Abstract: The present disclosure relates to a computer-implemented method of testing an autonomy stack for an autonomous vehicle in a simulated environment. The computer-implemented method comprises: receiving a scenario, a non-ego agent, and an ego agent, the scenario representing a real-world environment, wherein the ego agent is controlled by the autonomy stack; running a simulation of the scenario including the ego agent and the non-ego agent; receiving, from a user input device, a control change input; switching control of the non-ego agent between the autonomy mode and the manual mode in response to receiving the control change input; recording a sequence of events involving the ego agent to form a report; and sending a signal including the report to a report log.

Abstract: A computer-implemented method of generating a lane boundary model of a route traversed by an autonomous vehicle According to the present invention, there is provided a computer-implemented method of generating a lane boundary model of a route traversed by an autonomous vehicle. The computer-implemented method comprises: obtaining a three-dimensional LiDAR point cloud of a route and an image of the route traversed by the autonomous vehicle; detecting, using a machine learning model, a lane boundary in the image of the route; and generating a lane boundary model based on a plurality of points of the three-dimensional LiDAR point cloud of the route that correspond positionally with the lane boundary detected in the time.

Abstract: The subject-matter of the present disclosure relates to a printed circuit board circuit board. The printed circuit board comprises: a plurality of terminals for connecting to a controller area network, CAN, bus of a vehicle, a gear shift module, GSM, and an electronic control unit, ECU, the CAN bus configured to receive user control inputs from one or more control interfaces of the vehicle and to send gear selection control signals to gears of the vehicle, the GSM configured to select one gear of the gears based on the control inputs, the ECU configured to modify the gear selection control signals based on a control command from an autonomy stack; a stop terminal for connecting to a stop input; and a plurality of circuits configured to: connect the CAN bus to the ECU and the ECU to the GSM when the stop input is disengaged; and connect the CAN bus to the GSM and bypass the ECU when the stop input is engaged.

Abstract: The present invention relates to a computer-implemented method of determining a trajectory of an autonomous vehicle. The method comprises: receiving a candidate path for the autonomous vehicle to travel, and positional information of one or more objects; determining a plurality of passing strategies, each passing strategy comprising a passing action defining how the autonomous vehicle is constrained to pass the or each object; ranking the plurality of passing strategies; and determining a trajectory along the path for a highest ranked passing strategy.

Abstract: The present invention relates to a method of controlling gears in a vehicle. The method comprising: receiving, by an electrical control unit, ECU, associated with an autonomy stack of the vehicle, a gear selection control signal sent by a gear selection module, GSM, to the gears of the vehicle to select a gear from a plurality of gears; modifying, by the ECU, the gear selection control signal based on a target trajectory of the vehicle generated by the autonomy stack; sending, by the ECU, the modified gear selection control signal to the gears through a controller area network, CAN; then, sending, by the ECU, a power cycling signal to the GSM to turn a power of the GSM off then subsequently turn the power of the GSM back on, after a pre-determining power cycling time.

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 localizing a radar sensor, the method comprising: obtaining a first radar scan of a first environment of the radar sensor, wherein the first radar scan comprises a set of power-range spectra, including a first power-range spectrum; extracting a first set of landmarks, including a first landmark, from the first radar scan, wherein the first landmark is defined by a range and an azimuth; computing a respective first set of descriptors, including a first descriptor, of the first set of landmarks, wherein the first descriptor defines the first landmark by respective relative ranges and azimuths in relation to one or more landmarks included in the first set of landmarks; accessing one or more reference sets of landmarks of respective environments and computing respective reference sets of descriptors of the reference sets of landmarks; matching the first set of descriptors to a corresponding first reference set of descriptors; and localizing a first location of the radar sensor usi

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 localizing a radar sensor, the method comprising: obtaining a first radar scan of a first environment of the radar sensor, wherein the first radar scan comprises a set of power-range spectra, including a first power-range spectrum; extracting a first set of landmarks, including a first landmark, from the first radar scan, wherein the first landmark is defined by a range and an azimuth; computing a respective first set of descriptors, including a first descriptor, of the first set of landmarks, wherein the first descriptor defines the first landmark by respective relative ranges and azimuths in relation to one or more landmarks included in the first set of landmarks; accessing one or more reference sets of landmarks of respective environments and computing respective reference sets of descriptors of the reference sets of landmarks; matching the first set of descriptors to a corresponding first reference set of descriptors; and localizing a first location of the radar sensor usi

Abstract: The present disclosure relates to a computer-implemented method of testing an autonomy stack for an autonomous vehicle in a simulated environment. The computer-implemented method comprises: receiving a scenario, a non-ego agent, and an ego agent, the scenario representing a real-world environment, wherein the ego agent is controlled by the autonomy stack; running a simulation of the scenario including the ego agent and the non-ego agent; receiving, from a user input device, a control change input; switching control of the non-ego agent between the autonomy mode and the manual mode in response to receiving the control change input; recording a sequence of events involving the ego agent to form a report; and sending a signal including the report to a report log.

Abstract: The present invention relates to a computer-implemented method of comparing an autonomy stack with an updated autonomy stack in a simulator. The computer-implemented method comprising: retrieving a portion of telemetry data recorded by the autonomy stack, the telemetry data representing a real-world environment in which an autonomous vehicle operated, the autonomous vehicle controlled by the autonomy stack; generating a simulation of the real-world environment using the telemetry data; providing an ego-vehicle in the simulation at a position of the autonomous vehicle associated with the telemetry data, the ego-vehicle controlled in the simulation by the autonomy stack; providing an updated ego-vehicle in the simulation, the updated ego-vehicle controlled in the simulation by the updated autonomy stack; generating a real-time simulation by positionally synchronising the ego-vehicle with the updated ego-vehicle; and displaying the real-time simulation on a display device.

Abstract: According to the present disclosure there is provided a wiper assembly for removing contaminants from a surface of a housing provided around a sensor, comprising: a drive system; a moveable part connected to the drive system to be driven by the drive system; and at least one wiper connected to the moveable part, wherein the drive system is configured to drive the moveable part, thereby to cause movement of the at least one wiper at least partially around the housing thereby to wipe the surface of the housing to remove contaminants therefrom. Further, according to the present disclosure there is provided: a sensor assembly comprising a sensor and a wiper assembly; a vehicle; and a method of operating a wiper assembly.

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 localizing a radar sensor, the method comprising: obtaining a first radar scan of a first environment of the radar sensor, wherein the first radar scan comprises a set of power-range spectra, including a first power-range spectrum; extracting a first set of landmarks, including a first landmark, from the first radar scan, wherein the first landmark is defined by a range and an azimuth; computing a respective first set of descriptors, including a first descriptor, of the first set of landmarks, wherein the first descriptor defines the first landmark by respective relative ranges and azimuths in relation to one or more landmarks included in the first set of landmarks; accessing one or more reference sets of landmarks of respective environments and computing respective reference sets of descriptors of the reference sets of landmarks; matching the first set of descriptors to a corresponding first reference set of descriptors; and localizing a first location of the radar sensor usi

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.