first predetermined period, when the vehicle has been detected at the first position; and delays acceleration of the vehicle automatically by a second predetermined period, when the vehicle has been detected at the second position. Delaying acceleration of the vehicle automatically prevents the vehicle from being struck by a cross-traffic vehicle that has proceeded through the intersection against a first traffic signal in a red-light state.

Systems and methods for protecting a vehicle at an intersection

Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

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Nashville, TN, United States of America

Yanbing Wang