Abstract: Systems and method for updating an accumulated 3D map are disclosed. A current point cloud is received, which is an untwisted lidar point cloud captured over a current interval, each point in the current point cloud associated with feature data indicating a feature type of each point of the current point cloud. Respective subsets of the current point cloud are provided to a plurality of processing threads, wherein each processing thread operates on its point cloud subset in parallel with the other processing thread(s) to perform the following mapping operations: compare each point of its point cloud subset with features of the accumulated 3D map to identify a corresponding feature of the same feature type in the accumulated 3D map, compute a distance between each point of its subset and the corresponding feature in the accumulated point cloud, and determine a derivative of each distance with respect to lidar pose change.

Abstract: A computer-implemented method of computer-implemented method of perceiving structure in a point cloud comprises: applying clustering to the point cloud, and thereby identifying at least one moving object cluster within the point cloud, the point cloud comprising time-stamped points captured over a non-zero accumulation window; determining a motion model for the moving object cluster, by fitting one or more parameters of the motion model to the time-stamped points of that cluster; using the motion model to transform the time-stamped points of the moving object cluster to a common reference time; and applying a perception component to the transformed points of the moving object cluster to extract information about structure exhibited in the transformed points.

Abstract: A computer-implemented method of planning an ego trajectory for an ego robot in an environment in the presence of an occluding object, the method comprising: determining a trajectory cost function having one or more planning variables that define a planned ego trajectory, the planning variables tuneable to modify the planned ego trajectory, wherein the trajectory cost function is dependent on a predicted occluded region of the environment, as defined by the planned ego trajectory and a predicted state of the occluding object; and tuning the planning variables using an optimizer applied to the trajectory cost function, wherein the trajectory cost function encodes: (i) a progress objective that rewards modifications to the planned ego trajectory that progress the ego robot towards a chosen goal location, and (ii) a visibility objective, dependent on the predicted occluded region, that rewards modifications to the planned ego trajectory that improve visibility of the environment from the perspective of the ego r

Abstract: A computer-implemented method of generating road annotation data for annotating an electronic map, the method comprising accessing from persistent storage an electronic map defining a road network; generating a road topology graph encoding a topology of the road network, the road topology graph comprising nodes representing road structure elements and edges representing links between road structure elements; performing a search of the road topology graph for a predetermined graph structure; responsive to identifying a subgraph of the road topology graph exhibiting the predetermined graph structure, generating annotation data for marking in the electronic map a portion of the road network corresponding to the subgraph; and generating in persistent storage an augmented map comprising map data defining the portion of the road network and the annotation data.

Abstract: An encoder is trained together with a perception component based on a training set comprising unannotated sensor data sets and annotated sensor data sets in a sequence of multiple training steps.

Abstract: A computer implemented method of training an encoder to extract features from sensor data comprises training a machine learning (ML) system based on a self-supervised loss function applied to a training set, the ML system comprising the encoder. The training set comprises first data representations and corresponding second data representations, wherein the encoder extracts features from each first and second data representation, and wherein the self-supervised loss function encourages the ML system to associate each first data representation with its corresponding second data representation based on their respective features.

Abstract: A computer-implemented method of predicting an external actor trajectory comprises receiving, at a computer, sensor inputs for detecting and tracking an external actor; applying object tracking to the sensor inputs, in order track the external actor, and thereby determine an observed trace of the external actor over a time interval; determining a set of available goals for the external actor; for each of the available goals, determining an expected trajectory model; and comparing the observed trace of the external actor with the expected trajectory model for each of the available goals, to determine a likelihood of that goal.

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.