Here Global B.v.

Jeremy Michael Young

Leon Stenneth

Jerome Beaurepaire

A system to assist an owner of a low-profile vehicle to avoid damages related to vehicle impact upon a raised feature of a road is disclosed. The system may be configured to obtain one or more locations of a vehicle impact upon the raised feature by detecting an occurrence of the vehicle impact and recording contextual factors related to the vehicle impact upon the raised feature, where the one or more obtained locations are clustered into a first location bucket defined by a manual detection of the vehicle impact and a second location bucket defined by an automatic detection of the vehicle impact. The locations may be map-matched to map locations. The system may cluster the map-matched locations and automatically predict, using a trained machine learning model trained on ground truth of a region, potential locations of vehicle impact relevant to the owner of the low-profile vehicle.

Method to support low-profile vehicles

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

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 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 apparatus, method and computer program product are provided for determining a state of visibility for a road object. In one example, the apparatus receives temporal data, calculates an orientation of a light source with respect to a road object using the temporal data, and predicts a state of visibility for the road object based on the orientation of the light source. In another example, the apparatus determines an artificial light source associated with a road object, receives attribute data associated with the artificial light source, determines a state of the artificial light source using the attribute data, and predicts a state of visibility for the road object based on the state of the artificial light source.

Apparatus and methods for predicting a state of visibility for a road object based on a light source associated with the road object

System and methods are provided that allow users of shared vehicles to benefit from an enhanced user experience that seamlessly unlocks and/or provides access to features for autonomous vehicles by proactively computing an interest index based on detected contextual behavioral patterns of the pedestrians such as the trajectory a candidate passenger is walking given a locational context.

Providing access to an autonomous vehicle based on user's detected interest

A method, apparatus and computer program product are provided for controlling a functional road object. For example, at least one processor receives road sign attribute data indicating at least one attribute of a road sign and receives weather forecast data indicating a weather forecast of a location in which the road sign is disposed. The road sign includes at least one machinery for mitigating exposure to one or more weather conditions. The at least one processor identifies a state of visibility for the road sign using the road sign attribute data and the weather forecast data and generates a control signal for controlling the at least one machinery using the state of visibility.

Apparatus and method for controlling a functional road object

A method, apparatus, and user interface for road debris detection comprising, in some embodiments, obtaining image data of at least one navigable link and/or road debris, determining a road debris indicator based on the obtained image data, wherein the road debris indicator includes road debris classification data, and associating the determined road debris indicator with one or more navigable links to update a map layer of a geographic database.

Method and apparatus for determining road debris indicators

An approach is provided for computing an estimated time of arrival via a route based on a degraded state of a vehicle after an accident and/or malfunction. The approach involves receiving, by one or more processors, data indicating an operational status of a vehicle. The approach also involves processing, by the one or more processors, the data to determine a degraded operational state of the vehicle. The approach further involves computing, by the one or more processors, an estimated time of arrival, a route, or a combination thereof of the vehicle based on the degraded operational state.

Method and apparatus for computing an estimated time of arrival via a route based on a degraded state of a vehicle after an accident and/or malfunction

An apparatus, method and computer program product provide a route using a map layer of one or more sound events. For example, the apparatus receives a user preference indicating a sound type, identifies a road segment associated with the sound type from a plurality of road segments, selects a subset from the plurality of road segments as a route based on association of the subset with respect to the road segment, and outputs the route.

Apparatus and methods for providing a route using a map layer of one or more sound events

A method, apparatus and computer program product are provided for predicting a state of visibility for a road object. For example, at least one processor receives road sign attribute data indicating at least one attribute of a road sign. The processor further receives weather forecast data indicating a weather forecast of a location in which the road sign is disposed, and using the road sign attribute data and the weather forecast data, a state of visibility for the road sign is identified.

Apparatus and methods for predicting state of visibility for a road object

A method, apparatus and computer program product are provided to predict a map object based at least in part on audio data. With respect to predicting a map object, audio data created by a vehicle while driving over a road surface is obtained. The audio data includes one or more audio amplitude features, one or more audio frequency features or a combination of audio amplitude features and audio frequency features. The audio data including the one or more audio amplitude features is provided to a machine learning model and the map object that created the audio data while the vehicle interacted therewith is predicted utilizing the machine learning model. A method, apparatus and computer program product are also provided for training the machine learning model to predict the map object based at least in part on audio data.

Method and apparatus for prediciting a map object based upon audio data

An apparatus, method and computer program product are provided for predicting events in which drivers fail to see curbs while the drivers are maneuvering vehicles. In one example, the apparatus receives vehicle attribute data associated with a first vehicle, map data indicating one or more attributes of a road portion including a first curb, and sensor data indicating an orientation of a first driver within the first vehicle. The apparatus causes a machine learning model to render an output as a function of the vehicle attribute data, the map data, and the sensor data. The output indicates a likelihood of which the first driver will not be able to see the first curb at the road portion when the first driver is maneuvering the first vehicle. The machine learning model is trained to predict the output based on historical data indicating events in which second drivers maneuvered second vehicles to encounter the first curb or one or more second curbs.

Apparatus and methods for predicting events in which drivers fail to see curbs

An apparatus, method and computer program product are provided for predicting road locations that are likely to induce tire pressure changes. In one example, the apparatus receives historical data of an event in which a first vehicle is impacted by a first tire pressure change at a first road segment. The historical data indicates point-of-interest (POI) information associated with the first road segment. The apparatus trains a prediction model using the historical data. The prediction model is configured to determine a likelihood in which a second road segment induces a second tire pressure change for a second vehicle based on POI information associated with the second road segment.

Apparatus and methods for predicting locations inducing tire pressure changes

A method, system, and computer readable storage medium are provided for predicting behavior of a vehicle. More specifically, image data of the vehicle is obtained by generating class prediction data for the vehicle and then identifying one or more related points of interest. The method, system, and computer readable storage medium then predict the behavior of the vehicle based upon the one or more points of interest and the class prediction data.

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Chicago, IL, United States of America

Jeremy Michael Young