Abstract: Systems and method are provided for a driving run performed by a sensor-equipped robot in a driving scene comprising at least one dynamic signalling agent (DSA). DSA data indicating, signalling states of the at least one DSA as a function of time is received, and a graphical user interface (GUI), comprising a schematic representation of the run and at least one DSA state timeline showing a visual indicator of the current signalling state of a corresponding one of the at least one DSA, is rendered on the GUI.

Abstract: An occlusion metric is computed for a target object in a 3D multi-object simulation. The target object is represented in 3D space by a collision surface and a 3D bounding box. In a reference surface defined in 3D space, a bounding box projection is determined for the target object with respect to an ego location. The bounding box projection is used to determine a set of reference points in 3D space. For each reference point of the set of reference points, a corresponding ray is cast based on the ego location, and it is determined whether the ray is an object ray that intersects the collision surface of the target object. For each such object ray, it is determined whether the object ray is occluded. The occlusion metric conveys an extent to which the object rays are occluded.

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: The present disclosure relates to a computer-implemented method of training a generative model to insert an object in spatial sensor data. The method comprises receiving a training sample of spatial sensor data and receiving an indication of a 3D geometric property of an object captured in the training sample. A portion of spatial sensor data corresponding to the object is removed from the training sample, resulting a cropped training sample. The generative model is trained to reconstruct the training sample from the cropped training sample by, providing to the generative model: the cropped training sample as a target input, an indication of the object as a reference input, and the 3D geometric property of the object as a conditioning input. This results in a generated output sample of spatial sensor data.

Abstract: A position target for a planned speed change maneuver is received. From a predetermined family of kinematic functions, a kinematic function for carrying out the planned speed change maneuver, is determined. The kinematic function is a first or higher order derivative of acceleration with respect to time, and is computed in a constrained optimization process as substantially optimizing a cost function defined for the planned speed change maneuver, subject to a set of hard constraints.

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 computer-implemented method of evaluating the performance of a target planner for an ego robot comprises receiving evaluation data for evaluating the performance of the target planner in the scenario, generated by applying the target planner at incrementing planning steps, to compute a series of ego plans that respond to changes in the scenario and are implemented in the scenario to cause changes in an ego state. The evaluation data includes the ego plan computed by the target planner at one of the planning steps, and a scenario state at a time instant of the scenario. The evaluation data is used to evaluate the target planner by computing a reference plan for said time instant based on the scenario state, the scenario state including the ego state at that time instant, and computing at least one evaluation score for comparing the ego plan with the reference plan.

Abstract: A computer-implemented method of assessing performance of perception component, the perception component for interpreting structure in a scene comprises: receiving a set of multiple computed outputs obtained by applying the perception component to the scene, wherein each computed output comprises a confidence score: generating, from the set of multiple computed outputs, multiple pseudo-ground truth sets, wherein each pseudo-ground truth set comprises, for each computed output, a pseudo-ground truth output sampled from a set of possible ground truth outputs based on a probability distribution defined by the confidence score of the computed output; computing a performance score for the perception component applied to the scene with respect to each pseudo-ground truth set, by comparing the set of multiple outputs with that pseudo-ground truth set; and computing an overall performance score for the perception component applied to the scene, by aggregating the performance scores computed with respect to the multip

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: The present disclosure relates to techniques for locating and modelling a 3D object captured by a mobile robot. A cost function is defined over a set of variables, and is applied to sensor data. The set of variables comprises shape parameters of a 3D object model and a time sequence of poses of the 3D object model. The cost function penalizes inconsistency between the sensor data and the set of variables. The object belongs to a known object class, and the 3D object model or the cost function encodes expected 3D shape information associated with the known object class. The 3D object is modelled by tuning poses of the object and the shape parameters, to optimize the cost function.

Abstract: The present disclosure relates to techniques for training a generative model to insert an object in spatial sensor data. A first training sample of spatial sensor data of a first sensor modality, and a second training sample of spatial sensor data of a second sensor modality are received, the first training sample and the second training sample capture a common object. A first portion of sensor data corresponding to the object is removed from the first training sample, resulting a cropped training sample. A second portion of spatial sensor data corresponding to the common object is extracted from the second training sample.

Abstract: A computer-implemented method of determining control signals for controlling an autonomous vehicle to implement a slowdown manoeuvre, comprising: detecting an obstacle at a distance ahead of the autonomous vehicle; comparing the distance with a threshold value and implementing a slowdown manoeuvre in dependence on the comparison, the slowdown manoeuvre selected from: a first slowdown manoeuvre carried out by a kinematic function, which is a time derivative of acceleration, in which a constraint optimisation has been applied to optimise a cost function of the slowdown manoeuvre subject to a set of hard constraints that require a final acceleration, speed and position to satisfy respective acceleration, speed and position targets, given an initial speed and acceleration of the vehicle, and impose a jerk magnitude upper limit; and a second slowdown manoeuvre implemented in an adaptive cruise control mode which aims to reach a target headway between the autonomous vehicle and the obstacle.

Abstract: A computer-implemented method of planning ego actions for a mobile robot in the presence of at least one agent, comprising: searching for an optimal ego action in multiple search steps, each comprising: selecting an ego action from a set of possible ego actions, selecting an agent behaviour from a set of possible agent behaviours, running a simulation based on the selected ego action and agent behaviour, determining a possible outcome, and assigning a reward to the selected ego action, based on a reward metric, wherein selection of the ego action in later search steps is biased towards higher reward ego action(s) but selection of the agent behaviour in later search steps is biased towards riskier agent behaviour(s), a risky agent behaviour being, according to earlier search steps, more likely to result in a lower reward outcome and choosing an ego action based on the rewards computed in the search steps.

Abstract: A computer-implemented method of modelling a perception system for perceiving objects captured in sensor data comprises: receiving a plurality of training examples, each comprising a ground truth scene for a set of sensor data and a corresponding perceived scene obtained by applying the perception system to the set of sensor data; fitting to the training examples noise model parameters, encoding a noise distribution over perceived scenes given a misdetection scene, and misdetection model parameters, encoding a misdetection distribution over misdetection scenes given a ground truth scene; computing a perception distribution over perceived scenes for a given ground truth scene by marginalizing the product of noise and misdetection distributions over multiple misdetection scenes, wherein individual objects in the ground truth scene are not associated with individual objects in the perceived scenes; fitting the noise and misdetection model parameters to match the perception distribution to the perceived scene for

Abstract: A computer system for rendering a graphical user interface for visualising runs of a driving scenario in which an ego agent navigates a road layout, comprising an input configured to receive a map of the road layout and run data comprising a sequence of timestamped ego agent states and a time-varying numerical score quantifying the performance of the ego agent with respect a set of run evaluation rules; and a rendering component configured to cause a graphical user interface to display, for each rule: a plot of the time-varying numerical score, and a marker denoting a selected time index of the plot, the marker movable along the time axis to change the selected time index; and a scenario visualization comprising a visualization of the run at the selected time index, whereby moving the marker along the time axis causes the scenario visualisation to update as the time index is changed.

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: A method of predicting trajectories for agents of a scenario, the method comprising, for each agent generating an agent feature vector based on one or more observed past states of the agent, computing a set of pairwise feature vectors, each computed as a combination of the agent feature vector for that agent with a respective agent feature vector generated for each other agent of the scenario, processing the pairwise feature vectors as independent inputs to one or more interaction layers of a trajectory prediction neural network to generate a pairwise output for each pairwise feature vector, aggregating the pairwise outputs over the other agents of the scenario to generate an interaction-based feature representation for each agent, processing the interaction-based feature representation in one or more prediction layers of the trajectory prediction neural network, and generating, based on the output of the one or more prediction layers, at least one predicted trajectory for each agent.

Abstract: A method for testing performance of a stack for planning ego vehicle trajectories in real or simulated driving scenarios applying driving rules to the scenario ground truth for evaluating the performance of the stack in the scenario, and providing output indicating whether each driving rule has been complied with; wherein the driving rules include at least one ODD-based response rule, wherein applying the ODD-based response rule includes processing the scenario ground truth over multiple time steps, to determine whether the scenario is within the defined ODD at each time step, and thereby detecting a change in the scenario that takes the scenario outside the defined ODD, and processing the internal state data, to determine whether a state change occurred within the stack, within a time interval, the output for the at least one ODD-based response rule indicating whether the state change occurred within the time interval.

Abstract: A computer system configured to run queries on a static road layout, the computer system comprising: computer storage configured to store the static road layout, the static road layout comprising a section of road having a set of multiple road attributes, each road attribute described throughout the section of road by a describing function that exhibits a change in form at one or more change points along the section of road, the change points of a first of the road attributes exhibiting longitudinal misalignment with respect to the change points of a second of the road attributes; a road partitioning component configured to process the static road layout, and thereby partition the section of road into a sequence of road parts, each road part defined by a longitudinal coordinate interval, in which the describing function of every one of the road attributes has a form that is fixed throughout; a road indexing component configured to generate a road partition index having an entry for each road part, the entry i

Abstract: The disclosure provides systems and methods for identifying salient test runs involving an autonomous vehicle system. A processor receives sets of run data, each set representative of a driving scenario. For each set, an output set is generated, the output set comprising time-indexed events generated in response to a detected behaviour of at least one challenger agent, and a sequence of decision indicators indicating whether a driving action by an ego agent would be permissible. A data retrieval component is coupled to a results database and retrieves output sets based on the time-indexed events and the sequence of decision indicators. The processor generates the sequence of decision indicators by generating a planned trajectory of the ego agent, and determining whether the predefined driving action by the ego agent would be permissible.