There is provided an information processing system capable of performing driving assistance according to different automated driving levels.

An information processing system according to an embodiment of the present disclosure includes one or more mobile devices capable of setting an automated driving level, and an external network device capable of communicating with the mobile devices. The external network device includes a communication device that communicates with the mobile device, an arithmetic model determination device that determines an arithmetic model corresponding to the automated driving level and provides the arithmetic model to the mobile device via the communication device, and a registration determination device that determines whether or not registration of the automated driving level is possible on the basis of information regarding possession of the arithmetic model, and gives a notification of registration permission to the mobile device via the communication device. The mobile device includes an arithmetic model request unit that requests the arithmetic model from the arithmetic model determination device, and causes the information to be transmitted to the registration determination device when the requested arithmetic model is provided, and a movement control unit that starts movement control based on the automated driving level permitted to be registered when the notification is received from the registration determination device.

A method can be used to control a steer-by-wire steering system in an emergency steering mode. The method comprises checking a steering system for the presence of a fault state and upon detection of a fault implementing the emergency steering mode, which involves determining a setpoint position of a steering tie rod using a setpoint wheel steering angle, determining a front wheel to be braked and a brake pressure to attain the setpoint position with a control unit, transmitting the front wheel to be braked and the brake pressure to a brake system, braking the front wheel to be braked, and increasing a torque provided by a wheel drive to compensate for a loss of speed of the motor vehicle caused by the braking of the front wheel to be braked.

A system includes one or more video capture devices and a processor coupled to each video capture device. Each processor is operable to direct its respective video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, each bounding perimeter virtual object surrounding at least part of its respective object of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for multiple objects of interest, and may optionally generate an alert at least partially in response to the near-miss condition.

Provided are methods for thermal sensor data vehicle perception, which can include obtaining thermal sensor data, obtaining non-thermal sensor data, and determining, based on the thermal sensor data and the non-thermal sensor data, a perception parameter indicative of an object. Some methods described also include generating a trajectory for an autonomous vehicle. Systems and computer program products are also provided.

Using predictive visual anchors to control an autonomous vehicle, including: determining, based on a plurality of frames of video data from a camera of an autonomous vehicle, one or more predicted visual anchors, wherein the one or more predicted visual anchors comprise a predicted location of one or more visual anchors at a future time relative to when the plurality of frames were captured; identifying, in another frame of video data corresponding to the future time, the one or more visual anchors; determining one or more differentials between the one or more visual anchors and the one or more predicted visual anchors; determining, based on the one or more differentials, one or more control operations for the autonomous vehicle; and applying the one or more control operations.

At least one processor of an electronic device in a vehicle may be configured to: receive broadcast information which is broadcast from a beacon and includes reference data indicating the relative positional relationship between a designated object positioned in a designated place and the position of the beacon and the data of the designated place; in response to reception of the broadcast information, acquire sensed data indicating the relative positional relationship between the designated object and the vehicle through at least one sensor of the electronic device on the basis of the data of the designated place; in response to acquiring the sensed data, identify the difference between the sensed data and the reference data; identify whether the difference lies outside of a reference range; and determine correction of the at least one sensor to be required, on the basis of identification that the difference lies outside of the reference range.

A vehicle may transmit, to a network entity, a lane use request message associated with a lane for the vehicle. The network entity may identify lane information associated with a lane for a vehicle. The network entity may identify vehicle information associated with the vehicle. The network entity may transmit, to the vehicle, and the vehicle may receive, from the network entity, a lane use grant message based on at least one of the identified lane information or the identified vehicle information. The lane use grant message may be indicative of a permission for the vehicle to use the lane. The vehicle may not be permitted to use the lane without the permission. The lane may correspond to a flexible direction lane, an emergency lane, a road shoulder, an HOV lane, or a passing lane.

A security system includes: an autonomous vehicle; a camera installed in the autonomous vehicle; and a crime determination unit that makes a determination regarding a crime on the basis of an image captured by the camera. The autonomous vehicle is a shared car shared by residents within a region; in response to a request, the autonomous vehicle automatically picks up at the departure point of the residents and automatically moves to the destination of the residents; and the camera take pictures of the moving section to the starting point and the moving section from the starting point to the destination; and the crime determination unit determines a crime based on the images taken in the moving section to the departure point and the moving section from the starting point to the destination.

Provided are methods for correcting multi-zone motion blur, which include executing, using at least one processor, an alignment of at least one image capturing device with at least one collimating device in a plurality of collimating devices, causing a rotation of at least one collimating device, receiving at least one image of at least one target object captured by the image capturing device for processing by at least one rotating collimating device, and determining, based on the at least one processed image, a degradation of the received image of the target object.

Provided are methods for localization functional safety, which can include systems, methods, and computer program products are also provided. In examples, a method includes applying a transform to a source point cloud and calculating a second metric based on the application of the transform to the source point cloud and a map at a higher ASIL level. A first metric is determined based on a localization function that executes at a lower ASIL level. A deviation between a first metric and the second metric, is determined wherein the vehicle localization is validated when the deviation is less than a predetermined threshold.