Abstract: A computer system for testing the performance of a stack for planning ego vehicle trajectories in real or simulated driving scenarios, the computer system comprising: at least a first input configured to receive (i) scenario ground truth and (ii) internal state data of the stack, the scenario ground truth and internal state data generated using the stack to control an ego agent responsive to at least one other agent in the simulated driving scenario; at least a second input configured to receive a defined operational design domain (ODD); a test oracle configured to apply one or more driving rules to the scenario ground truth for evaluating the performance of the stack in the scenario, and provide an output for each of the driving rules indicating whether that driving rule has been complied with; wherein the one or more driving rules include at least one ODD-based response rule, the test oracle configured to apply the ODD-based response rule by: processing the scenario ground truth over multiple time steps, to

Abstract: A computer-implemented method of evaluating the performance of a trajectory planner for a mobile robot in a real or simulated scenario, comprises receiving scenario ground truth of the scenario, the scenario ground truth generated using the trajectory planner to control an ego agent of the scenario responsive to at least one scenario element of the scenario. One or more performance evaluation rules for the scenario and at least one activation condition for each performance evaluation rule are received. A test oracle processes the scenario ground truth to determine whether the activation condition of each performance evaluation rule is satisfied over multiple time steps of the scenario. Each performance evaluation rule is evaluated by the test oracle, to provide at least one test result, only when its activation condition is satisfied.

Abstract: A computer system receives scenario data generated using a trajectory planner to control an ego agent responsive to at least one other agent in a real or simulated scenario. A test oracle provides predetermined extractor functions for extracting time-varying numerical signals from the scenario data and predetermined assessor functions for assessing the extracted time-varying signals. The test oracle applies, to the scenario data, a rule graph comprising extractor nodes and assessor nodes. Each extractor node applies one of the predetermined extractor functions to the scenario data to extract an output in the form of a time-varying numerical signal. Each assessor node has one or more child nodes, each child node being one of the extractor nodes or another of the assessor nodes, and the assessor node applies one of the predetermined assessor functions to the output(s) of its child node(s).

Abstract: A computer system for analysing driving scenes in relation to an autonomous vehicle (AV) operational design domain (ODD), the computer system comprising: an input configured to receive a definition of the ODD in a formal ontology language; a scene processor configured to receive data of a driving scene and extract a scene representation therefrom, the data comprising an ego trace, at least one agent trace, and environmental data about an environment in which the traces were captured or generated, wherein the scene representation is an ontological representation of both static and dynamic elements of the driving scene extracted from the traces and the environmental data, and expressed in the same formal ontology language as the ODD; and a scene analyzer configured to match the static and dynamic elements of the scene representation with corresponding elements of the ODD, and thereby determine whether or not the driving scene is within the defined ODD.

Abstract: A computer-implemented method of evaluating the performance of a full or partial autonomous vehicle (AV) stack in simulation, the method comprising: applying an optimization algorithm to a numerical performance function defined over a scenario space, wherein the numerical performance function quantifies the extent of success or failure of the AV stack as a numerical score, and the optimization algorithm searches the scenario space for a driving scenario in which the extent of failure of the AV stack is substantially maximized, wherein the optimization algorithm evaluates multiple driving scenarios in the search space over multiple iterations, by running a simulation of each driving scenario in a simulator, in order to provide perception inputs to the AV stack, and thereby generate at least one simulated agent trace and a simulated ego trace reflecting autonomous decisions taken in the AV stack in response to the simulated perception inputs, wherein later iterations of the multiple iterations are guided by the