Abstract: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

Abstract: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

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: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

Abstract: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

Abstract: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

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: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

Abstract: An advanced map matching algorithm is disclosed, which embeds the road topology inherently in a set of path candidates. Road connectivity is maintained even in special situations such as U-turns, reverse driving, and tunnels. Careful path candidate management is designed to reduce the candidate count while preserving well-matching candidates. Paths are treated and evaluated independently using multiple criteria, which makes the algorithm reliable and robust in different scenarios.

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.