Disclosed are methods, systems, and non-transitory computer-readable medium for distributed vehicle processing. For instance, the method may include: in response to determining a first trigger condition of a first set of trigger conditions is satisfied, performing a first process corresponding to the first trigger condition on-board a vehicle; in response to determining a second trigger condition of a second set of trigger conditions is satisfied, prompting a second process corresponding to the second trigger condition by transmitting an edge request to an edge node and receiving an edge response from the edge node; and in response to determining a third trigger condition of a third set of trigger conditions is satisfied, prompting a third process corresponding to the third trigger condition by transmitting a cloud request to a cloud node and receiving a cloud response from the cloud node.

The present teaching relates to apparatus, method, medium, and implementations for initiating sensor calibration. A first GPS signal is received by a GPS receiver residing in an ego vehicle and is used to determine a first geo-position of the ego vehicle. A GPS related signal transmitted by a fiducial marker is received and is used to obtain a second geo-position of the fiducial marker. A distance between the ego vehicle and the fiducial marker is determined based on the first and second geo-positions and is used to determine whether to initiate calibration of one or more sensors using the fiducial marker.

A method comprising receiving, at a receiver, a plurality of signals from at least one remote source and selecting at least one selected signal in the plurality of signals. The method determines, using the at least one selected signal, a position of the receiver and receives correction data for improving the position of the receiver to a sub-wavelength accuracy. The method further determines motion of the receiver, generates, from the at least one selected signal, a motion-compensated correlation signal based on the determined motion of the receiver and uses the motion-compensated correlation signal to either (1) select the at least one selected signal to be used to determine the position of the receiver, (2) correct at least one of motion sensor errors or clock errors, or (3) both (1) and (2). Embodiments include a positioning system for performing the method.

A system is disclosed for determining a physical metric such as position. The system comprises a local signal generator (8) configured to provide a local signal and a receiver (4) configured to receive a signal having properties corresponding to those in a signal transmitted by a trusted remote source. An inertial measurement unit (12) is configured to provide a measured or assumed movement of the receiver. A correlator (6) is configured to provide a correlation signal by correlating the local signal with the received signal. A motion compensation unit (14) is configured to provide motion compensation of at least one of the local signal, the received signal, and the correlation signal based on the measured or assumed movement. A signal analysis unit (16) is configured to determine whether the received signal includes a component received in a direction that is different to a line-of-sight direction between the receiver and the trusted remote source, wherein the determination is based on the correlation signal. Finally, a metric determination unit or positioning unit (20) is configured to determine a physical metric associated with the receiver, such as its position, based on the determination made by the signal analysis unit (16).

A system is disclosed for determining a physical metric such as position. The system comprises a local signal generator (8) configured to provide a local signal and a receiver (4) configured to receive a signal having properties corresponding to those in a signal transmitted by a trusted remote source. An inertial measurement unit (12) is configured to provide a measured or assumed movement of the receiver. A correlator (6) is configured to provide a correlation signal by correlating the local signal with the received signal. A motion compensation unit (14) is configured to provide motion compensation of at least one of the local signal, the received signal, and the correlation signal based on the measured or assumed movement. A signal analysis unit (16) is configured to determine whether the received signal includes a component received in a direction that is different to a line-of-sight direction between the receiver and the trusted remote source, wherein the determination is based on the correlation signal. Finally, a metric determination unit or positioning unit (20) is configured to determine a physical metric associated with the receiver, such as its position, based on the determination made by the signal analysis unit (16).

The disclosure includes embodiments for early detection of abnormal driving behavior. A method according to some embodiments includes sensing, by a sensor set of an ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method includes comparing the sensor data to a set of criteria for abnormal driving behavior. The method includes determining that a subset of the set of criteria are described by the sensor data. The subset satisfies a threshold for early detection of abnormal driving behavior. For example, detecting the subset triggers the determination that abnormal driving behavior is detected. The method includes determining that the remote vehicle is engaged in abnormal driving behavior based on satisfaction of threshold. In some embodiments, an abnormal driving behavior is one which satisfies a threshold for abnormality or matches a pattern of an identified abnormal driving behavior.

The disclosure includes embodiments for early detection of abnormal driving behavior. A method according to some embodiments includes sensing, by a sensor set of an ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method includes comparing the sensor data to a set of criteria for abnormal driving behavior. The method includes determining that a subset of the set of criteria are described by the sensor data. The subset satisfies a threshold for early detection of abnormal driving behavior. For example, detecting the subset triggers the determination that abnormal driving behavior is detected. The method includes determining that the remote vehicle is engaged in abnormal driving behavior based on satisfaction of threshold. In some embodiments, an abnormal driving behavior is one which satisfies a threshold for abnormality or matches a pattern of an identified abnormal driving behavior.

A method for locating an object of interest using offset. The object may be a mobile platform, or portion of same, associated with a vehicle, or a pavement segment or feature of or on a pavement segment on which the mobile platform is located. The vehicle includes first and second fixed points having a known offset from each other. An image sensor whose field of view includes the second fixed point and a portion of the mobile platform provides image data which is used with the known offset to calculate the precise location of the object of interest.

A method for locating an object of interest using offset. The object may be a mobile platform, or portion of same, associated with a vehicle, or a pavement segment or feature of or on a pavement segment on which the mobile platform is located. The vehicle includes first and second fixed points having a known offset from each other. An image sensor whose field of view includes the second fixed point and a portion of the mobile platform provides image data which is used with the known offset to calculate the precise location of the object of interest.

A machine includes a rotational sensor configured to sense rotation of an upper frame of the machine relative to a lower frame of the machine. The machine also includes a three-dimensional position sensor spaced from an axis of rotation of the upper frame relative to the lower frame. The machine can also include a number of additional sensors including sensors to detect track movement, imaging sensors, ranging sensors, IMUs, linear displacement sensors and/or the like. A computing system receives the various inputs from the sensors and fuses the data to determine state information for the machine.