Here Global B.v.

Nokia Technologies Oy

Nokia Corporation

Here Glboal B.v.

Jerome Beaurepaire

Leon Stenneth

Jeremy Michael Young

Marko Tapio Tuukkanen

Sven Krome

Jens Unger

Philippe Beaurepaire

Ari Aarnio

Amarnath Nayak

Maguy Jamain

Lauri Aarne Johannes Wirola

Olivier Dousse

Dmitry Koval

Francesco Grani

Aaron Rincover

Jarmo Kemppainen

Vasileios Bosdelekidis

Wiebke Hurrelmann-Wieding

Gianpietro Battistutti

Richa Menke

Nicolas Neubauer

Basel Hashisho

Joachim Meister

Jens Von Lindequist

Eckhart Koppen

Mirko Konig

Daniel Kraus

Johannes Lauer

Andre Kuhn

Juan Lablanca

David Falla-Cepeda

Stephan Kosmella

Andreas Graf

Andre Fialho

Virva Emila Auvinen

Eckhart Köppen

Tiitus Hiltunen

Michele Velastri

Huib Kalb

Sjoerd Spaargaren

Sigrid Van Veen

Kiranmayee Mhetre

Uwa Okonkwo

Jarmo Juhani Kemppainen

System and methods for determining traffic violation hotspots based on roadway feature and/or sensor data. Traffic violation hotspots include, for example areas where traffic violations are more likely to occur such as traffic light jumping, wrong way driving, over speeding, not wearing seatbelt, avoiding stop signs and red lights, distracted driving, passing other vehicles in a no-passing zone, among others. Embodiments further provide predictions for traffic accidents hotspots based on the presence or absence of the traffic violation hotspots.

Determining traffic violation hotspots

An approach for detecting LIDAR blind spots in parking garages. This approach may comprise receiving floor layout data of a parking garage and, based on the received floor layout data, determining at least one additional floor layout data for a floor of a parking garage. The determined floor layout data may include LIDAR blind spot data based on clusters of similarly laid out floors.

Systems and methods for detecting and mapping LIDAR blind spots in parking garages

A method, apparatus, and computer program product are therefore provided for emulating vehicle features of a first vehicle in a second vehicle having different features. Methods may include: receiving an indication of a user operating an unfamiliar vehicle; determining vehicle features familiar to the user; and providing emulation of one or more features of the vehicle features familiar to the user in the unfamiliar vehicle. According to some embodiments, the vehicle features familiar to the user are determined based on one or more vehicles familiar to the user. According to certain embodiments, vehicle features familiar to the user include one or more of vehicle size, vehicle performance, or vehicle autonomy level.

Method, apparatus and computer program product for vehicle feature emulation

Systems and methods for determining a route for mood improvement are provided. For example, a method of determining a route for mood improvement includes determining a mood of an individual. The method also includes receiving a destination as an input. The method also includes determining a route from a current location of the individual to the destination via a plurality of road segments for improving the mood of the individual.

Systems and methods for determining a route for mood improvement

A system, a method and a computer program product are provided to predict vehicle parking bunching in a geographic region. For example, the system is configured to obtain a plurality of contextual features and/or a plurality of sensor data related to parking information in the geographic region. The system is configured to predict a vehicle parking bunching based on a vehicle unparking threshold, a vehicle separation distance cluster threshold and/or unparking vehicle information. The system may also be configured to alert a vehicle of the vehicle parking bunching with a vehicle parking bunching notification.

System to predict vehicle parking bunching

An approach is provided for presenting a feedforward cue in a user interface before an upcoming vehicle event occurs. The approach involves retrieving map data covering an upcoming area in which a vehicle is to travel. The approach also involves extracting a feedforward cue from the map data, wherein the feedforward cue preemptively indicates an upcoming vehicle event that can have an effect on an experience that a user has while engaged in a non-driving activity in the vehicle. The approach further involves providing data for presenting the feedforward cue in a user interface before the upcoming vehicle event occurs.

Method and apparatus for presenting a feedforward cue in a user interface before an upcoming vehicle event occurs

A method, apparatus and computer program product are provided for quantifying public transport coverage. In this regard, for a traveler journey beginning at a starting location, transit time data is determined. The transit time data is indicative of a predicted amount of time to travel between the starting location and a departure location associated with a public transport boarding location for a public transport. Furthermore, waiting time data is determined. The waiting time is indicative of a predicted amount of time to wait at the departure location prior to departure via the public transport. Additionally, time to departure data is computed based on a combination of the transit time data and the waiting time data. For the traveler journey, a time to departure probability prediction is also determined.

Method, apparatus, and computer program product for quantifying public transport coverage

An apparatus, method and computer program product are provided for providing signature reduction for a vehicle. For example, the apparatus receives a destination for a vehicle as input, selects a subset from a plurality of road segments as a route from a location to the destination, and outputs the route or a portion thereof. The subset is selected based on association of each of the plurality of road segments with respect to a source, and the source is capable of acquiring vehicle signature information.

Apparatus and methods for providing vehicle signature reduction

An approach is disclosed for computing a spatial footprint index. The approach involves, for example, receiving, by one or more processors, information indicating that one or more physical items are associated with a user, a set of users, or a combination thereof. The approach also involves determining, by the one or more processors, spatial data indicating a physical space occupied by the one or more physicals items. The approach further involves computing, by the one or more processors, a spatial footprint index for the user, the set of users, or a combination thereof based on the spatial data, wherein the spatial footprint index is a quantified representation of the physical space occupied by the one or more physical items. The approach further involves providing, by the one or more processors, the spatial footprint index as an output.

Method, apparatus, and system for computing a spatial footprint index

The disclosure provides a system, a method, and a computer program product for verification of traffic incident. The system may be configured to receive a traffic incident trigger. Based on the traffic incident trigger, the system may select one or more vehicles for verification of the traffic incident detected at a location. The system may determine a route of travel for each vehicle of the selected one or more vehicles based on the location of the detected traffic incident. The system may reroute the selected one or more vehicles on the respective route of travel determined for each vehicle. The selected one or more vehicles are rerouted to record data of the traffic incident. The system may further verify the detected traffic incident based on the data of the traffic incident recorded by the rerouted one or more vehicles.

System and method for verification of traffic incidents

An apparatus, method and computer program product are provided for predicting tire temperature levels. In one example, the apparatus receives input data indicating attributes of the target vehicle and attributes of target routes for the target vehicle. The apparatus causes a machine learning model to generate output data as a function of the input data, where the output data indicate a subset of the target routes of which the target vehicle can successfully traverse while using the at least one spare tire. The machine learning model is trained to generate the output data as a function of the input data based on training data, where the training data indicate events in which vehicles used spare tires to traverse routes. The training data indicate attributes of the vehicles and attributes of the routes.

Apparatus and methods for providing routes for vehicles using spare tires

An approach is provided for location sharing using a LiDAR-based location signature. The approach involves, for example, receiving a Light Detection and Ranging (LiDAR) scan of a location captured by a LiDAR sensor of a portable device. The approach also involves processing the LiDAR scan to generate a LiDAR location signature that is representative of the location of the portable device. The approach further involves transmitting the LiDAR location signature to another device to share the location of the portable device with the another device.

Method, apparatus, and system for location sharing using a LiDAR-based location signature

An approach is provided for determining placement location(s) in a public space for a shared micro-mobility vehicle. The approach involves identifying a candidate placement location in the sidewalk area. The approach also involves determining factor(s) associated with the location and/or nearby location(s) in the sidewalk area. The approach further involves making a determination of whether the location is suitable for temporary placement of a shared micro-mobility vehicle based on whether the location enables: (i) avoiding interference between the vehicle and a current/predicted people flow, a bicycle lane, and/or one or more objects, (ii) preventing the vehicle from hindering usage of a point of interest, a vehicle parking area, and/or a transit station, or (iii) accommodating the vehicle to a sidewalk configuration. The approach further involves based on the determination, outputting an indication of whether the location is suitable for temporary placement of the vehicle.

Method and apparatus for placing a shared micro-mobility vehicle in public spaces

A method, apparatus, and user interface for a vehicle window damage detection system comprising obtaining image data of at least one vehicle window and determining a window damage indicator based on the obtained image data, identifying one or more road segments, and associating the determined window damage indicator with one or more identified road segments to update a map layer of a geographic database.

Method and apparatus for determining window damage indicators

A network apparatus, for example, obtains probe data corresponding to a respective vehicle making a late lane change while traversing a TME of a traversable network. Location information indicating a location of the late lane change is extracted from the probe data. Map, weather, and/or traffic data for the location is obtained. A late lane change feature description is generated based on information extracted from the probe data and the map, weather, and/or traffic data. A model is trained using a machine learning technique and training data comprising the late lane change feature description. The model is executed to generate a late lane change prediction corresponding to a TME of a digital map. The network apparatus causes at least one of (a) the digital map to be updated, (b) traffic data corresponding to the TME to be updated, or (c) a navigation-related function to be performed based on the prediction.

Methods and apparatuses for late lane change prediction and mitigation

Growing community of inventors

Nantes, France

Jerome Beaurepaire