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: 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: A method performed by a scenario reconstruction system for supporting assessment of unforeseen driving scenarios of a host vehicle. The scenario reconstruction system stores timestamped sensor data comprising the host vehicle's geographical position and surroundings captured with support from one or more surrounding detecting sensors onboard the host vehicle. The system detects at a scenario time instance, involvement of the host vehicle in an unforeseen driving scenario. Moreover, the scenario reconstruction system derives from the timestamped sensor data, subsets of sensor data and identifies one or more surrounding detecting sensors-provided entities respectively present in the subsets pertinent at least two respective separate timestamps.

Abstract: Method and server for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A server (101; 500) provides (301) a virtual environment (200) simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (230a-c), human controlled movable virtual objects (220a-c) and at least one virtual AD/ADAS vehicle (210) operating according to said AD/ADAS functionality. The server (101; 500) allows devices (101-103) to remotely connect to the server (105; 500) and users of said devices (101-103) to, via user interfaces of the devices (101-103), control said human controlled movable virtual objects (220a-c), respectively, in the virtual environment (200), and thereby cause generation of scenarios that said at least one virtual AD/ADAS vehicle (210) is subjected to.

Abstract: Various methods for managing and/or monitoring new experiences of an Automated Driving System, ADS, of a vehicle are disclosed. In one example, an ADS experience library is used as training data for an autoencoder on-board the vehicle. A data stream representing driving experiences encountered by the vehicle as it is being driven around is suitably segmented and passed through the autoencoder. The output of the autoencoder exaggerates the reconstruction error for any data in a data segment not included in the training data set. This enables anomalous behavioural data indicative of a new experience encountered by a vehicle when it is being driven around to be identified in the data stream passed through the autoencoder, making it possible to send just the anomalous behavioural data to a back-end fleet server configured to monitor and manage a fleet of vehicles in a timely and bandwidth efficient manner.

Abstract: Computer-implemented methods and processing systems for estimating a probability of failure for different severity levels for an Automated Driving System (ADS) feature in a virtual test environment are provided. The estimation of a probability of crash of different severities may be enabled by utilizing a limit state function (LSF) that attains increasingly negative or positive values after crash (e.g., when TTC=0 or PET=0). This may be achieved by defining a function for severity that is more negative for more severe crashes. The LSF may include a function of the delta speed at collision (i.e., minus delta speed at collision).

Abstract: The present disclosure relates to a method performed by a trajectory monitoring system of a vehicle for assessment of a vehicle control system of an advanced driver-assistance system, ADAS, or autonomous driving, AD, system of the vehicle. The trajectory monitoring system determines in view of a reference system a planned vehicle trajectory adapted to be executed by the vehicle control system during a predeterminable time period (T). The trajectory monitoring system furthermore determines following initiation and/or completion of the time period, an actual vehicle trajectory of the vehicle during the time period in view of the reference system. Moreover, the trajectory monitoring system determines an accuracy deviation measure indicating deviation between the actual vehicle trajectory and the planned vehicle trajectory. The trajectory monitoring system further communicates acknowledgment data indicative of the accuracy deviation measure.

Abstract: The present disclosure relates to systems and methods capable of adaptively estimating time-to-take over during ODD exit events, by estimating the recovery time for driver and the action time required to safely handle the situation. In more detail, the proposed system allows for field monitoring for online verification (i.e., in-vehicle verification) of adaptive hand over time, and for facilitated updating of the systems predicting the hand-over time (i.e., Action Time Network and Reaction Time Network) in a decoupled manner by efficient use of data from field monitoring.

Abstract: A method and system for enabling online perception performance evaluation for an ego-vehicle are disclosed. Method includes obtaining sensor data from a perception system of the ego-vehicle, where the sensor data includes information about at least one object in surrounding environment of ego-vehicle. Method includes obtaining communication data from an external vehicle in surrounding environment of ego-vehicle, where obtained communication data includes at least one physical attribute of external vehicle. Method includes comparing obtained at least one physical attribute of the external vehicle with information about at least one object in order to find an object of at least one object that matches at least one physical attribute. Then, if a match for at least one physical attribute is found, method further includes obtaining world view data indicative of world view of external vehicle, forming a common reference system between the ego-vehicle and external vehicle.

Abstract: The present disclosure relates to methods and system for managing an Automated Driving System of a vehicle. Moreover, the disclosure presents a use of statistical modelling that is utilized to determine if the conditions and/or requirements of the operational design domain (ODD) are met, whereby the ADS validity may be determined. In more detail, the present disclosure relates to the utilization of nearby vehicles' ADS s to reciprocally share their locally updated statistical models. If the statistical models, from the two ADS, collectively show that the baseline statistical model is not valid at this point in time the appropriate actions from each ADS can be taken.

Abstract: Systems and methods for monitoring and managing an Automated Driving System, ADS, of a vehicle is proposed herein. In more detail, the disclosure proposes a system in the vehicle to discern the state of the environment and map it towards a statistical model used for safety assurance. Given this data it is further proposed to conduct updates to the statistical model in the ADS and to transmit the updates of the model rather than the entire sensor data, the trajectories of the surrounding objects or detailed measurements of the environment. Thus, the proposed solution allows for capturing all the events that the ADS is exposed to for updating the global model used for safety assurance of all the ADSs in the vehicle fleet.

Abstract: An apparatus and method performed by a perception comparing system of a vehicle for perception performance evaluation of an ADAS or ADS. The perception comparing system establishes communication with a secondary vehicle determined and/or estimated to be positioned within a potential range of surrounding detecting sensors on-board the vehicle. The system derives perception data from a perception system of the ADAS or ADS. The system receives secondary perception data from a secondary perception system of a secondary ADAS or ADS of the secondary vehicle and determines a discrepancy output based on comparison of at least a portion of the both of perception data and the secondary perception data. The system communicates acknowledgement data when at least a portion of the discrepancy output fulfils discrepancy exceedance criteria and/or when the vehicle is perceivable in the secondary perception data but the secondary vehicle not is locatable in the perception data.