ZENUITY AB

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.