Abstract: Methods and apparatus, including computer program products, implementing, and using techniques for determining a spin axis of a ball in flight. At least a portion of an observed trajectory of the ball is obtained. The trajectory includes three-dimensional locations of the ball over time, as determined from individual observations of the ball by one or more sensors. A point in three-dimensional space is selected. An optimization problem is solved, wherein the three-dimensional acceleration at the selected point is used as an optimization target, and a spin axis, a spin magnitude and a wind vector are used as optimization parameters, and wherein the solution to the optimization problem is a combination of optimization parameter values. The spin axis determined in the solution to the optimization problem is selected as the spin axis for the ball. The spin axis is used to generate ball trajectory information for presentation on an output device.

Abstract: A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

Abstract: A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.