Zenuity Ab

Tommy Tram

Ivo Batkovic

Anton Jansson

Robin Grönberg

Robin GRÖNBERG

A control device that generates manoeuvring decisions for an ego-vehicle in a traffic scenario is provided. The control device includes a first module including a trained self-learning model, the first module being configured to receive data including information about a surrounding environment of the ego-vehicle determine, using the trained self-learning model, an action to be executed by the ego-vehicle based on the received data. The control device includes a second module configured to receive the determined action, receive data including information about the surrounding environment of the ego-vehicle during a finite time horizon, predict an environmental state for a first time period of the finite time horizon, determine a trajectory for the ego-vehicle based on the received action for the finite time horizon and on the predicted environmental state for the first time period, send a signal in order to control the ego-vehicle according to the determined trajectory.

Autonomous decisions in traffic situations with planning control

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.

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Gothenburg, Sweden

Tommy Tram