Abstract: A method of generating a horizon for use by an ADAS of a vehicle involves using digital location-based data, driver data and/or vehicle data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability may be based on one or more of: an angle of the path relative to the incoming path, the road class of the path, a speed profile of the path, historical paths taken by vehicles at the decision point, and historical paths taken at the decision point by the individual driver or vehicle.

Abstract: A method of generating a horizon for use by an ADAS of a vehicle involves using digital location-based data, driver data and/or vehicle data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability may be based on one or more of: an angle of the path relative to the incoming path, the road class of the path, a speed profile of the path, historical paths taken by vehicles at the decision point, and historical paths taken at the decision point by the individual driver or vehicle.

Abstract: A method of generating a horizon for use by an ADAS of a vehicle involves using digital location-based data, driver data and/or vehicle data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability may be based on one or more of: an angle of the path relative to the incoming path, the road class of the path, a speed profile of the path, historical paths taken by vehicles at the decision point, and historical paths taken at the decision point by the individual driver or vehicle.

Abstract: A method of generating a horizon for use by an ADAS of a vehicle involves using digital location-based data, driver data and/or vehicle data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability may be based on one or more of: an angle of the path relative to the incoming path, the road class of the path, a speed profile of the path, historical paths taken by vehicles at the decision point, and historical paths taken at the decision point by the individual driver or vehicle.

Abstract: Real time navigation assistance is provided on multi-level roadways using on-the-fly elevation determinations obtained by measuring the ambient air pressure. Absolute air pressure measurements adjusted to sea level (QNH) are obtained by on-the-fly wireless searches. A navigation device (10) calculates its current elevation above sea level as a function of the measured ambient actual pressure and a selected local QNH reading. This elevation is correlated to a digital map so that the navigation device (10) can be accurately located relative to a multi-level roadway. Elevation readings along any road segment (14) can be transmitted to a map update center for the purpose of updating and improving digital map data.