A driving assistance system includes a front camera module (FCM). The system, responsive to processing captured image data from the FCM, generates FCM control signals. The system includes a plurality of vehicle sensors capturing sensor data and an advanced driving-assistance system (ADAS) controller. The ADAS controller, responsive to processing the sensor data, generates ADAS control signals. The ADAS controller generates an ADAS status signal indicating a reliability of the generated ADAS control signals. With the ADAS status signal indicating the reliability of the generated ADAS control signals is at or above a threshold ADAS reliability level, the system controls the vehicle using the ADAS control signal and, responsive to determining that the ADAS status signal indicates that the reliability of the generated ADAS control signals is below the threshold, switches from controlling the vehicle based on the ADAS control signals to controlling the vehicle based on the FCM control signals.

The present disclosure provides a method and system to operationalize driver eye movement data analysis based on moving objects of interest. Correlation and/or regression analyses between indirect (e.g., eye-tracking) and/or direct measures of driver awareness may identify variables that feature spatial and/or temporal aspects of driver eye glance behavior relative to objects of interest. The proposed systems and methods may be further combined with computer-vision techniques such as object recognition to (e.g., fully) automate eye movement data processing as well as machine learning approaches to improve the accuracy of driver awareness estimation.

A method for operating an at least partially automated vehicle. The method includes: supplying surroundings data detected with the aid of sensors to at least three AI computing units which are independent of one another; generating data regarding at least one object from the surroundings data; carrying out a plausibility check of the pieces of data with respect to one another with the aid of majority voting; and using the data for which the plausibility check has been carried out for controlling, at least in a semi-automated manner, a lateral and/or longitudinal guidance of the at least partially automated vehicle.

Various embodiments include methods and devices for training and implementing a tree-based behavior prediction model for use in autonomous vehicle control systems. Some embodiments may include labeling real-world autonomous vehicle run data to indicate an insight of the data, selecting an insight decision tree of the tree-based behavior prediction model for training using the labeled data, training the insight decision tree using the labeled data to classify a probability of an insight associated with the insight decision tree, and updating the tree-based behavior prediction model based on training the insight decision tree. Some embodiments may include selecting an insight decision tree of a tree-based behavior prediction model configured for classifying a probability of an insight associated with the insight decision tree, executing the insight decision tree, and outputting a probability of an insight determined from executing the insight decision tree using the data.

An information collection system includes a control device installed at a vehicle, a collection device that collects obstacle information from a plurality of the control devices, and a relay device that relays communications between the control device and the collection device. A first processor of the control device calculates a degree of confidence that an obstacle as included in a captured image captured by an image capture device provided at the vehicle, and transmits image information related to the captured image, position information added to the captured image, and the degree of confidence, to the relay device. A second processor of the relay device receives the image information, the position information, and the degree of confidence, from the control devices, and identifies an obstacle included in a captured image according to the image information in descending order of the degree of confidence. If an obstacle has been identified, the second processor transmits text information relating to the position of the identified obstacle and the content of the identified obstacle to the collection device. A third processor of the collection device receives the text information from one or more of the relay devices and stores the text information corresponding to the identified obstacle.

A method and device for operating a vehicle comprising a step of recording environment data values, which represent an environment of the vehicle, the environment comprising at least one environmental feature; a step of determining a comparative value of a comparison between the at least one environmental feature and a map, the map comprising at least one map feature, the at least one environmental feature corresponding the at least one map feature; a step of determining an up-to-dateness of the map, based on a comparison of the comparative value with a threshold value; and a step of operating the vehicle, as a function of the up-to-dateness of the map.

A control device for automated valet parking lot includes: a travel route determining unit configured to determine a travel route to a parking space; a travel route transmission unit configured to transmit the travel route to a vehicle; a position acquiring unit configured to acquire, from the vehicle, a position of the vehicle that is estimated by the vehicle while traveling; and a positional accuracy calculation unit configured to calculate a dynamic estimation accuracy, which is an accuracy of the position of the vehicle acquired by the position acquiring unit from the vehicle while the vehicle is travelling, prior to performing automated driving of the vehicle in accordance with the travel route.

Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.

A method for controlling a subject vehicle with a braking system, a drive system and a distance control system, wherein the distance control system is configured to control an actual following distance between the subject vehicle and a vehicle ahead to a predetermined target following distance, wherein the target following distance is predetermined as a function of an activated operating mode of the distance control system, including performing a plausibility check by checking whether V2X data is exchanged or can be exchanged between the vehicle ahead and the subject vehicle over a V2X connection, and activating, if no V2X data is exchanged or can be exchanged between the vehicle ahead and the subject vehicle over the V2X connection, a first operating mode in which a first target following distance is predetermined as a function of a reaction time of a driver of the subject vehicle.

In a parking assistance device for moving a vehicle from a current location to a scheduled parking location and parking the vehicle at the scheduled parking location, parking information indicated by a parking assistance code includes obstacle information which is information about obstacles in a parking area, and target location information which is information about a target parking location. A route generation unit is configured to generate a parking route from a current location to a scheduled parking location that is set to the target parking location indicated by the target location information, while avoiding the obstacles indicated by the obstacle information.