Abstract: A processing apparatus for generating route navigation data is provided, to, generate training data based on road network data corresponding to a network of roads in a defined geographical area, and journey data sets, each journey data set comprising data indicative of a journey by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, train a classifier model based on the training data, apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road, and generate the route navigation data indicative of the predicted direction of the traffic flow on the road. A method for generating route navigation data is also provided.

Abstract: A computer implemented method of annotating map data, in particular for navigation of vehicles, the method including: receiving the map data including a graph of a road network including a plurality of vertices and edges; pre-processing the graph to produce a planar graph including a plurality of polygons; grouping the plurality of polygons to form a plurality of geofences, each of the plurality of geofences including a number of POIs which may be within a pre-determined POI range, and each of the plurality of geofences having a road traversal length that may be within a pre-determined length range; storing geofence data of the plurality of geofences in a memory of a computer; and annotating each of the plurality of geofences.

Abstract: Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

Abstract: A system configured for managing orders is disclosed. The system may include one or more processor(s) which may collect first 2D training image data; collect second 2D training image data; construct a 3D map for the geographical area based on the first training image data and the second training image data; determine a likelihood of a potential missing feature in the 3D map based on the first 2D training image data and the second 2D training image data; collect third 2D training image data comprising third map images of the geographical area acquired by the one or more image acquisition apparatus if the likelihood of the potential missing feature is above a predetermined threshold; and generate the road map based on the first 2D training image data, the second 2D training image data and the third 2D training image data.

Abstract: Aspects concern a method for generating map data, the method including: generating of a plurality of cells of a real space area, each cell representing a two-dimensional sub-space of the real space area, wherein each of the cells comprise at least one global positioning system (GPS) point, and wherein the real space area comprises a road network; determining a number of GPS points for each cell of the plurality of cells; determining a road network based on the numbers of GPS points using a Smoothed Particle Hydrodynamics (SPH) analysis, wherein the cells of the plurality of cells are particles of the SPH analysis and the number of GPS points per cell of the plurality of cells are a scalar of each cell of the plurality of cells in the SPH analysis; and comparing the road network determined using the SPH analysis with the road network of the real space area.

Abstract: Aspects concern a method for determining a navigation profile for a vehicle in a geographical area, comprising obtaining a road network, initializing a navigation map by assigning an initial speed to each road segment of the road network, training the navigation map using training data comprising a training route for each of a multiplicity of trips by, if the number of trips for which a road segment is in the training route but not in the determined route is above the first threshold, assigning a higher speed to the road segment, and, if the number of trips for which a road segment is in the determined route but not in the training route is above the second threshold, assigning a lower speed to the road segment, and determining a navigation profile including a route for a vehicle in the geographical area using the trained navigation map.

Abstract: An off-axis system for LCD projectors comprises an LED light source, a condenser cup, a rear Fresnel lens, a heat insulation lens, an LCD, a front Fresnel lens, and a projection lens group, provided in sequence from light source to projection screen. The optical axis of the projection lens is inclined upward by 9.5 degrees, the front Fresnel lens is inclined by 3 to 6 degrees, and the LCD projector's base plane is inclined by 9.5 degrees. The invention solves image separation and optical loss by applying to all single-panel LCD projectors. The viewing wall and front lens form a 70-to-75.5-degree angle for keystone correction, and the LED light, condenser cup, rear Fresnel lens, front Fresnel lens and LCD form a 7-to-10-degree angle with the horizontal plane, and the reflection mirror and projection lens group form a 90-degree angle, achieving concentric alignment of optical axis to solve optical loss.

Abstract: Aspects concern a method for generating map data, the method including: generating of a plurality of cells of a real space area, each cell representing a two-dimensional sub-space of the real space area, wherein each of the cells comprise at least one global positioning system (GPS) point, and wherein the real space area comprises a road network; determining a number of GPS points for each cell of the plurality of cells; determining a road network based on the numbers of GPS points using a Smoothed Particle Hydrodynamics (SPH) analysis, wherein the cells of the plurality of cells are particles of the SPH analysis and the number of GPS points per cell of the plurality of cells are a scalar of each cell of the plurality of cells in the SPH analysis; and comparing the road network determined using the SPH analysis with the road network of the real space area.

Abstract: The present disclosure provides methods, devices and systems for route deviation quantification and vehicular route learning based thereon. In some examples, there is provided a method for route deviation quantification of a suggested route. The method comprises: obtaining a ground truth route based on a filtered trajectory, the filtered trajectory including an inferred location of origin and an inferred location of destination; obtaining a suggested route generated based on the inferred location of origin and the inferred location of destination; quantifying a deviation of the suggested route from the ground truth route by calculating an off course ratio based on a combined length of road segments in the suggested route that are matched to corresponding road segments in the ground truth route and a combined length of road segments in the ground truth route.

Abstract: A processing apparatus for generating route navigation data is provided, to, generate training data based on road network data corresponding to a network of roads in a defined geographical area, and journey data sets, each journey data set comprising data indicative of a journey by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, train a classifier model based on the training data, apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road, and generate the route navigation data indicative of the predicted direction of the traffic flow on the road. A method for generating route navigation data is also provided.

Abstract: A processing apparatus for generating route navigation data is provided, to, generate training data based on road network data corresponding to a network of roads in a defined geographical area, and journey data sets, each journey data set comprising data indicative of a journey by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, train a classifier model based on the training data, apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road, and generate the route navigation data indicative of the predicted direction of the traffic flow on the road. A method for generating route navigation data is also provided.

Abstract: Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

Abstract: Aspects concern a method for generating map data, the method including: generating of a plurality of cells of a real space area, each cell representing a two-dimensional sub-space of the real space area, wherein each of the cells comprise at least one global positioning system (GPS) point, and wherein the real space area comprises a road network; determining a number of GPS points for each cell of the plurality of cells; determining a road network based on the numbers of GPS points using a Smoothed Particle Hydrodynamics (SPH) analysis, wherein the cells of the plurality of cells are particles of the SPH analysis and the number of GPS points per cell of the plurality of cells are a scalar of each cell of the plurality of cells in the SPH analysis; and comparing the road network determined using the SPH analysis with the road network of the real space area.

Abstract: A processing apparatus is provided, to, for each candidate point-of-interest of at least one candidate point-of-interest identified for association with a road in a network of roads for determining a name for the road, generate first data indicative of a relationship relating to a distance between the candidate point-of-interest and a road segment of the road, generate second data indicative of a relationship between the road segment and a projection of the candidate point-of-interest in a direction of the road segment, and, if the first and second relationships satisfy a first and second conditions for association respectively, generate data indicative of the candidate point-of-interest being an associated point-of-interest, process data corresponding to the associated point-of-interest to extract name data indicative of a road name associated with the associated point-of-interest, and generate, based on the name data, data indicative of the name for the road.

Abstract: A processing apparatus for traffic management of a network of roads is provided, to, generate, based on journey data sets, first count data indicative of a first count of the road users travelling on an incoming road leading to the intersection node, and second count data indicative of a second count of the road users travelling on an outgoing road of at least two outgoing roads, each outgoing road leading away from the intersection node, generate result data indicative of a result that is determined based on the first count and the second count, if the result satisfies a condition for restriction, generate restriction data indicative of a restriction of traffic from the incoming road to the outgoing road via the intersection node, and generate, based on an angular relationship between the outgoing road and the incoming road, type data indicative of a type of the restriction.

Abstract: Aspects concern a method for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations, generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned, identifying the largest strongly connected component of the routing graph, identifying one or more further strongly connected components of the routing graph, detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph and amending the map data by the identified travel possibilities.

Abstract: Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model, HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

Abstract: The present disclosure provides methods, devices and systems for route deviation quantification and vehicular route learning based thereon. In some examples, there is provided a method for route deviation quantification of a suggested route. The method comprises: obtaining a ground truth route based on a filtered trajectory, the filtered trajectory including an inferred location of origin and an inferred location of destination; obtaining a suggested route generated based on the inferred location of origin and the inferred location of destination; quantifying a deviation of the suggested route from the ground truth route by calculating an off course ratio based on a combined length of road segments in the suggested route that are matched to corresponding road segments in the ground truth route and a combined length of road segments in the ground truth route.

Abstract: Aspects concern a method for determining a navigation profile for a vehicle in a geographical area, comprising obtaining a road network, initializing a navigation map by assigning an initial speed to each road segment of the road network, training the navigation map using training data comprising a training route for each of a multiplicity of trips by, if the number of trips for which a road segment is in the training route but not in the determined route is above the first threshold, assigning a higher speed to the road segment, and, if the number of trips for which a road segment is in the determined route but not in the training route is above the second threshold, assigning a lower speed to the road segment, and determining a navigation profile including a route for a vehicle in the geographical area using the trained navigation map.

Abstract: A processing apparatus for generating route navigation data is provided, to, generate training data based on road network data corresponding to a network of roads in a defined geographical area, and journey data sets, each journey data set comprising data indicative of a journey by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, train a classifier model based on the training data, apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road, and generate the route navigation data indicative of the predicted direction of the traffic flow on the road. A method for generating route navigation data is also provided.