Systems and methods for monitoring the lane of an object in an environment of an autonomous vehicle are disclosed. The methods include receiving sensor data corresponding to the object, and assigning an instantaneous probability to each of a plurality of lanes based on the sensor data as a measure of likelihood that the object is in that lane at a current time. The methods also include generating a transition matrix for each of the plurality of lanes that encode one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time. The methods then include determining an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix as a measure of likelihood of the object occupying that lane at the current time.

A technique for tracking objects includes: determining a set of detected measurements based on a received return signal; determining a group that includes a set of group measurements and a set of group tracks; creating a merged factor, including a merged set of track state hypotheses associated with a merged set of existing tracks including a first set of existing tracks and a second set of existing tracks, by calculating the cross-product of a first set of previous track state hypotheses and a second set of previous track state hypotheses; determining a first new factor and a second new factor; calculating a first set of new track state hypotheses for the first new factor based on a first subset of the group measurements; and calculating a second set of new track state hypotheses for the second new factor based on a second subset of the group measurements.

The invention relates to a method for detecting at least one road user on a traffic route by means of a radar sensor and an optical detector, wherein with said method radar radiation is emitted by at least one radar transmitter of the radar sensor and reflected by the at least one road user, the reflected radar radiation is detected by means of at least one radar receiver of the radar sensor, the detected radar radiation is evaluated in such a way that at least one distance and one radial velocity of the at least one road user relative to the radar sensor is determined, an optical image of the at least one road user is detected by means of the optical detector, and the optical image is evaluation,
wherein at least one parameter of the at least one road user is determined both from the detected radar radiation and the optical image.

Systems and methods for providing a synthetic track to observation devices are provided. In one embodiment, a method can include determining a location range and a time range for a synthetic track to be created by a plurality of platforms. The method can further include determining an emission location and an emission time for each of the platforms of the plurality of platforms based, at least in part, on the location range and the time range. The method can include sending a set of data to each of the plurality of platforms, each respective set of data indicating the emission location and the emission time at which the respective platform is to generate the emission to create the synthetic track.

Exemplary aspects are directed to or involve a radar transceiver to transmit signal and receive reflected radar signals via a communication channel. The exemplary method includes radar receiver data processing circuitry that may be used to differentiate a subset of representations of the received signals. This differentiation may be used to select signals that are more indicative of target(s) having a given range than other ones of the received signals. The received signal's representations may then be compressed by using variable-mantissa floating-point numbers having mantissa values that vary based, at least in part, on at least one strength characteristic of the respective representations.

Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.

The present disclosure provides an error detector for determining an error vector between a radio trajectory and an image trajectory. The error detector includes: an input for monitoring a radio trajectory of an object from a radio signal and an image trajectory of an object from an image over an observation area; a correlation module arranged to correlate the radio trajectory with the image trajectory; an error module arranged to determine an error vector between the radio trajectory and the image trajectory; and an output arranged to transmit the error vector for use in determining an estimated trajectory of a target based on a target trajectory from a radio signal.

Generally, the present disclosure is directed to systems and methods for streaming processing within one or more systems of an autonomy computing system. When an update for a particular object or region of interest is received by a given system, the system can control transmission of data associated with the update as well as a determination of other aspects by the given system. For example, the system can determine based on a received update for a particular aspect and a priority classification and/or interaction classification determined for that aspect whether data associated with the update should be transmitted to a subsequent system before waiting for other updates to arrive.

This document describes techniques and systems for generating a fused object bounding box based on uncertainty. At least two bounding boxes, each associated with a different sensor, is generated. A fused center point and yaw angle as well as length, width, and velocity can be found by mixing the distributions of the parameters from each bounding box. A discrepancy between the center points of each bounding box can be used to determine whether to refine the fused bounding box (e.g., find an intersection between at least two bounding boxes) or consolidate the fused bounding box (e.g., find a union between at least two bounding boxes). This results in the fused bounding box having a confidence level of the uncertainty associated with the fused bounding box. In this manner, better estimations of the uncertainty of the fused bounding box may be achieved to improve tracking performance of a sensor fusion system.

A tracking method and system includes receiving a primary radar report after establishment of a real track of the aircraft, determining a false track slant range associated with the aircraft based on an effective altitude of the aircraft above a ground or water surface and an aircraft slant range defined between the radar arranged on the ground surface and the aircraft, determining a capture area based on the false track slant range and an azimuth of the aircraft, and determining whether the primary radar report is a false report by comparing a position of the aircraft determined from the primary radar report to the capture area.