Abstract: A method of operating a data recording device for Autonomous Driving, or Advanced Driver Assistant Systems applications for vehicles is provided. The data recording device comprises a data logging unit, a computing unit, a storage unit and a relevancy determination unit including at least one relevancy determination module. The storage unit includes a circular buffer and a persistent storage region. The method comprises retrieving sensor data acquired by sensors, storing the sensor data in the circular buffer, retrieving and storing perception results or neural network embeddings from the sensor data and deciding whether the retrieved perception results or neural network embeddings are considered relevant or irrelevant, and triggering a transfer of the sensor data from the circular buffer to the persistent storage region in case the perception results or neural network embeddings corresponding to the sensor data are considered relevant.

Abstract: The present application relates to the field of range sensors, and more particular to a method, a system and a computer-readable storage medium for object detection in a 3D point cloud representing a scanned surrounding of a vehicle. One aspect of the present invention relates to a computer-implemented method for object detection in a three-dimensional, 3D, point cloud representing a scanned surrounding of a vehicle. The method comprises determining a first plurality of scanning samples representing the scanned surrounding, the first plurality of scanning samples consisting of a first half of scanning samples and a second half of scanning samples. The method further comprises populating the 3D point cloud with the first plurality of scanning samples. The method further comprises detecting objects in the 3D point cloud contained in one or more of the second half of scanning samples.

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 computer implemented method for determining candidate data sets for labelling comprises the following steps carried out by computer hardware components: determining a plurality of sensor data sets; determining a respective signature for each of the plurality of sensor data sets; determining, based on the signature of the respective sensor data set, for each of the plurality of sensor data sets whether the respective sensor data set is a candidate data set for labelling; and providing the sensor data set to a labeling instance for labelling if the sensor data set is a candidate data set for labelling.

Abstract: A computer implemented method for detecting objects includes providing signal representation data comprising range information, velocity information and angular information; for each of a plurality of spatial scales, determining respective scaled data for the respective spatial scale based on the signal representation data, to obtain a plurality of scaled data; providing the plurality of scaled data to a plurality of detectors; and each detector carrying out object detection based on at least one of the plurality of scaled data.