Implementations for an intelligent road barrier (IRB) system can include a plurality of road barriers located along a roadway, and a plurality of IRB kits, each IRB kit being including a set of sensors configured to obtain sensor data representative of at least a portion of a road state of the roadway proximate to the respective road barrier, a data processing unit configured to process at least a portion of the sensor data, a communication unit configured to send at least a portion of the sensor data and to receive sensor data from the one or more other IRB kits, and a state conveyance system configured to convey at least a portion of road state data to one or more vehicles associated with the roadway, the at least a portion of road state data being determined based on the at least a portion of the sensor data.

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 system for an autonomous vehicle that predicts a location-based maneuver of a remote vehicle located in a surrounding environment includes one or more vehicle sensors collecting sensory data indicative of one or more vehicles located in the surrounding environment. The system also includes one or more automated driving controllers in electronic communication with the one or more vehicle sensors. The one or more automated driving controllers execute instructions to compare a lane of travel of the remote vehicle with a current lane of travel of the autonomous vehicle. In response to determining the lane of travel of the remote vehicle is a different lane than the current lane of the autonomous vehicle, the one or more automated driving controllers predict the location-based maneuver of the remote vehicle based on aggregated vehicle metrics that are based on historical data collected at the specific geographical location.

An entry point and an exit point are specified in a non-lane section. A lane definition line is searched to be continuous in a longitudinal direction of the road from the entry point to the exit point. A virtual lane marking line is generated to connect between the lane marking line on an entry side and the lane marking line on an exit side, based on the lane definition line detected as a search result. A travelling track is generated as a trajectory on which a vehicle is scheduled to travel, based on the virtual lane marking line.

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 system includes an autonomous vehicle (AV) comprising a sensor, a control subsystem, and an operation server. The control subsystem receives sensor data comprising location coordinates of the AV from the sensor. The operation server detects an unexpected event from the sensor data, comprising at least one of an accident, an inspection, and a report request. The operation server receives a message from a user comprising a request to access particular information regarding the AV and location data. The operation server associates the AV with the user if the location coordinates of the AV match location data of the user. The operation server establishes a communication path between the user and a remote operator for further communications.