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: 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: 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 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 on at least one performance evaluation rule that returns a performance score that can be numerical or non-numerical. A hierarchical score prediction model is constructed as follows. A score classification model is trained to probabilistically predict whether a point in the parameter space will result in a numerical or non-numerical outcome. A score regression model is trained to probabilistically predict a performance score for a given point, given that the score is numerical. The score classification and regression models are used to guide a directed search of the parameter space towards the most salient instances of the scenario.

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: 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: 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: Performance of a substitute upstream processing component is tested, in order to determine whether that performance is sufficient to support a downstream processing component, within an autonomous driving system, in place of an existing upstream processing component. The existing upstream processing component and the substitute upstream processing component are mutually interchangeable in so far as they provide the same form of outputs interpretable by the downstream processing component, such that either upstream processing component may be used without modification to the downstream processing component. A direct or indirect metric-based comparison is formulated in terms of the resulting performance of the downstream processing component.

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: 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 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: A computer-implemented method of training a depth uncertainty estimator comprises receiving, at a training computer system, a set of training examples, each training example comprising (i) a stereo image pair and (ii) an estimated disparity map computed from at least one image of the stereo image pair by a depth estimator. The training computer system executes a training process to learn one or more uncertainty estimation parameters of a perturbation function, the uncertainty estimation parameters for estimating uncertainty in disparity maps computed by the depth estimator. The training process is performed by sampling a likelihood function based on the training examples and the perturbation function, thereby obtaining a set of sampled values for learning the one or more uncertainty estimation parameters.

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 of training a depth uncertainty estimator comprises receiving, at a training computer system, a set of training examples, each training example comprising (i) a stereo image pair and (ii) an estimated disparity map computed from at least one image of the stereo image pair by a depth estimator. The training computer system executes a training process to learn one or more uncertainty estimation parameters of a perturbation function, the uncertainty estimation parameters for estimating uncertainty in disparity maps computed by the depth estimator. The training process is performed by sampling a likelihood function based on the training examples and the perturbation function, thereby obtaining a set of sampled values for learning the one or more uncertainty estimation parameters.