A method for performing an at least partially automatic vehicle function of a vehicle depending on a travel route to be assessed by means of a driver assistance system is disclosed. The method comprises providing a plurality of clusters from route data with respect to at least one known travel route, wherein the clusters group the route data sectionwise according to predefined geometric parameters. The method comprises providing recorded course data that indicate a course of the travel route to be assessed and applying the clusters to the course data in order to divide the travel route to be assessed into route sections corresponding to the clusters. The method comprises determining at least one uncertainty quantity which is characteristic of an uncertainty with respect to the assignment made and determining a control quantity as a function of the uncertainty quantity and providing the control quantity for performing the vehicle function.

A vehicle includes a front camera, a front radar, a corner radar, a corner LiDAR, and a controller configured to generate a first fusion mode by processing image data and radar data or to generate a second fusion mode by processing radar data and LiDAR data, wherein the controller changes the first fusion mode to the second fusion mode when the controller detects an abnormality of the front camera while performing avoidance control of the vehicle based on the first fusion mode, and performs the avoidance control based on the second fusion mode for a predetermined time period.

A vehicle includes a front camera, a front radar, a corner radar, a corner LiDAR, and a controller configured to generate a first fusion mode by processing image data and radar data or to generate a second fusion mode by processing radar data and LiDAR data, wherein the controller changes the first fusion mode to the second fusion mode when the controller detects an abnormality of the front camera while performing avoidance control of the vehicle based on the first fusion mode, and performs the avoidance control based on the second fusion mode for a predetermined time period.

A method for determining the reliability of detected and/or tracked objects for use in the driver assistance or the at least semiautomated driving of a vehicle. The method includes: a) receiving sensor data for the detected objects from a plurality of sensors, b) associating pieces of object existence information with each object, c) checking, considering, or representing redundancy of pieces of object existence information.

A method for determining the reliability of detected and/or tracked objects for use in the driver assistance or the at least semiautomated driving of a vehicle. The method includes: a) receiving sensor data for the detected objects from a plurality of sensors, b) associating pieces of object existence information with each object, c) checking, considering, or representing redundancy of pieces of object existence information.

Autonomous vehicles are an exciting prospect to the future of driving. However, concerns about the decision-making made by the AI controlling a vehicle has been of concern, particularly in light of high-profile accidents. We can alleviate some concern, introduce better decisions, and also train an AI to make better decisions by introducing a remote viewer's, e.g., a human's, reaction to a possibly complex environment surrounding a vehicle that includes a potential threat to the vehicle. One or more remote viewer may provide a recommended response to the threat that may be incorporated in whole or in part in how the vehicle reacts. Various ways to engage and utilize remote viewers are proposed to improve the likelihood of receiving useful recommendations, including modifying how the environment is presented to a remote viewer to best suit the remote viewer, e.g., perhaps present the threat in a game.

Autonomous vehicles are an exciting prospect to the future of driving. However, concerns about the decision-making made by the AI controlling a vehicle has been of concern, particularly in light of high-profile accidents. We can alleviate some concern, introduce better decisions, and also train an AI to make better decisions by introducing a remote viewer's, e.g., a human's, reaction to a possibly complex environment surrounding a vehicle that includes a potential threat to the vehicle. One or more remote viewer may provide a recommended response to the threat that may be incorporated in whole or in part in how the vehicle reacts. Various ways to engage and utilize remote viewers are proposed to improve the likelihood of receiving useful recommendations, including modifying how the environment is presented to a remote viewer to best suit the remote viewer, e.g., perhaps present the threat in a game.

A system for opportunistic imaging includes an imaging device that generates data comprising a plurality of sequential image frames. The system also includes a control module that causes the imaging device to generate standard-resolution image frames at a standard-frame rate and a standard-resolution, such that there is a processing time lull between the generation of sequential standard-resolution image frames. The control module also causes the imaging device to generate one or more burst-resolution image frames at a burst-frame rate and a burst-resolution within the processing time lull.

A system for opportunistic imaging includes an imaging device that generates data comprising a plurality of sequential image frames. The system also includes a control module that causes the imaging device to generate standard-resolution image frames at a standard-frame rate and a standard-resolution, such that there is a processing time lull between the generation of sequential standard-resolution image frames. The control module also causes the imaging device to generate one or more burst-resolution image frames at a burst-frame rate and a burst-resolution within the processing time lull.

Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle, based on anomalies detected between driver behavior and environment captured by the outward facing camera. Various aspects of the driver, which may include his direction of sight, point of focus, posture, gaze, is determined by image processing of the upper visible body of the driver, by a driver facing camera in the vehicle. Other aspects of environment around the vehicle captured by the multitude of cameras in the vehicle are used to correlate driver behavior and actions with what is happening outside to detect and warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.