Five AI Limited

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.

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.

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.

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.

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 data of the road image, in association with annotation data of the object projection for annotating the image data with a location of the known object in the image plane.

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. The method includes storing the set of constraints and defined interaction stage in an interaction container in a computer memory of the computer system; and generating a scenario to be run in a simulation environment, the scenario comprising the defined interaction stage executed on the static scene topology at the interaction point.

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 descriptor of at least one road element, search the topological index to locate the corresponding node(s), identify at least one other node satisfying the topological query based on the topological relationships encoded in the edges of the topological index, and return a descriptor of the other node(s) satisfying the topological query.

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.

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.

Depth information is extracted from a stereoscopic image pait by an image processing system. For each pixel of a target image of the stereoscopic image pair, a final disparity cost vector is computed having cost components corresponding to different disparities. The final disparity cost vector is stored in association with that pixel. That pixel is assigned the disparity corresponding to the lowest cost component of the final disparity cost vector, wherein the extracted depth information comprises the disparities assigned to the pixels of the target image. For at least a subset of the pixels of the target image, the final disparity cost vector is computed for each of those pixels by computing, with respect to the reference image, a set of matching costs for that pixel and the different disparities, and combining the matching costs with the one or more final disparity cost vectors stored in association with one or more of the adjacent pixels of the target image.