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 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 method of evaluating the performance of a target planner for an ego robot in a scenario. Evaluation data is generated by applying the target planner from an initial scenario state to generate an actual ego trajectory taken by the ego robot. The actual ego trajectory is defined by a target trajectory parameter. Comparison data is generated by applying a comparison planner from the same initial scenario state to generate a comparison ego trajectory for a comparison ego robot, the comparison ego trajectory comprising at least one comparison trajectory parameter. A juncture point at which the comparison trajectory parameter differs from the actual trajectory parameter is determined and a difference between the actual trajectory parameter and the comparison trajectory parameter at the juncture point is determined. A comparison between the determined difference and a threshold value indicates whether the juncture point is significant.

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 evaluating planner performance for an ego robot in a scenario, the method comprising: receiving for each of a set of runs, run evaluation data, wherein the run evaluation data for each run is generated by applying a planner in a scenario of that run to generate an ego trajectory taken by the ego robot in the scenario; determining for the set of runs, an examination category; generating for each run, an indicator of an examination parameter for that run in the examination category; the indicator selected from a group of different indicators in the examination category; and identifying a cluster of runs of the set of runs sharing the same indicator in the examination category.

Abstract: A computer implemented method of evaluating the performance of a target planner for an ego robot in a scenario, the method comprising: rendering on a display of a graphical user interface of a computer device, a dynamic visualisation of an ego robot moving along a first path in accordance with a first planned trajectory from the target planner and of a comparison ego robot moving along a second path in accordance with a second planned trajectory from a comparison planner; detecting a juncture point at which the first and second trajectories diverge; rendering the ego robot and the comparison ego robot as a single visual object in motion along a common path shared by the first and second paths prior to the juncture point; and rendering the ego robot and the comparison ego robot as separate visual objects on the display along the respective first and second paths from the juncture point.

Abstract: A computer implemented method of evaluating planner performance for an ego robot, the method comprising: receiving first run data of a first run, the run data generated by applying a planner in a scenario of that run to generate an ego trajectory taken by the ego robot in the scenario; extracting scenario data from the first run data to generate scenario data defining the scenario; providing the scenario data to a simulator configured to execute a simulation using the scenario data and implementing a second planner to generate second run data; comparing the first run data and the second run data to determine a difference in at least one performance parameter; and generating a performance indicator associated with the run, the performance indicator indicating a level of the determined difference between the first run data and the second run data.

Abstract: A computer implemented method of evaluating the performance of at least one component of a planning stack for an autonomous robot, the method comprising: generating first evaluation data of a first run by operating the autonomous robot under the control of a planning stack under test in a scenario; modifying at least one operating parameter of at least one component of the planning stack by applying a variable modification to the operating parameter; generating second evaluation data of a second run by operating the autonomous robot under the control of the planning stack in which the at least one operating parameter has been modified, in the scenario; and comparing the first evaluation data with the second evaluation data using at least one performance metric for the comparison.

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 determining a series of control signals for controlling an autonomous vehicle to implement a planned speed change maneuver comprises: receiving from a maneuver planner a position target for the planned speed change maneuver; selecting, from a predetermined family of kinematic functions, a kinematic function for carrying out the planned speed change maneuver, each kinematic function being a first or higher order derivative of acceleration with respect to time; and using the selected kinematic function to determine a series of control signals for implementing the planned speed change maneuver; wherein the kinematic function is selected in a constrained optimization process as substantially optimizing a cost function defined for the speed change maneuver, subject to a set of hard constraints that: (i) require a final acceleration, speed and position corresponding to the selected kinematic function to satisfy, respectively, an acceleration target, a speed target and the position t