Abstract: A system for remote control of an occupied vehicle includes one or more external vehicle environmental sensors. The system further includes a suspect event identification module including instructions stored in at least one memory and executable by one or more processors to cause the passenger identification module to determine the occurrence of a suspect event at the occupied vehicle based, at least in part, on data indicative of an audio environment of a surrounding of the occupied vehicle. The system also includes a communication module including instructions stored in at least one memory and executable by one or more processors to cause the communication module to communicate an initial signal to a remote device of an operator of the occupied vehicle outside of the occupied vehicle in response to the determination of the suspect event.

Abstract: A system for assisting a distracted vehicle occupant. The system includes a first occupant sensor configured to capture data indicative of an emotional state of an occupant of a vehicle. The system also includes a distraction determination module configured to recognize that the occupant is in a high emotional state based on the captured data indicative of the emotional state of the occupant. The distraction determination module is also configured to determine one or more corrective actions configured to calm the occupant from the high emotional state to a normal emotional state in response to the recognized high emotional state of the occupant. The distraction determination module is further configured to implement or cause to be implemented the corrective action(s).

Abstract: Method and system for validating an automated control software, ACS, for a vehicle, wherein the ACS is adapted for operating a vehicle in autonomous driving mode. The method comprises simulating use of the ACS in agent movement scenarios, wherein said simulating includes providing input sensor data to each vehicle agent to which an ACS has been assigned, and wherein movement of each of the other agents in the simulation is controlled to move either according to a replay of a corresponding real-world agent, or by an agent movement model.

Abstract: A method and system for predicting a near future path for a vehicle. For predicting the near future path sensor data and vehicle driving data is collected. Road data is collected indicative of a roadway on the presently occupied road for the vehicle. The sensor data and the vehicle driving data is pre-processed to provide object data comprising a time series of previous positions, headings, and velocities of each of the objects relative the vehicle. The object data, the vehicle driving data, and the road data is processed in a deep neural network to predict the near future path for the vehicle. The invention also relates to a vehicle comprising the system.

Abstract: A method for predicting a trajectory of at least one secondary road user for avoiding a collision course with the secondary road user for a host vehicle. The method includes determining the present location for the host vehicle, retrieving a plurality of modelled clusters of trajectories for a present traffic situation, and detecting the position and speed of the at least one secondary road user. The method also includes predicting at least one feasible trajectory for the at least one secondary road user based on the position and the speed of the at least one secondary road user to the plurality of modelled clusters of trajectories and selecting at least one feasible trajectory of the feasible trajectories for each secondary road user based on a selection criterion. At least one action is performed based on the selected at least one feasible trajectory.

Abstract: Method and system for validating an automated control software, ACS, for a vehicle, wherein the ACS is adapted for operating a vehicle in autonomous driving mode. The method comprises simulating use of the ACS in agent movement scenarios, wherein said simulating includes providing input sensor data to each vehicle agent to which an ACS has been assigned, and wherein movement of each of the other agents in the simulation is controlled to move either according to a replay of a corresponding real-world agent, or by an agent movement model.

Abstract: A method for predicting a trajectory of at least one secondary road user for avoiding a collision course with the secondary road user for a host vehicle. Determining the present location for the host vehicle. Retrieving a plurality of modelled clusters of trajectories for a present traffic situation. Detecting the position and speed of the at least one secondary road user. Predicting at least one feasible trajectory for the at least one secondary road user based on the position and the speed of the at least one secondary road user to the plurality of modelled clusters of trajectories. Selecting at least one feasible trajectory of the feasible trajectories for each secondary road user based on a selection criterion. Performing at least one action based on the selected at least one feasible trajectory.

Abstract: A method and system for predicting a near future path for a vehicle. For predicting the near future path sensor data and vehicle driving data is collected. Road data is collected indicative of a roadway on the presently occupied road for the vehicle. The sensor data and the vehicle driving data is pre-processed to provide object data comprising a time series of previous positions, headings, and velocities of each of the objects relative the vehicle. The object data, the vehicle driving data, and the road data is processed in a deep neural network to predict the near future path for the vehicle. The invention also relates to a vehicle comprising the system.