Abstract: The present invention relates to a computer-implemented method of controlling an autonomous vehicle. The method comprises: receiving current sensor data defining a current scenario; determining a distance between the current scenario and a closest scenario from a database of known scenarios; and controlling the autonomous vehicle by: manoeuvring the autonomous vehicle within a manoeuvring constraint associated with the closest scenario when the distance is below a first threshold; performing a minimal risk manoeuvre when the distance is above a second threshold, wherein the second threshold is greater than the first threshold; and interpolating the manoeuvring constraint associated with the closest scenario when the distance is between the first threshold and the second threshold, and manoeuvring the autonomous vehicle within the interpolated manoeuvring constraint.

Abstract: The present invention relates to a computer-implemented method of generating a descriptor associated with data of a first modality.

Abstract: A computer-implemented method of generating trajectories of actors, the method comprising: simulating a first scenario comprising an environment having therein an ego-vehicle, a set of actors, including a first actor, and optionally a set of objects, including a first object, wherein simulating the first scenario comprises using a first trajectory of the first actor; observing, by a first adversarial reinforcement learning agent, a first observation of the environment, for example the ego-vehicle, a second actor of the set thereof and/or the first object of the set thereof, in response to the first trajectory of the first actor; and generating, by the first agent, a second trajectory of the first actor based on the observed first observation of the environment.

Abstract: A computer-implemented method of generating training data, the method comprising: providing a representation of an environment, wherein the representation of the environment has a defined structure and/or a defined geometry; and generating the training data comprising a set of transformed representations, including a first transformed representation, of the environment by transforming the representation of the environment to the set of transformed representations, including the first transformed representation, of the environment; wherein providing the representation of the environment comprises synthesizing, at least in part, an image of the environment using semantic information.

Abstract: A computer-implemented method of generating trajectories of actors, the method comprising: simulating a first scenario comprising an environment having therein an ego-vehicle, a set of actors, including a first actor, and optionally a set of objects, including a first object, wherein simulating the first scenario comprises using a first trajectory of the first actor; observing, by a first adversarial reinforcement learning agent, a first observation of the environment, for example the ego-vehicle, a second actor of the set thereof and/or the first object of the set thereof, in response to the first trajectory of the first actor; and generating, by the first agent, a second trajectory of the first actor based on the observed first observation of the environment.

Abstract: A computer-implemented method of generating trajectories of actors, the method comprising: simulating a first scenario comprising an environment having therein an ego-vehicle, a set of actors, including a first actor, and optionally a set of objects, including a first object, wherein simulating the first scenario comprises using a first trajectory of the first actor; observing, by a first adversarial reinforcement learning agent, a first observation of the environment, for example the ego-vehicle, a second actor of the set thereof and/or the first object of the set thereof, in response to the first trajectory of the first actor; and generating, by the first agent, a second trajectory of the first actor based on the observed first observation of the environment.