Abstract: A method performed by an annotation system for supporting annotation of objects in image frames of a traffic environment-related video sequence. The annotation system determines an annotation of an object in an image frame of the video sequence, which annotation comprises at least a first property of the object; tracks the object through the video sequence; and assigns the at least first object property to the object in one or more previous and/or subsequent image frames of the video sequence. The annotation system further identifies at least a first image frame based on one or more criterion. Moreover, the annotation system appoints the at least first identified image frame as annotation data.

Abstract: A method performed by a distributed subscription-handling system for supporting subscription-based activation of Automated Driving System (ADS) features in a vehicle. Arrangements that support a process where complexity and/or the transaction cost of ADS feature subscription handling may be limited. There is identified at one or more ADS-equipped vehicles connected via a peer-to-peer blockchain network having at least a first service provider node dedicated to an ADS feature provider, data indicative of one or more subscription options comprising indication of various ADS feature sets available for activation and indication of respective corresponding required ADS-related vehicle configuration are defined in at least a first blockchain-stored smart contract deployed by the at least first service provider node.

Abstract: A distributed verification system for supporting verification of ADS features within specific ODDs. The distributed verification system identifies at one or more ADS-equipped vehicles, a respective at least first verification instruction pertinent an at least first specified ODD of a specified ADS feature; collects at at least a first vehicle, verification data from execution by the at least first vehicle of the at least first ADS verification instruction while the specified ADS feature is active; generates at the at least first vehicle, a verification result transaction to be added to a distributed ledger of a block chain network, which verification result transaction includes verification information derived from the verification data along with information of circumstances and/or configuration of the at least first vehicle; and when consensus of the verification result transaction is reached on the network, adds the verification result transaction to the distributed ledger.

Abstract: The present invention relates to methods and systems that utilize the production vehicles to develop new perception features related to new sensor hardware as well as new algorithms for existing sensors by using federated learning. To achieve this, the production vehicle's own worldview is post-processed and used as a reference, towards which the output of the software (SW) or hardware (HW) under development is compared. Through this comparison, a cost function can be calculated and an update of the SW parameters can be locally updated according to this cost function.

Abstract: The present disclosure relates to a method for determining a state of a vehicle on a road portion having two or more lanes. The method comprises obtaining map data associated with the road portion and positioning data indicating a position of the vehicle on the road and a sensor data from a sensor system of the vehicle. The method further comprises initializing a filter per lane of the road portion based on the obtained map data, the obtained positioning data, and the obtained sensor data, wherein each filter indicates an estimated state of the vehicle on the road portion. Then, selecting one of the initialized filters using a trained machine-learning algorithm, configured to use the obtained map data, the positioning data, the sensor data, and each estimated state as indicated by each filter as input and to output a current state of the vehicle on the road portion.

Abstract: The present disclosure describes a method for monitoring operations of an automated driving system (ADS) of a vehicle. For each monitored operation the method includes: determining a geographical position of the vehicle; determining an intended path of the vehicle; and determining one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path. For each monitored operation the method further includes: obtaining one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position; and retrieving, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path.

Abstract: The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the “under-test” path planning module's output is evaluated in open-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning module.

Abstract: The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the “under-test” path planning module's output is evaluated in closed-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning development-module.

Abstract: A computer implemented method and related aspects for updating a perception function of a plurality of vehicles having an Automated Driving System (ADS) are disclosed. The method includes obtaining one or more locally updated model parameters of a self-supervised machine-learning algorithm from a plurality of remote vehicles, and updating one or more model parameters of a global self-supervised machine-learning algorithm based on the obtained one or more locally updated model parameters. Further, the method includes fine-tuning the global self-supervised machine-learning algorithm based on an annotated dataset in order to generate a fine-tuned global machine-learning algorithm comprising one or more fine-tuned model parameters.

Abstract: The present invention relates to methods and systems that utilize the production vehicles to develop new perception features related to new sensor hardware as well as new algorithms for existing sensors by using self-supervised continuous training. To achieve this the production vehicle's own perception output is fused with other sensors in order to generate a bird's eye view of the road scenario over time. The bird's eye view is synchronized with buffered sensor data that was recorded when the road scenario took place and subsequently used to train a new perception model to output the bird's eye view directly.

Abstract: A test-optimizing system for supporting shadow mode testing of ADS software included in an ADS-provided vehicle. One or more potential vehicle routes are retrieved. A geographical area covering the one or more potential vehicle routes is obtained and data of crucial locations associated with past vehicle situations identified as critical and/or challenging. Moreover, the test-optimizing system retrieves respective ODD for one or more candidate software respectively adapted to run in the background of the vehicle. ODD-compliant locations for respective candidate software are determined, by identifying locations out of the data of crucial locations lying within respective candidate software's ODD. At least a first test-compliant location along at least a first route out of the one or more potential vehicle routes is determined by identifying for at least a first candidate software, locations out of the ODD-compliant locations situated along the at least first route.

Abstract: The present disclosure relates to a method for determining a vehicle pose, predicting a pose (xk, yk, ?k) of vehicle on a map based on sensor data acquired by a vehicle localization system, transforming a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and predicted pose of the vehicle. The transformed set of map road references form a set of polylines in image-frame coordinate system. Identifying a set of corresponding image road reference features in an image acquired by vehicle mounted camera, where each identified road references feature defines a set of measurement coordinates (xi, yi) in image-frame. Projecting each of identified set of image road reference features onto formed set of polylines in order to obtain a set of projection points.

Abstract: The present invention relates to methods and systems that utilize the production vehicles to develop new perception features related to new sensor hardware as well as new algorithms for existing sensors by using federated learning. To achieve this, the production vehicle's own worldview is post-processed and used as a reference, towards which the output of the software (SW) or hardware (HW) under development is compared. In case of a large discrepancy between the baseline worldview and perceived worldview by the module-under-test, the data is weakly annotated by the baseline worldview. Such weakly annotated data may subsequently be used to update the SW parameters of the “perception model” in the module-under-test in each individual vehicle, or to be transmitted to the “back-office” for off-board processing or more accurate annotations.