Abstract: Embodiments of the present disclosure relate to a system and method used to transfer image data via Ethernet. In some embodiments, the method may include determining, using a machine learning model, an estimated velocity corresponding to an object based at least on measured RADAR data, where the measured RADAR data may correspond to RADAR detections associated with the object. In some embodiments, the method may further include determining expected RADAR data corresponding to the object based at least on the estimated velocity. Some embodiments may additionally include updating one or more parameters of the machine learning model based on the difference between the measured RADAR data and the expected RADAR data.

Abstract: Provided is a data transmission system, a vehicle comprising the data transmission system, a data transmission method and a computer program. Specifically, the system comprises a centralized controller, a publisher entity and a subscriber entity; wherein the publisher entity and the subscriber entity are compiled with a predefined data format; the publisher entity registers at the centralized controller with topic information; the centralized controller, upon registration of the publisher entity, stores the topic information with information for registering at the publisher entity; the subscriber entity subscribes at the centralized controller to the topic information; the centralized controller, upon subscription of the subscriber entity, provides to the subscriber entity information for registering at the publisher entity; and the subscriber entity creates the communication channel between the publisher entity and the subscriber entity based on the information for registering at the publisher entity.

Abstract: A computer system for storing data of driver-assistance systems of vehicles may include: a sensor entity configured to store, for each of at least one sensor mounted on a vehicle, respective sensor configuration data comprising a respective sensor identifier; a vehicle entity configured to store respective vehicle configuration data comprising a respective vehicle identifier and a reference to at least one sensor identifier; a log entity configured to store respective logging configuration data comprising a respective log identifier and a reference to at least one vehicle identifier; a stream entity, wherein the stream entity is configured to store, for each of at least one stream, respective stream configuration data comprising a respective stream identifier, and a reference to at least one log identifier, and a sample entity configured to store respective sample configuration data, comprising a respective sample identifier and a reference to at least one stream identifier.

Abstract: A computer system for storing data of driver-assistance systems of vehicles may include: a sensor entity configured to store, for each of at least one sensor mounted on a vehicle, respective sensor configuration data comprising a respective sensor identifier; a vehicle entity configured to store respective vehicle configuration data comprising a respective vehicle identifier and a reference to at least one sensor identifier; a log entity configured to store respective logging configuration data comprising a respective log identifier and a reference to at least one vehicle identifier; a stream entity, wherein the stream entity is configured to store, for each of at least one stream, respective stream configuration data comprising a respective stream identifier, and a reference to at least one log identifier, and a sample entity configured to store respective sample configuration data, comprising a respective sample identifier and a reference to at least one stream identifier.

Abstract: Systems and methods are disclosed that relate to freespace detection using machine learning models. First data that may include object labels may be obtained from a first sensor and freespace may be identified using the first data and the object labels. The first data may be annotated to include freespace labels that correspond to freespace within an operational environment. Freespace annotated data may be generated by combining the one or more freespace labels with second data obtained from a second sensor, with the freespace annotated data corresponding to a viewable area in the operational environment. The viewable area may be determined by tracing one or more rays from the second sensor within the field of view of the second sensor relative to the first data. The freespace annotated data may be input into a machine learning model to train the machine learning model to detect freespace using the second data.

Abstract: Provided is a data transmission system, a vehicle comprising the data transmission system, a data transmission method and a computer program. Specifically, the system comprises a centralized controller, a publisher entity and a subscriber entity; wherein the publisher entity and the subscriber entity are compiled with a predefined data format; the publisher entity registers at the centralized controller with topic information; the centralized controller, upon registration of the publisher entity, stores the topic information with information for registering at the publisher entity; the subscriber entity subscribes at the centralized controller to the topic information; the centralized controller, upon subscription of the subscriber entity, provides to the subscriber entity information for registering at the publisher entity; and the subscriber entity creates the communication channel between the publisher entity and the subscriber entity based on the information for registering at the publisher entity.

Abstract: A method for automatically determining and using noise statistics of noisy observation data provided by at least one sensor connected to a tracker, in order to improve tracking of a time-varying object across a scene using a Kalman algorithm. The Kalman algorithm allowing to get an estimation of dynamic parameters of the object and providing a precision of said estimation from said noisy observation data and from a corrected previous estimation. The method includes receiving reference data from a data source external to the Kalman algorithm and connected to the tracker, deriving, from said reference data, said noise statistics reflecting errors made by the sensor, and using said noise statistics as setting parameters of the Kalman algorithm.

Abstract: A radar system suitable for an automated vehicle comprises at least one receiving element configured to detect reflected radar signals reflected by an object in a field-of-view of the system. The receiving element is further configured to generate detected signals indicative of the reflected radar signals detected by the receiving element. Furthermore, an auto-encoding device performing an auto-encoding operation on the detected signals is embedded in the receiving element such that the receiving element outputs the detected signals in an encoded form.

Abstract: A radar system suitable for an automated vehicle comprises at least one receiving element configured to detect reflected radar signals reflected by an object in a field-of-view of the system. The receiving element is further configured to generate detected signals indicative of the reflected radar signals detected by the receiving element. Furthermore, an auto-encoding device performing an auto-encoding operation on the detected signals is embedded in the receiving element such that the receiving element outputs the detected signals in an encoded form.