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. The sensor data is associated with a time period and includes information about a surrounding environment of the vehicle during the time period. The method further includes generating a local candidate path to be executed by the ADS based on the generated local world-view of the ADS, and transmitting a first set of data to a remote system. The first set of data is associated with the time period and including information about the surrounding environment of the vehicle during the time period.

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: The present disclosure relates to a control device and method for estimating a drivable space in a surrounding environment of a vehicle. In particular, the disclosure relates to a “free space estimation” solution with reduced validation effort. The control device includes one or more processors adapted to obtain sensor data, determine a surface flatness MF and a surface texture MR of at least one portion of the surrounding environment by means of two independent algorithms, and defining a drivable space based on obtained values.

Abstract: The present disclosure relates to methods and systems for generating a trained learning algorithm configured to predict the presence of environmental conditions in image data, a vehicle for utilizing the algorithm, as well as methods and systems for generating the training data for the automated supervised training of a learning algorithm. In some embodiments the training data is in the form of labelled images generated by a camera device arranged on a vehicle, where the labels are indicative of an environmental sensor activation or deactivation signal, or a user-input signal which serve as a supervisory signal for the training of the learning algorithm.

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: 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 control system for an ego-vehicle traveling in a first direction in a first lane on a road segment where the vehicle has a driver support function for autonomously maneuvering the vehicle. The control system includes control circuitry configured to obtain sensor data including information about a surrounding environment of the vehicle. The control circuitry determines a relative velocity of at least one external vehicle traveling in a second lane adjacent to the first lane based on the obtained sensor data. Moreover, the control circuitry is configured to generate a control signal in to control an availability of the driver support function for the road segment based on the comparison in order to make the driver support function available for an occupant of the vehicle if at least one external vehicle of the external vehicle(s) has been confirmed to have a relative velocity below the maximum velocity threshold.

Abstract: A method for managing a hand-over from an Automated Driving System (ADS) to driver of vehicle, where ADS includes ADS feature being associated with first set of pre-cautionary constraints out of plurality of pre-cautionary constraints imposed by Pre-cautionary Safety (PCS) module while ADS feature controls vehicle. The method includes obtaining request to deactivate ADS feature, and providing partial control of vehicle to driver in order to enable manual driver operation of vehicle based on obtained request. The method includes monitoring manual driver operation of vehicle for a time period, and evaluating monitored manual driver operation of vehicle against second set of pre-cautionary constraints during time period. Then, based on evaluation, the method includes deactivating second set of pre-cautionary constraints upon expiry of time period if manual driver operation passes evaluation, or providing control of vehicle to ADS if manual driver operation fails evaluation.

Abstract: The present disclosure relates to a method for controlling a control system of a vehicle having a first driver support module (e.g. ADAS feature) and a second driver support module (e.g. “under-development” ADS feature). The first driver support module and the second driver support module are capable of operation within an overlapping operational design domain (ODD). The method includes obtaining sensor data including information about a surrounding environment of the vehicle and determining fulfilment of the overlapping ODD based on the obtained sensor data. Further, if the overlapping ODD is fulfilled, the method includes switching between a first configuration where the first driver support module is active and the second driver support module is inactive, and a second configuration where the first driver support module is inactive and the second driver support module is active.

Abstract: There is provided a method for operating a vehicle having an automated driving system (ADS) and a fallback stop feature. The method includes obtaining sensor data and localization data including information about a surrounding environment of the vehicle, and determining a plurality of candidate paths for a prediction time horizon within a drivable area in the surrounding environment of the vehicle based on the sensor data and the localization data. Further, the method includes determining an expected trajectory of a target vehicle located in the surrounding environment of the vehicle for the prediction time horizon based on the obtained sensor data and localization data, and determining, for each candidate path, an overlap cost parameter for an overlap between the target vehicle's expected trajectory and a set of stop positions of the vehicle based on predicted executions of the fallback stop feature within the prediction time horizon.

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: A method for evaluating a minimum braking distance of a vehicle, in particular a car. The method comprises the step of obtaining at least one image in a movement direction of the vehicle associated substantially with an actual location of vehicle. A first road type indication from the at least one image is determined by a trained neural network architecture. Second road type indication associated with the actual location of the car are obtained from a database and compared with the first road type indication. If the second road type indication supports the determined first road type indication, an adjustment parameter associated with one of the at least first and second road type indication is selected. If second road type indication does not support the determined first road type indication, a default adjustment parameter as adjustment parameter is selected. Finally, a minimum braking distance using the adjustment parameter is set.

Abstract: A control device that generates manoeuvring decisions for an ego-vehicle in a traffic scenario is provided. The control device includes a first module including a trained self-learning model, the first module being configured to receive data including information about a surrounding environment of the ego-vehicle determine, using the trained self-learning model, an action to be executed by the ego-vehicle based on the received data. The control device includes a second module configured to receive the determined action, receive data including information about the surrounding environment of the ego-vehicle during a finite time horizon, predict an environmental state for a first time period of the finite time horizon, determine a trajectory for the ego-vehicle based on the received action for the finite time horizon and on the predicted environmental state for the first time period, send a signal in order to control the ego-vehicle according to the determined trajectory.

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: 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: The present disclosure relates to a method performed by a luminance assessment system of a vehicle for monitoring of on-board vehicle image capturing device functionality compliance with a predeterminable requirement level. The luminance assessment system obtains at respective one or more time instants (t1, t2, t3), a first image and a second image. A first section of the first images and a second section of the second images respectively cover a primary region. A first luminance value of the first section and a second luminance value of the second section are measured. A luminance deviation is determined by comparing at least one of the first luminance values to at least one of the second luminance values. A determination is made that, when the luminance deviation exceeds a deviation threshold, the first image capturing device or the second image capturing device failed to function according to the requirement level.

Abstract: A method performed by a road identifying system of a vehicle for localization of the vehicle in a multi-level road system. The road identifying system determines with support from a positioning system, a position of the vehicle, identifies, based on the vehicle position with support from at least a first digital map, a multi-level road system in which the vehicle is positioned and obtains image data with support from one or more image capturing devices adapted to capture surroundings of the vehicle. Moreover, the road identifying system determines a road level at which the vehicle is positioned based on feeding the image data through a neural network trained to classify road level based on context of image content and identifies based on the determined road level, with support from the at least first digital map, a road and/or lane of the multi-level road system at which the vehicle is positioned.

Abstract: Method and device for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A device (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 (220) and at least one virtual AD/ADAS vehicle (210) operating according to said AD/ADAS functionality. The device allows (303) a user of the device (101; 500) to, via user interface (506), control said one or more human controlled movable virtual objects (220) during operation and thereby cause generation of scenarios that the virtual AD/ADAS vehicle (210) is subjected to.

Abstract: The present disclosure relates to a method to control a vehicle system. The method includes detecting a deviating vehicle having at least one of a deviating behaviour and a deviating vehicle property by means of a perception system of a first vehicle, wherein the perception system includes at least one sensor device configured to monitor a surrounding environment of the first vehicle; assigning a deviating vehicle classification to the deviating vehicle based on the deviating behaviour and/or deviating vehicle property; determining at least one second vehicle to receive information relating to the deviating vehicle; and transmitting to each determined second vehicle a set of information relating to the deviating vehicle, said set of information including at least one of the deviating vehicle classification and a predetermined instruction to be performed by the determined second vehicle, wherein the predetermined instruction is dependent on the deviating vehicle classification.

Abstract: Method for estimating a vertical offset of road segment for vehicle is disclosed. The method includes tracking road reference on road segment at first moment in time from first observation point in order to form first representation. Method includes obtaining vehicle movement data over time period extending from first moment in time to second moment in time, tracking road reference on road segment at second moment in time from second observation point in order to form second representation.