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: 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.