Abstract: A computer-implemented method of processing images for extracting information about known objects comprises the steps of receiving an image containing a view of a known object at a scale dependent on an object distance of the known object from an image capture location of the image; determining, from a world model representing one or more known objects in the vicinity of the image capture location, an object location of the known object, the object location and the image capture location defined in a world frame of reference; and based on the image capture location and the object location in the world frame of reference, applying image scaling to the image, to extract a rescaled image containing a rescaled view of the known object at a scale that is substantially independent of the object distance from the image capture location.

Abstract: A computer implemented method of path verification in a computer system is described. A user provides input to mark a displayed image of a scenario. A path is generated representing the trajectory of a vehicle. Control points along the path are recorded, each control point associated with a vehicle position and target speed. A vehicle position end target speed of two control points is used to calculate at least one path verification parameter which defines how a vehicle travelling along the path would behave. The at least one verification parameter is compared with a corresponding threshold value; and an alert is generated to the user at the user interface when the at least one path verification parameter exceeds the corresponding threshold value.

Abstract: The invention provides a computer-implemented method of planning a path for a mobile robot such as an autonomous vehicle in the presence of K obstacles. The method uses, for each of the K obstacles, a shape Bk and a density function pk(x) representing the probabilistic position of the obstacle. The method repeats the following steps for at least two different paths A: —choosing a path A, where A is the swept area of the robot within a given time interval; and—calculating based on the density function of each obstacle and the swept path an upper bound on the total probability of at least one collision FD between the robot and the K obstacles. This allows a number of candidate paths to be ranked for safety. By precomputing factors of the computational steps over K obstacles, the computation per path is O(N), and not O(NK). A safety threshold can be used to filter out paths below that threshold.

Abstract: A computer system for analysing driving scenes in relation to an autonomous vehicle (AV) operational design domain (ODD), the computer system comprising: an input configured to receive a definition of the ODD in a formal ontology language; a scene processor configured to receive data of a driving scene and extract a scene representation therefrom, the data comprising an ego trace, at least one agent trace, and environmental data about an environment in which the traces were captured or generated, wherein the scene representation is an ontological representation of both static and dynamic elements of the driving scene extracted from the traces and the environmental data, and expressed in the same formal ontology language as the ODD; and a scene analyzer configured to match the static and dynamic elements of the scene representation with corresponding elements of the ODD, and thereby determine whether or not the driving scene is within the defined ODD.

Abstract: Abstract: A driving scenario is extracted from real-world driving data captured within a road layout. A simulation is run based on the extracted driving scenario, in which an ego agent and a simulated non-ego agent each exhibit closed-loop behaviour. The closed-loop behaviour of the ego agent is determined by autonomous decisions taken in an AV stack under testing in response to simulated inputs, reactive to the simulated agent. The closed-loop behaviour of the non-ego agent is determined by implementing an inferred goal or behaviour, reactive to the ego agent. The goal or behaviour is inferred from an observed trace of a real-world agent extracted from the real-world driving data.

Abstract: A computer implemented method of generating a scenario to be run in a simulation environment for testing the behaviour of an autonomous vehicle is described. An image is rendered on a display. A user can mark multiple locations to create at least one path for an agent vehicle in the rendered image. A path is generated which passes through the locations and rendered on the display. A user can define at least one behavioural parameter for controlling behaviour of the agent vehicle associated with the at least one path when the scenario is run in a simulation environment. The scenario is recorded for future use.

Abstract: In one aspect, hierarchical image segmentation is applied to an image formed of a plurality of pixels, by classifying the pixels according to a hierarchical classification scheme, in which at least some of those pixels are classified by a parent level classifier in relation to a set of parent classes, each of which is associated with a subset of child classes, and each of those pixels is also classified by at least one child level classifier in relation to one of the subsets of child classes, wherein each of the parent classes corresponds to a category of visible structure, and each of the subset of child classes associated with it corresponds to a different type of visible structure within that category.

Abstract: A computer-implemented method of evaluating the performance of a full or partial autonomous vehicle (AV) stack in simulation, the method comprising: applying an optimization algorithm to a numerical performance function defined over a scenario space, wherein the numerical performance function quantifies the extent of success or failure of the AV stack as a numerical score, and the optimization algorithm searches the scenario space for a driving scenario in which the extent of failure of the AV stack is substantially maximized, wherein the optimization algorithm evaluates multiple driving scenarios in the search space over multiple iterations, by running a simulation of each driving scenario in a simulator, in order to provide perception inputs to the AV stack, and thereby generate at least one simulated agent trace and a simulated ego trace reflecting autonomous decisions taken in the AV stack in response to the simulated perception inputs, wherein later iterations of the multiple iterations are guided by the

Abstract: A computer-implemented method of predicting behaviour of an agent for executing an objective of a mobile robot in the vicinity of the agent, in dependence on the predicted behaviour comprises: determining a reference path, wherein multiple actions are available to the agent, and the reference path relates to one of those actions; projecting a measured velocity vector of the agent onto a reference path, thereby determining a projected speed value for the agent along the reference path; computing predicted agent motion data for the agent along the reference path based on the projected speed value; and generating a series of control signals for controlling a mobile robot to fulfil the objective in dependence on the predicted agent motion data.

Abstract: A method of annotating frames of a time sequence of frames captured by at least one travelling vehicle comprises, in a frame processing system: determining a three-dimensional (3D) road model for an area captured in the time sequence of frames; receiving first annotation data denoting a known 3D location of a moving object for a first frame of the time sequence of frames; and automatically generating second annotation data for marking an expected moving object location in at least a second frame of the time sequence of frames, by assuming the moving object moves along an expected path determined from the known 3D location and the 3D road model.

Abstract: A computer system for planning mobile robot trajectories, the computer system comprising: an input configured to receive a set of scenario description parameters describing a scenario and a desired goal for the mobile robot therein; a runtime optimizer configured to compute a final mobile robot trajectory that substantially optimizes a cost function for the scenario, subject to a set of hard constraints that the final mobile robot trajectory is guaranteed to satisfy; and a trained function approximator configured to compute, from the set of scenario description parameters, initialization data defining an initial mobile robot trajectory.

Abstract: A computer-implemented method of determining control actions for controlling a mobile robot comprises: receiving a set of scenario description parameters describing a scenario and a desired goal for the mobile robot therein; in a first constrained optimization stage, applying a first optimizer to determine a first series of control actions that substantially globally optimizes a preliminary cost function for the scenario, the preliminary cost function based on a first computed trajectory of the mobile robot, as computed by applying a preliminary robot dynamics model to the first series of control actions, and in a second constrained optimization stage, applying a second optimizer to determine a second series of control actions that substantially globally optimizes a full cost function for the scenario, the full cost function based on a second computed trajectory of the mobile robot, as computed by applying a full robot dynamics model to the second series of control actions; wherein initialization data of at l

Abstract: Ego actions for a mobile robot in the presence of at least one agent are autonomously planned. In a sampling phase, a goal for an agent is sampled from a set of available goals based on a probabilistic goal distribution, as determined using an observed trajectory of the agent. An agent trajectory is sampled, from a set of possible trajectories associated with the sampled goal, based on a probabilistic trajectory distribution, each trajectory of the set of possible trajectories reaching a location of the associated goal. In a simulation phase, an ego action is selected from a set of available ego actions and based on the selected ego action, the sampled agent trajectory, and a current state of the mobile robot, (i) behaviour of the mobile robot, and (ii) simultaneous behaviour of the agent are simulated, in order to assess the viability of the selected ego action.

Abstract: A computer-implemented method of determining a series of control signals for controlling an autonomous vehicle to implement a planned speed change maneuver comprises: receiving from a maneuver planner a position target for the planned speed change maneuver; selecting, from a predetermined family of kinematic functions, a kinematic function for carrying out the planned speed change maneuver, each kinematic function being a first or higher order derivative of acceleration with respect to time; and using the selected kinematic function to determine a series of control signals for implementing the planned speed change maneuver; wherein the kinematic function is selected in a constrained optimization process as substantially optimizing a cost function defined for the speed change maneuver, subject to a set of hard constraints that: (i) require a final acceleration, speed and position corresponding to the selected kinematic function to satisfy, respectively, an acceleration target, a speed target and the position t

Abstract: There is provided an adaptive cruise control method for autonomously adapting the speed of an ego vehicle (300) to maintain a target headway, headway being distance from the ego vehicle to a forward vehicle (302), the ego vehicle equipped with a perception system (100) for measuring a current headway and a current speed and acceleration of the forward vehicle relative to ego vehicle, the method comprising: in response to detecting that the current headway is below the target headway, determining and implementing a deceleration strategy for increasing to the target headway; wherein the deceleration strategy is determined so as to selectively optimize for comfort in dependence on a predicted headway, the predicted headway computed for a future time instant based on the current speed and acceleration of the forward vehicle relative to the ego vehicle.

Abstract: A method of training a 3D structure detector to detect 3D structure in 3D structure representation, the method comprising the following steps: receiving, at a trainable 3D structure detector, a set of training inputs, each training input comprising at least one 3D structure representation; the 3D structure detector determining, for each training input, a set of predicted 3D objects for the at least one 3D structure representation of that training input; and training the 3D structure detector to optimize a cost function, wherein the cost function penalizes deviation from an expected geometric relationship between the set of predicted 3D objects determined for each training in put.

Abstract: A method of annotating road images, the method comprising implementing, at an image processing system, the following steps: receiving a time sequence of two dimensional images as captured by an image capture device of travelling vehicle; processing the images to reconstruct, in three-dimensional space, a path travelled by the vehicle; using the reconstructed vehicle path to determine expected road structure extending along the reconstructed vehicle path; and generating road annotation data for marking at least one of the images with an expected road structure location, by performing a geometric projection of the expected road structure in three-dimensional space onto a two-dimensional plane of that image.

Abstract: A computer-implemented method of creating one or more annotated perception inputs, the method comprising, in an annotation computer system: receiving a plurality of captured frames, each frame comprising a set of 3D structure points, in which at least a portion of a common structure component is captured; computing a reference position within at least one reference frame of the plurality of frames; generating a 3D model for the common structure component by selectively extracting 3D structure points of the reference frame based on the reference position within that frame; determining an aligned model position for the 3D model within a target frame of the plurality of frames based on an automatic alignment of the 3D model with the common structure component in the target frame; and storing annotation data of the aligned model position in computer storage, in association with at least one perception input of the target frame for annotating the common structure component therein.

Abstract: Herein, a “perception statistical performance model” (PSPM) for modeling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modeled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.

Abstract: Herein, a “perception statistical performance model” (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A first PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled, in order to compute a first time series of perception outputs. A second time series of perception outputs is computed using a second PSPM for modelling a second perception slice of the runtime stack, the first PSPM learned from data of a first sensor modality of the perception slice and the time series, and the second PSPM learned independently thereof from data of a second sensor modality of the second perception slice and the second time series.