An example method of providing an at least partially automatic driving function and/or a personalized function in a motor vehicle as well as to the motor vehicle may include establishing a communication link between a communication interface of the motor vehicle and a communication interface of a mobile terminal, which is associated with a user of the motor vehicle; receiving terminal data transferred via the communication link in the motor vehicle, which is recorded on the mobile terminal and which describes at least navigation information and/or user information relating to the user; and providing an at least partially automatic driving function of a driver assistance system of the motor vehicle and/or a personalized function in the motor vehicle by evaluating the received terminal data.

A human machine interface control unit as an information presentation control device is used in a vehicle having an autonomous driving function to perform a driving action on behalf of a driver, and controls an information presentation device configured to present information to the driver. The human machine interface control unit includes a permissible action determination unit configured to determine a permissible action that a driver is permitted to take among actions, other than a driving action, to be possibly taken by the driver when the autonomous driving function is implemented, and an information presentation content control unit configured to cause an information presentation device to present information related to a determination result of the permissible action.

The present disclosure relates to navigation and to systems and methods for using a dual sensor readout channel to allow for frequency detection. In one implementation, at least one processing device may receive a plurality of images acquired by a camera onboard a host vehicle, wherein the plurality of images are received via a first channel and via a second channel, and wherein the first channel is associated with a first frame capture rate, and the second channel is associated with a second frame capture rate different from the first frame capture rate. The processing device may use images received via the first channel to detect flickering and non-flickering light sources in an environment of the host vehicle; and provide, based on images received via the second channel, images for showing on one or more human-viewable displays.

Aspects relate to methods and systems for controlling usage of parking maps for autonomous parking. In some embodiments, an exemplary system includes a computing device, configured to receive a first parking map representative of a first point of interest, wherein the first parking map comprises a first access datum, generate a first map metric associated with the first parking map, and selectively communicate the first parking map with a first remote device as a function of the first access datum.

The disclosed technology provides solutions for validating operation of a sensor assembly by performing an assembly test. In some aspects, a process of performing the assembly test includes steps for collecting motor controller measurements, wherein the motor controller measurements include an amount of current supplied to a motor coupled when performing a sensor sweep, calculating an average current drawn by the motor based on the current measurements, and calculating a peak current drawn by the motor based on the current measurements. In some aspects, the process can further include steps for determining if the sensor assembly passes the sensor assembly test based on the average current drawn and the peak current drawn. Systems and machine-readable media are also provided.

In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

A route guidance apparatus includes: a reservation information communications unit to receive reservation information which includes at least one of a parking-reserved space of the parking lot, a vehicle type, a vehicle number, and a parking time; a reserved vehicle recognition unit to recognize a vehicle number of a reserved vehicle and determine whether the reserved vehicle enters or exits the parking lot; a traffic flow determination unit that divides an area of each floor of the parking lot into a plurality of unit areas and calculates density within the parking lot based on the number of vehicles within each divided unit area; and a parking route generation unit and an exit route generation unit that respectively generate an optimal parking route and an optimal exit route based on the calculated density; and a route guidance unit to provide the user with the generated optimal parking and exit routes.

Apparatuses, systems, and techniques to place one or more objects in a location and orientation. In at least one embodiment, one or more circuits are to use one or more neural networks to cause one or more autonomous devices to place one or more objects in a location and orientation based, at least in part, on one or more images of the location and orientation.

A method is for training an object detector configured to detect objects in sensor data of a sensor. The method includes providing first sensor data of the sensor, providing an object representation assigned to the first sensor data, and transmitting the object representation to a sensor model. The method further includes imaging object representations onto the first sensor data of the sensor with the sensor model, assigning the object representation to second sensor data with the sensor model, and training the object detector based on the second sensor data.

Path generation apparatus configured to generate target path of own vehicle traveling in own lane, includes: sensor configured to detect objects in surrounding area of own vehicle; and electronic control unit including processor and memory. Electronic control unit is configured to perform: recognizing adjacent vehicle traveling in adjacent lane adjacent to own lane from among detected objects; determining whether own lane is congested based on travel speed of own vehicle and determining whether adjacent lane is congested based on recognition result of adjacent lane; and generating target path of own vehicle. Generating target path includes generating target path on side away from adjacent lane with respect to that of case where own lane is determined to be congested and adjacent lane is determined to be congested, in case where own lane is determined to be congested and adjacent lane is determined not to be congested.