Techniques are described for dynamically selecting vehicles to perform road friction probing maneuvers and estimating road friction based on sensor data collected while a vehicle performs the road friction probing maneuvers. In one example, a computing system is configured to select, from a plurality of vehicles, based on an amount of elapsed time since each respective vehicle of the plurality of vehicles has performed a road friction probing maneuver, a vehicle to perform the road friction probing maneuver within a road segment of a roadway, and responsive to selecting the vehicle, output, to the vehicle, a command causing the vehicle to perform the road friction probing maneuver within the road segment.

Systems, apparatuses, and methods disclosed provide for receiving internal information, external static information, and external dynamic information of a hybrid vehicle, and selectively enable or disable a stop/start function for the engine of the hybrid vehicle based on the internal hybrid vehicle information, external static information, and external dynamic information. The stop/start function controls selective activation and deactivation of the engine during operation of the hybrid vehicle.

The invention relates to a detection device (4) for a motor vehicle (1) for detecting a distance (x1) of an object (O1) in a surrounding region (5) of the motor vehicle (1) from the motor vehicle (1), comprising an emitting unit (8), which is designed to emit a light beam (9) and to scan the surrounding region (5) by orienting the light beam (9) along predetermined emission angles (10), and comprising a receiving unit (11) having at least two receiving elements (16), which are designed to receive a part (12) of the light beam (9) reflected on the object (O1), to detect the distance (x1) on the basis of a duration between the emission of the light beam (9) and the reception of the reflected part (12) of the light beam (9), and to detect a reception angle (13), at which the reflected part (12) of the light beam (9) from the surrounding region (5) is incident on the receiving unit (11), wherein the receiving unit (11) is designed to detect a deviation (17) between the emission angle (10) of the light beam (9) and the reception angle (13) of the reflected part (12) of the light beam (9) corresponding to the emission angle (10). The invention additionally relates to a driver assistance system (2), a motor vehicle (1), and a method for detecting a distance (x1) of an object (O1) in a surrounding region (5) of a motor vehicle (1).

The described techniques relate to coordinating and managing faults of systems of a vehicle, such as an autonomous vehicle, to enable the vehicle to respond safely and appropriately to the faults. In examples, a centralized fault monitor system receives faults from different vehicle systems, maps the received faults to associated fault constraints, prioritizes different and shared parameters between the fault constraints, and communicates the constraint parameters to appropriate vehicle systems accordingly.

Examples relating to vehicle velocity calculation using radar technology are described. An example method performed by a computing system may involve, while a vehicle is moving on a road, receiving, from two or more radar sensors mounted at different locations on the vehicle, radar data representative of an environment of the vehicle. The method may involve, based on the data, detecting at least one scatterer in the environment. The method may involve making a determination of a likelihood that the at least one scatterer is stationary with respect to the vehicle. The method may involve, based on the determination being that the likelihood is at least equal to a predefined confidence threshold, calculating a velocity of the vehicle based on the data from the sensors. The calculated velocity may include an angular and linear velocity. Further, the method may involve controlling the vehicle based on the calculated velocity.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

Vehicle having dangerous situation notification function and control method, where the method includes monitoring a vicinity of a host vehicle, detecting a movable object in the vicinity of the host vehicle, predicting a dangerous situation in which a collision between the host vehicle and the object is expected, and displaying a warning mark on at least one of a roadway or a wall surface in a projection mapping method in response to the dangerous situation. One or more of an autonomous vehicle, a user terminal, and a server may be in conjunction with an Artificial Intelligence (AI) module, an Unmanned Aerial Vehicle (UAV), an Augmented Reality (AR) device, a Virtual Reality (VR) device, and a device related to a 5G service, etc.

A vehicle includes an electric machine that generates torque to move wheels of the vehicle, and a controller. The controller operates the electric machine to limit a maximum speed at which the vehicle is driven in reverse such that the maximum speed depends on a number of detected objects behind the vehicle.

A system and method are described for controlling a vehicle interior environmental condition. A biometric sensor senses a biometric condition of a vehicle seat occupant and generates a sensed biometric condition value. A controller receives the sensed biometric condition value, a sensed interior environmental condition value, and a sensed exterior environmental condition value. Each of multiple exterior environmental condition values has an associated biometric condition value defined as optimal for the vehicle occupant. The controller determines the optimal biometric condition value associated with the sensed exterior environmental condition value, compares the optimal biometric condition value to the sensed biometric condition value, and in response to a difference between the optimal biometric condition value and the sensed biometric condition value, generates a control signal to control an actuator to control the controllable interior environmental condition to reduce the difference between sensed biometric condition value and the optimal biometric condition value.

Sensor data is received from an array of sensors configured to capture one or more objects of an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on vehicle operation data representative of a state of the autonomous vehicle. First sensor data from the first group is prioritized for transmission based on the vehicle operation data.