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.

Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A first error value for a boundary of an object, a second error value for a location of the autonomous vehicle, a third error value for a predicted future location of the object may be received. An uncertainty value for the object may be determined by combining the first error value, the second error value, and the third error value. A lateral gap threshold for the object may be determined based on the uncertainty value. The autonomous vehicle may be controlled in an autonomous driving mode based on the lateral gap threshold for the object.

A controller and method for real-time customization of presentation features of a vehicle. A method includes collecting a first dataset about a knowledge level of an operator of the vehicle, wherein the first dataset is collected with respect to a feature of the vehicle; collecting, using at least one sensor, a second dataset regarding an external environment of the vehicle and a cabin of the vehicle; determining, based on the first dataset and the second dataset, a presentation feature from a plurality of presentation features associated with the feature of the vehicle; customizing the presentation feature based on at least the first dataset, wherein the customization is performed in real-time when the operator operates the vehicle; and presenting the presentation feature to the operator of the vehicle.

Path specific rolling resistance is determined in a method including: receiving, from a first source, first rolling resistance data of a first set of road surface portions of a corresponding first set of times; receiving, from a second source, second rolling resistance data of a second set of road surface portions of a corresponding second set of times; determining, using any of the first rolling resistance data and the second rolling resistance data, a path specific rolling resistance for a given path including one or more of the road surface portions selected from the first and second set of road surface portions; and providing at least one target vehicle with the path specific rolling resistance or a derivative of the path specific rolling resistance.

The disclosure is generally directed to remotely-controlled automated vehicle parking operations. A driver of a vehicle stands on a curb and launches a software application on a handheld device. The software application uses a camera of the handheld device to capture images of the vehicle that can be observed by the driver. The software application then attempts to obtain a visual lock between the camera and the vehicle. In some cases, obtaining the visual lock may be hampered due to adverse lighting conditions. Light may be transmitted from the handheld device to illuminate the vehicle for better image capture. Alternatively, a command can be transmitted from the handheld device to a controller in the vehicle for turning on a light in the vehicle. After obtaining visual lock, various techniques such as vehicle path prediction and interpolation, can be used for effectively tracking the vehicle.

A drive control device is applicable to a vehicle including a first motor which drives a first wheel and a second motor which drives a second wheel. The drive control device includes a sensor, a torque setting section, an anomaly detection section, and an information output section. The sensor detects information about the drive control device. The torque setting section sets upper limit values of torques that can be generated by the first motor and the second motor based on the information detected by the sensor. The anomaly detection section detects an anomaly in the drive control device. In response to the anomaly detection section detecting the anomaly, the information output section outputs, to the torque setting section, setting information that sets the upper limit values of the torques of the first motor and the second motor to a common predetermined value, as the information.

An advanced driver assistance system (ADAS) and method for a vehicle include a light intensity detection system configured to detect a set of light density changes proximate to a face of a driver of the vehicle, a vehicle-to-everything (V2X) communication system, a global navigation satellite system (GNSS) system, and a controller configured to detect a current glare condition based on the set of detected light density changes from the light intensity detection system, share, using the V2X communication system, the detected current glare condition and the corresponding GNSS location, based on the sharing and another set of parameters, determine whether the vehicle is likely to experience a future glare condition during a future period, and in response to determining that the vehicle is likely to experience the future glare condition during the future period, at least one remedial action.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

The disclosure is generally directed to an advanced driver assistance system (ADAS) enhancement system. In an example method of operation, a processor of the ADAS enhancement system obtains information about a laden weight of a vehicle, identifies a setting associated with an operation of an ADAS of the vehicle (a braking operation, a cruising speed control operation, a lane maintaining operation, etc.) and modifies the first setting based on the laden weight of the vehicle. For example, the ADAS enhancement system may increase a first braking distance set by the ADAS to a second braking distance system based on the laden weight of the vehicle. As another example, the ADAS enhancement system may change a first level of responsiveness to an action performed by the driver of the vehicle to a second level of responsiveness in proportion to the weight of a cargo item in the vehicle.

A system in a vehicle includes one or more sensors to provide data, the one or more sensors including one or more cameras. The system also includes processing circuitry to obtain sensor information based on the data. The sensor information includes a position of one or more features around the vehicle, the one or more features being stationary objects. The processing circuitry additionally obtains information from messages, fuses the sensor information and the information from the messages, and generates and broadcasts an adaptive safety message based on fusion that does not include a satellite-based position of the vehicle.