The disclosure includes embodiments for managing vehicles under anomalous driving behavior. In some embodiments, a method includes determining, by the processor, an ego behavior associated with the ego vehicle and a remote behavior associated with a remote vehicle. The method includes calculating a variance between the ego behavior and the remote behavior. The method includes determining a presence of an anomaly based on the variance satisfying a threshold, wherein the satisfying the threshold indicates the ego behavior is incompatible with the remote behavior. The method includes generating a driving management plan which is configured to mitigate the anomaly and is consistent with the ego behavior. The method includes implementing the driving management plan so that the threshold is no longer satisfied.

An axial displacement angle estimation device repeatedly calculates an axial displacement angle based on the detection result of the radar apparatus. The axial displacement angle estimation device extracts the axial displacement angle included in a predetermined extraction angle range among a plurality of axial displacement angles, and calculate an average value and a median value of the extracted plurality of axial displacement angles to be an axial displacement angle average value and an axial displacement median value. The axial displacement angle estimation device determines, based on the axial displacement angle average value and the axial displacement angle median value, whether a predetermined allowable condition is met. The axial displacement angle estimation device utilizes, when determined that the predetermined allowable condition is met, the axial displacement angle average value as an estimation result of the axial displacement angle.

This application provides example detection signal transmitting methods, detection apparatuses, and storage medium. One example method includes determining an orientation of a field of view of a detection apparatus. One of a plurality of anti-interference parameters can then be selected as a target anti-interference parameter based on the orientation of the field of view of the detection apparatus and according to a predefined rule, where the plurality of anti-interference parameters are determined according to the predefined rule. A detection signal can then be transmitted based on the target anti-interference parameter.

Disclosed are techniques for determining a motion state of a target object. In an aspect, an on-board computer of an ego vehicle detects the target object in one or more images, determines one or more first attributes of the target object based on measurements of the one or more images, determines one or more second attributes of the target object based on measurements of a map of a roadway on which the target object is travelling, and determines the motion state of the target object based on the one or more first attributes and the one or more second attributes of the target object.

Various aspects of the subject technology relate to a mobile support platform for vehicle sensor calibration. The mobile support platform includes a chassis, lift posts on the chassis configured to interface with one or more lift points on a vehicle and raise the vehicle, and a set of wheels mounted to the chassis configured to carry the vehicle through a calibration sequence.

Systems, methods and circuitries are disclosed for compressing radar data. In one example, a radar sender unit includes adaptive compression circuitry configured to determine tuning data, wherein the tuning data is based on one or more operating conditions; compress radar data based on the tuning data; and transmit the compressed radar data to a radar control unit for further processing.

Provided is an electronic device including: a sensing device selected from a group including a radar and a lidar and installed in a vehicle to have a sensing zone directed to outside of the vehicle, the sensing device configured to obtain sensing data about an object; an image obtainer installed in the vehicle to have a field of view directed to the outside of the vehicle, the sensing device configured to obtain image data; and a controller including at least one processor configured to process the sensing data obtained by the sensing device and the image data obtained by the image obtainer, wherein the controller generates a first virtual driving path and a second virtual driving path based on the sensing data and the image data, and when a first boundary of the first virtual driving path and a second boundary of the second virtual driving path are located at different positions, provides a virtual driving path having a boundary closest to the vehicle between the first virtual driving path and the second virtual driving path as an actual driving path.

Ground truth data may be too sparse to supervise training of a machine-learned (ML) model enough to achieve an ML model with sufficient accuracy/recall. For example, in some cases, ground truth data may only be available for every third, tenth, or hundredth frame of raw data. Training an ML model to detect a velocity of an object when ground truth data for training is sparse may comprise training the ML model to predict a future position of the object based at least in part on image, radar, and/or lidar data (e.g., for which no ground truth may be available). The ML model may be altered based at least in part on a difference between ground truth data associated with a future time and the future position.

A computer implemented method for determining a position, and/or an acceleration, and/or an angular rate and/or an orientation of a vehicle includes the following steps carried out by computer hardware components: determining first measurement data using a first sensor; determining a preliminary position and/or a preliminary orientation based on the first measurement data; determining second measurement data using a second sensor, wherein the second sensor includes a radar sensor and/or a LIDAR sensor and/or a camera; determining a preliminary acceleration and/or a preliminary angular rate based on the second measurement data; and determining a final position, and/or a final acceleration, and/or a final angular rate and/or a final orientation based on the preliminary acceleration and/or the preliminary angular rate, and the preliminary position and/or the preliminary orientation.

An autonomous driving system includes a target object position recognition unit configured to recognize a target object position detected by a vehicle-mounted sensor based on map information in a map database, a vehicle position recognition unit configured to recognize a vehicle position, a relative-relationship-on-map acquisition unit configured to acquire a relative-relationship-on-map between the target object and the vehicle based on the target object position and the vehicle position on the map, a detected-relative-relationship acquisition unit configured to acquire a detected-relative-relationship between the target object detected by the sensor and the vehicle based on a result of detection performed by the sensor, a map accuracy evaluation unit configured to evaluate map accuracy of the map information based on the relative-relationship-on-map and the detected-relative-relationship, and an autonomous driving permission unit configured to permit an autonomous driving control using the map information based on the result of evaluation of the map accuracy.