Abstract: A path planning method and system for a vehicle. The method includes obtaining risk map of a surrounding environment of vehicle. Risk map is formed based on an actuation capability of the vehicle and location of free-space areas in the surrounding environment, actuation capability including uncertainty estimation for actuation capability and the location of free-space areas comprising an uncertainty estimation for the estimated location of free-space areas. Risk map includes risk parameter for each of a plurality of area segments included in the surrounding environment of the vehicle. Obtaining at least one candidate path for vehicle, determining total risk value for each candidate path based on risk parameters of a set of area segments intersected by the at least one path, selecting a candidate path, of at least one candidate path, fulfilling a risk value criterion, and generating, at an output, a first signal indicative of selected candidate path.

Abstract: The present inventive concept relates to a computer-implemented method for determining a consolidation policy for forming a consolidated machine learning model from a number of local machine learning models of a fleet of vehicles equipped with an automated driving system. The method includes: obtaining two or more model updates from one or more vehicles of the fleet of vehicles, wherein each model update is a result of training a local machine learning model of the respective vehicle; consolidating the two or more model updates according to a candidate consolidation policy, thereby forming a consolidated machine learning model; evaluating the consolidated machine learning model according to an evaluation criterion; and updating the candidate consolidation policy in view of the evaluation, thereby forming an updated candidate consolidation policy. It further relates to a device thereof.

Abstract: The present inventive concept relates to a computer-implemented method for determining a distribution of a training dataset for subsequent training of a machine learning model of an automated driving system, as well as other aspects thereof. The method includes: providing a first dataset by selecting, based on a candidate distribution, data samples from a second dataset of available training data; training the machine learning model on the first dataset; evaluating the machine learning model according to an evaluation criterion; and updating the candidate distribution in view of the evaluation, thereby forming an updated candidate distribution. The present inventive concept further relates to a method for forming a training dataset for subsequent training of a machine learning model, as well as other aspects thereof.

Abstract: A control system and a method for estimating a risk exposure of an automated driving system (ADS) of a vehicle. Obtaining an actuation capability of the vehicle, wherein the obtained actuation capability includes an uncertainty estimation for the actuation capability. Obtaining a location of free-space areas in the surrounding environment of the vehicle, wherein the obtained location of free-space areas comprises an uncertainty estimation for the estimated location of free-space areas. Forming a risk map of the surrounding environment of the vehicle based on the obtained actuation capability and the obtained location of free-space areas, wherein the risk map includes a risk parameter for each of a plurality of area segments included in the surrounding environment of the vehicle. Determining a total risk value of the ADS based on the risk parameters of a set of area segments intersected by at least one planned path of the ADS.

Abstract: A scenario generating system for supporting generation of driving scenarios of an ADS.

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 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: 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 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: 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 disclosure relates to methods, systems, a vehicle and a computer-readable storage medium and a computer program product. The method includes obtaining a baseline risk model generated based on one or more risk-associated parameters. The method further includes obtaining sensor data from a sensor system of a vehicle. The method further includes estimating one or more risk values associated with the one or more risk-associated parameters based on the obtained sensor data and generating, based on the one or more estimated risk values, an adopted risk model. Further, the method includes provisioning the generated adopted risk model for determining an acceptable risk model for forming a driving policy of an ADS.

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: A method for identifying scenarios of interest for development, verification and/or validation of an ADS of vehicle. Obtaining risk map of surrounding environment of vehicle, risk map is formed based on actuation capability of vehicle and location of free-space areas in surrounding environment. The actuation capability comprises uncertainty estimation for actuation capability and location of free-space areas comprises uncertainty estimation for estimated location of free-space areas. Risk map includes risk parameter for each of a plurality of area segments comprised in surrounding environment of vehicle. Determining compounded risk value of ADS based on risk parameters of a set of area segments of risk map. Monitoring scenario trigger by monitoring at least one of determined compounded risk value against compounded risk trigger threshold, a development of risk map over time against a map volatility trigger threshold, and a development of compounded risk value over time against a risk volatility threshold.

Abstract: A method performed by an Automated Driving Systems (ADS) development system for supporting proving fulfilment of safety requirements imposed on ADSs. The ADS development system derives a corresponding set of parameter-specific ADS safe driving policies, each safe driving policy exhibiting a respective uncertainty. The ADS development system identifies at least a first parameter-specific ADS safe driving policy inflicted with an uncertainty fulfilling predeterminable criteria, which criteria filters out uncertainties indicating, respectively, that the corresponding operational parameter needs to be further observed in order to relax the ADS's currently allowed safety requirements-fulfilled ADS safe driving policy. The ADS development system identifies at least a first geographical location exhibiting conditions allowing the operational parameter(s) in need of further observance, to be observed.

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.

Abstract: A method for closed-loop evaluation of path planning modules for a vehicle equipped with an Automated Driving System (ADS) is disclosed. The method includes obtaining a candidate path from each of a plurality of path planning modules of the ADS. The method further includes determining a fulfilment of convergence criteria by the obtained candidate paths by comparing the obtained candidate paths with each other and determining a level of convergence between the candidate paths. If the convergence criteria is fulfilled, one of the obtained candidate paths is selected and the vehicle is controlled so to execute the selected candidate path. If the convergence criteria is not fulfilled, determining an exposure need of each path planning module in view of a predicted scene or scenario in the surrounding environment of the vehicle that the vehicle is expected to be exposed to while executing any one of the obtained candidate paths.

Abstract: A method performed by an Automated Driving Systems (ADS) development system for supporting proving fulfilment of safety requirements imposed on ADSs. The ADS development system derives a corresponding set of parameter-specific ADS safe driving policies, each safe driving policy exhibiting a respective uncertainty. The ADS development system identifies at least a first parameter-specific ADS safe driving policy inflicted with an uncertainty fulfilling predeterminable criteria, which criteria filters out uncertainties indicating, respectively, that the corresponding operational parameter needs to be further observed in order to relax the ADS's currently allowed safety requirements-fulfilled ADS safe driving policy. The ADS development system identifies at least a first geographical location exhibiting conditions allowing the operational parameter(s) in need of further observance, to be observed.

Abstract: A method for updating a perception function of a vehicle having an Automated Driving System (ADS) is disclosed. The ADS has a self-supervised machine-learning (ML) algorithm for reconstructing an ingested image and a ML algorithm for an in-vehicle perception module for detecting one or more objects or free-space areas depicted in an ingested image. At first, an image of a scene in a surrounding environment of the vehicle is obtained. The obtained image is processed to obtain an output image with one or more detected objects or free-space areas. Then, an evaluation dataset is formed accordingly. The evaluation dataset and the obtained image is processed to obtain a reconstruction error value for each evaluation image and an evaluation image with highest reconstruction error value is selected among plurality of evaluation images. Using the obtained image and the selected evaluation image, the ML algorithm for the in-vehicle perception module is updated.

Abstract: An assessment system for performance evaluation and updating of a PMUD of an ego-vehicle. The assessment system obtains world view data from a perception module configured to generate the world view data based on sensor data obtained from vehicle-mounted sensors; obtains other world view data generated by another perception module; forms a joint world view by matching the world view data the other world view data; and obtains perception data based on a perception model and sensor data obtained from one or more vehicle-mounted sensors. The assessment system further matches the perception data to the formed joint world view; evaluates the obtained perception data in reference to the joint world view to determine an estimation deviation in an identified match between the perceptive parameter of the perception data and a corresponding perceptive parameter in the joint world view; and updates parameters of the perception model based on the estimation deviation.

Abstract: The present disclosure relates to a method for augmenting capabilities of an Automated Driving System (ADS) of a vehicle. The method includes locally processing, by means of a perception module of the ADS, sensor data obtained from one or more sensors of the vehicle in order to generate a local world-view of the ADS, wherein the sensor data includes information about a surrounding environment of the vehicle. The method further includes transmitting sensor data including information about the surrounding environment of the vehicle to a remote system, and receiving off-board processed data from the remote system, the off-board processed data being indicative of a supplementary world-view of the ADS. Furthermore, the method includes forming an augmented world-view of the ADS based on the generated local world-view and the supplementary world-view.