Abstract: A directed search method is applied to a parameter space of a scenario for testing the performance of a robotic system in simulation. The directed search method is applied based multiple performance evaluation rules. A performance predictor is trained to probabilistically predict a pass or fail result for each rule at each point in the parameter space. An overall acquisition function is determined as follows: if a pass outcome is predicted at a given, the performance evaluation rule having the highest probability of an incorrect outcome prediction at determines the acquisition function; whereas, if a fail outcome is predicted at a given point for at least one rule, then the acquisition function is determined by the performance evaluation rule for which a fail outcome is predicted with the lowest probability of an incorrect outcome prediction.

Abstract: A computer-implemented method of perceiving structure in an environment comprises steps of: receiving at least one structure observation input pertaining to the environment; processing the at least one structure observation input in a perception pipeline to compute a perception output; determining one or more uncertainty source inputs pertaining to the structure observation input; and determining for the perception output an associated uncertainty estimate by applying, to the one or more uncertainty source inputs, an uncertainty estimation function learned from statistical analysis of historical perception outputs.

Abstract: A computer-implemented method is provided for generating a trajectory for a first agent of a plurality of agents navigating a mapped area, the method comprising: receiving an observed state of each of the plurality of agents, and map data of the mapped area; generating an initial estimated trajectory for each of the plurality of agents based on the observed state of each agent and the map data; performing a first collision assessment to determine a likelihood of collision between the first agent and each other agent, based on the initial estimated trajectory for the first agent and the initial estimated trajectory for each other agent; and generating a second estimated trajectory for the first agent based on the observed states of each of the plurality of agents, the map data, and the results of the first collision assessment.

Abstract: A method of generating at least one trajectory in scenario comprising an agent navigating a mapped area, the method comprising: receiving an observed state of the agent and map data of the mapped area; generating a set of multiple trajectory basis elements from the observed state of the agent based on the map data; processing one or more scenario inputs in a neural network to generate a set of weights, each weight corresponding to one of the trajectory basis elements; and generating a trajectory for the agent by weighting each trajectory basis element by its corresponding weights and combining the weighted trajectory basis elements.

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 PSPM is configured to receive a computed perception ground truth, and 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. A simulated scenario is run based on a time series of such perception outputs (with modelled perception errors), but can also be re-run based on perception ground truths directly (without perception errors). This can, for example, be way to ascertain whether perception error was the cause of some unexpected decision within the planner, by determining whether such a decision is also taken in the simulated scenario when perception error is “switched off”.

Abstract: A computer-implemented method of generating black-box adversarial inputs to a perception component using a surrogate model of the perception component comprises receiving an initial input to the perception component and repeatedly perturbing the initial input until an adversarial input is found that satisfies an attack objective by: performing a primary attack process by perturbing the initial input based on a computed gradient of a surrogate attack loss function of the surrogate model that encodes the attack objective; wherein, if the primary attack process terminates without finding any perturbed input satisfying the promising attack condition, a backup attack process is performed to perform a randomized search of the input space of the perception component, guided by the surrogate model, until a perturbed input satisfying the promising attack condition is found; wherein the primary attack process is repeated based on the perturbed input found by the primary attack process or backup process.

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 computer-implemented method for assessing autonomous vehicle performance comprising receiving, at an input, performance data of at least one autonomous driving run, the performance data comprising at least one time series of perception errors and at least one time series of driving performance results; and generating, at a rendering component, rendering data for rendering a graphical user interface, the graphical user interface for visualizing the performance data and comprising: a perception error timeline, and a driving assessment timeline, wherein the timelines are aligned in time, and divided into multiple time steps of the at least one driving run, wherein, for each time step: the perception timeline comprises a visual indication of whether a perception error occurred at that time step, and the driving assessment timeline comprises a visual indication of driving performance at that time step.

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 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. The PSPM comprises a time-dependent model such that the perception output sampled at the current time instant depends on at least one of: an earlier one of the perception outputs sampled at a previous time instant, and an earlier one of the perception ground truths computed for a previous time instant.

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 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. The modelled perception slice includes an online error estimator, and the computer system is configured to use the PSPM to obtain a predicted online error estimate for the perception output in response to the perception ground truth. This recognizes that online perception error estimates may, themselves, be subject to error.

Abstract: One aspect herein pertains to a computer-implemented method of predicting agent motion comprises receiving a first observed agent state corresponding to a first time instant; determining a set of agent goals; for each agent goal, planning an agent trajectory based on the agent goal and the first observed agent state; receiving a second observed agent state corresponding to a second time instant later than the first time instant; for each goal, comparing the second observed agent state with the at least one agent trajectory planned for the goal, and thereby computing a likelihood of the goal and/or the planned agent trajectory for the goal. Another aspect pertains to trajectory generation, e.g., within a motion planner.

Abstract: A computer-implemented method of planning a path for a mobile robot in the presence of a set of obstacles comprises: computing for each obstacle a probabilistic obstacle position distribution; computing at least one path-independent function as a combination of the probabilistic obstacle position distributions; and for at least one candidate path, determining an upper bound on the total probability of obstacle collision along that path, by aggregating the path-independent function based on an area defined by the candidate path and a mobile robot shape, wherein the path-independent function is independent of the candidate path.

Abstract: The problem of domain shift error in computer vision models and other perception components is addressed. In a label approximation phase, an approximate label distribution is computed for each input of a target batch using a trained machine learning (ML) perception component. In an online label optimization phase, a modified label distribution is assigned to each input of the target batch, via optimization of an unsupervised loss function that (i) penalizes divergence between the approximate label distribution and the modified label distribution for each input of the target batch (ii) penalizes deviation between the modified label distributions assigned to input pairs of the target batch having similar features.

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.

Abstract: For generating driving scenarios for testing an autonomous vehicle planner in a simulation environment, a scenario model comprises a scenario variable and a distribution associated with the scenario variable. The scenario variable is a road layout variable. Multiple sampled values of the scenario variable are computed based on the distribution associated the scenario variable. Based on the scenario model, multiple driving scenarios are generated for testing an autonomous vehicle planner in a simulation environment, each driving scenario comprising a road layout generated using a sampled value of said multiple sampled values of the scenario variable.

Abstract: A method of annotating known objects in road images captured from a sensor-equipped vehicle, the method implemented in an annotation system and comprising: receiving at the annotation system a road image containing a view of a known object; receiving ego localization data, as computed in a map frame of reference, via localization applied to sensor data captured by the sensor-equipped vehicle, the ego localization data indicating an image capture pose of the road image in the map frame of reference; determining, from a predetermined road map, an object location of the known object in the map frame of reference, the predetermined road map representing a road layout the map frame of reference, wherein the known object is one of: a piece of road structure, and an object on or adjacent a road; computing, in an image plane defined by the image capture pose, an object projection, by projecting an object model of the known object from the object location into the image plane; and storing, in an image database, image

Abstract: A computer system comprising: computer storage configured to store a static road layout; a topological indexing component configured to generate an in-memory topological index of the static road layout, the topological index in the form of a graph of nodes and edges, wherein each node corresponds to a road structure element of the static road layout, and the edges encode topological relationships between the road structure elements; a geometric indexing component configured to generate at least one in-memory geometric index of the static road layout for mapping geometric constraints to road structure elements of the static road layout; and a scenario query engine configured to receive a geometric query, search the geometric index to locate at least one static road element satisfying one or more geometric constraints of the geometric query, and return a descriptor of the at least one road structure element(s), wherein the scenario query engine is configured to receive a topological query comprising a descripto

Abstract: A computer implemented method of generating a scenario to be run in a simulation environment for testing the behaviour of an autonomous vehicle includes rendering, on a display of a computer device, an image of a static scene topology; and rendering on the display an object editing node comprising a set of input fields for receiving user input. The object editing node is for parameterizing an interaction of a challenger object relative to an ego object; and the method includes receiving into the input fields of the object editing node user input defining at least one temporal or relational constraint of the challenger object relative to the ego object. The at least one temporal or relational constraints define an interaction point of a defined interaction stage between the ego object and the challenger object.

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: A computer-implemented method of evaluating the performance of a trajectory planner for a mobile robot in a scenario, in which the trajectory planner is used to control the mobile robot responsive to at least one other agent of the scenario the method comprising: determining a scenario parameter set for the scenario and a likelihood of the scenario parameter set; computing an impact score for a failure event or near failure event between the mobile robot and the other agent occurring in the scenario instance, the impact score quantifying severity of the failure event or near failure event; and computing a risk score for the instance of the scenario based on the impact score and the likelihood of the scenario parameter set.