Abstract: Vehicles and related systems and methods are provided for controlling a vehicle in an autonomous operating mode. One method involves identifying an object in a potential incursion zone corresponding to a lane adjacent to a current lane of travel for the vehicle, determining an estimated time in zone associated with the object, and in response to determining the estimated time in zone is greater than a threshold, determining a longitudinal adjustment strategy to reduce the estimated time in zone associated with the object, determining an adjusted speed for the vehicle in accordance with the longitudinal adjustment strategy, determining a longitudinal trajectory for the vehicle within the current lane of travel based at least in part on the adjusted speed, and autonomously operating one or more actuators onboard the vehicle in accordance with the longitudinal trajectory.

Abstract: Method, system and computer program product for providing a predicted vehicle track and for providing alerts when two predicted vehicle tracks are closer than a threshold amount. A vehicle intent prediction model is generated based on past instance of tracks for a vehicle operation, known vehicle intent data for the past instances, and contextual factors, such as weather, airline operator, air vehicle type or configuration, day of the week, etc. for the past instances. The vehicle intent prediction model can be generated using one or more machine learning algorithms. A future vehicle trajectory for a current vehicle operation can be output by the vehicle intent prediction model using the current track and existing contextual factors for the current vehicle operation. In the event that two vehicles following their respective predicted vehicle future trajectories would be closer than a threshold distance, an alert can be provided.

Abstract: Method, system and computer program product for providing a predicted vehicle track and for providing alerts when two predicted vehicle tracks are closer than a threshold amount. A vehicle intent prediction model is generated based on past instance of tracks for a vehicle operation, known vehicle intent data for the past instances, and contextual factors, such as weather, airline operator, air vehicle type or configuration, day of the week, etc. for the past instances. The vehicle intent prediction model can be generated using one or more machine learning algorithms. A future vehicle trajectory for a current vehicle operation can be output by the vehicle intent prediction model using the current track and existing contextual factors for the current vehicle operation. In the event that two vehicles following their respective predicted vehicle future trajectories would be closer than a threshold distance, an alert can be provided.