There is provided a signal processing apparatus, a signal processing method for appropriate driving control. The signal processing apparatus includes a safety determination unit that determines safety of a preceding vehicle on a basis of sensor-related information related to a sensor provided in the preceding vehicle, the sensor being relevant to a safety function, and a control signal generation unit that generates, on a basis of a result of the determination of the safety, a control signal for controlling a host vehicle.

Methods and systems for mapping boundaries of a given environment by a processor of a computer system, the method comprising: determining a trajectory of the body in the given environment over the given time period; and determining, based on the trajectory of the body in the given environment, one or more of an outer boundary of the given environment, and an inner boundary of the given environment. Methods and systems for mapping functionalities of a given environment executable by a processor of a computer system, the method comprising determining a pattern of movement of a body in the given environment in a given time period; and determining a functional identity of at least one zone in the given environment based on the pattern of movement of the body to obtain a mapped given environment.

Techniques are disclosed for systems and methods to provide perimeter ranging for navigation of mobile structures. A navigation control system includes a logic device, a perimeter ranging sensor, one or more actuators/controllers, and modules to interface with users, sensors, actuators, and/or other elements of a mobile structure. The logic device is configured to receive perimeter sensor data from the perimeter ranging system. The logic device determines a range to and/or a relative velocity of a navigation hazard disposed within a monitoring perimeter of the perimeter ranging system based on the received perimeter sensor data. The logic device then generates a display view of the perimeter sensor data or determines navigation control signals based on the range and/or relative velocity of the navigation hazard. Control signals may be displayed to a user and/or used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.

A system for stand-off screening of individuals and/or an item of baggage carried by an individual. The system including: a sensor array, the sensor array including: an optical sensor, configured to collect data indicative of a position of the individual and/or the presence and dimension of the item of baggage relative to the optical sensor; a first radar sensor, configured to collect data indicative of: properties of objects concealed under clothing worn by the individual and/or properties of one or more objects within the item of baggage; an acoustic sensor, configured to collect data indicative of: properties of objects concealed under clothing worn by the individual and/or properties of one or more objects within the item of baggage. The system also includes a processor, configured to combine the data collected from the optical sensor, the first radar sensor, and the acoustic sensor, and to derive a risk estimation for the individual and/or the item of baggage carried by the individual based on the combined data.

An object validation system for validating perceived surrounding objects to support safety-critical threat assessment governing emergency maneuvering of an ADS on-board a vehicle. The system stores in respective sensor/modality-specific data buffers, respective sensor/modality-specific sensor data obtained at least during a predeterminable time interval continuously and/or intermittently from one or more vehicle-mounted surrounding detecting sensors. The system determines with support from a perception module configured to generate perception data based on sensor data from one or more vehicle-mounted surrounding detecting sensors, object data of a perceived object valid for the time interval. The system evaluates one or more of the respective sensor/modality-specific data buffers, separately, in view of the object data. The system also determines that the perceived object is a validated object when the object data matches sensed objects in the one or more respective sensor/modality-specific data buffers according to predeterminable matching criteria, and otherwise is an unvalidated object.

An electronic system for a vehicle includes: a first module including: a first circuit board having a predetermined form factor; a first module on the first circuit board and including a plurality of pins; a first electrical connector disposed on a first surface of the first circuit board and including a plurality of pins; and a plurality of electrical conductors connecting the pins of the first electrical connector with the pins of the first module, respectively; and a second module including: a second circuit board having the predetermined form factor; a second module on the second circuit board and including a plurality of pins; a second electrical connector disposed on a first surface of the second circuit board and including a plurality of pins, the second electrical connector coupled to the first electrical connector.

A data processing apparatus is provided, which includes processing circuitry. The processing circuitry is configured to acquire a data set from target detected by a detection apparatus, perform rendering of the data set, and generate a plurality of views arranged on a screen. Each view of the plurality of views includes a plurality of pixels. Each pixel included in the plurality of views is associated with a plurality of pieces of information including a first information displayed on the screen and a second information that indicates a view among the plurality of views to which the pixel belongs.

To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

An object detection apparatus detects a target object present in a periphery of a moving body. The object detection apparatus derives recognition information indicating a state of a target object, and predicts a state of the target object at a next second observation timing, based on the recognition information derived at a first observation timing. The object detection apparatus derives a score based on a degree of difference between a state of the target object observed at the second observation timing and a next state of the target object predicted at the first observation timing. The object detection apparatus derives a reliability level by statistically processing scores related to the target object derived at a plurality of observation timings from past to present. In response to the reliability level satisfying a predetermined reference, the object detection apparatus determines that the target object related to the reliability level is actually present.