Abstract: The subject-matter of the present disclosure relates to a computer-implemented method of generating a trajectory for an autonomous vehicle, AV, using an autonomy stack. The autonomy stack includes an end-to-end network trained to generate a trajectory for the AV from sensor inputs, a tracking module and a planning module. The computer-implemented method comprises: receiving, by the tracking module, a plurality of objects identified based on sensor inputs; fusing, by the tracking module, the plurality of objects; and generating, using the planning module, a further trajectory for the AV based on the fused plurality of objects and the trajectory generated by the end-to-end network.

Abstract: Systems and methods for generating a trajectory for an autonomous vehicle, AV, using an autonomy stack are disclosed. The autonomy stack can include a first component for generating a AV trajectory based on sensor inputs and a second component for adjusting the trajectory based on the sensor inputs. Both the first and second components can each include a perception module and a planning module. The method can include: identifying, using a perception module of a first component, objects based on sensor inputs; generating, using a planning module of the first component, a trajectory for the AV based on the objects identified by the perception module of the first component; identifying, using a perception module of a second component, objects based on the sensor inputs; and adjusting, using a planning module of the second component, the trajectory based on the objects identified by the perception module of the second component.

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.