Here Global B.v.

Amarnath Nayak

Leon Stenneth

Alex Averbuch

Jerome Beaurepaire

Bruce A Bernhardt

Advait Mohan Raut

Ram Marappan

Sandeep Belgaonkar

Systems and methods are described for the vehicle emission measurement. An example method may include receiving a plurality of images from a first vehicle traveling on a section of roadway, determining a quantity of surrounding vehicles from the plurality of images, determining a cropped image of at least one of the surrounding vehicles from the plurality of images, identifying a model of the at least one of the surrounding vehicles from the cropped image, and calculating an emission measurement factor for the section of roadway based on at least the quantity of surrounding vehicles for the at least one of the surrounding vehicles.

Vehicle emission measurement

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

A method, apparatus, user interface, and computer program product are provided to receive an aerial image of a geographic region, identify at least one potential map object within the aerial image, determine at least one shadow area associated with the at least one potential map object within the aerial image, identify at least one camera proximate to the location of the at least one potential map object, capture image data of the area proximate to the least one potential map object via the at least one camera, and generate location data for the at least one map object or a road attribute based at least in part on the aerial image and image data of the area proximate to the least one potential map object or road attribute.

Method, apparatus, user interface, and computer program product for identifying map objects or road attributes based on imagery

An apparatus, method and computer program product are provided for providing a tire pressure analysis. In one example, the apparatus receives first sensor data indicating a first tire pressure change associated with a first vehicle and causes a prediction model to generate a predicted cause of the first tire pressure change based on the first sensor data. The prediction model is trained based on historical data, the historical data defining a plurality of tire pressure signatures. Each of the plurality of tire pressure signatures correlates a cause of a second tire pressure change associated with a second vehicle to second sensor data indicating one or more tire pressure levels of the second vehicle over a period in which a wheel of the second vehicle completes a revolution. The apparatus further causes a notification of the predicted cause at a user interface associated with the first vehicle.

Apparatus and methods for providing tire pressure analysis

An apparatus, method and computer program product are provided for providing a map layer of noise levels. In one example, the apparatus receives input data including an image and location data indicating an area in which the image was captured. The apparatus causes a machine learning model to generate a datapoint in a map layer as a function of the input data. The datapoint indicate a predicted decibel level at the area during an instance in which the image was captured. The machine learning model is trained to generate the datapoint as a function of the input data based on training data, where the training data include images and audio data indicating decibel levels of areas in which the images were captured during instances in which the images were captured.

Apparatus and methods providing a map layer noise levels

Embodiments described herein may provide a method for analyzing traffic congestion at a toll plaza and identifying a preferred lane of travel through the toll plaza based on the traffic congestion. Methods may include: determining active toll lanes of all toll lanes at a toll plaza; obtaining probe data for each active toll lane at the toll plaza; determining lane-wise congestion for each active toll lane at the toll plaza; identifying a preferred lane of the active toll lanes; and providing for at least one of a navigational instruction or at least semi-autonomous vehicle control to enter or continue in the preferred lane.

Method, apparatus and computer program product for lane level toll plaza congestion modeling

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

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. Traffic violation data from one region is used to train a model that is applied to a second region for which traffic violation data does not exist or is limited.

Predicting traffic violation hotspots using map features and sensors data

A system, a method, and a computer program product may be provided for processing event data. For example, event data relating to an occurrence of an event corresponding to a first tile is received, and a first count value associated with the received event data is determined. A threshold count value associated with the event within the first tile is obtained, and dynamic switching between a link-based processing and a tile-based processing is performed, based on a comparison between the first count value and the threshold count value, wherein on determining the first count value to be greater than the threshold count value, the tile-based processing is performed, and on determining the first count value to be lesser than the threshold count value, the link-based processing is performed.

System and method for processing event data

A method, apparatus and computer program product are provided for automatically identify wrong-way driven vehicles in real-time on a roadway. Methods may include: receiving an image from at least one image sensor captured at a location; processing the image using a computer vision model to detect features within the image; identifying, using the computer vision model, a wrong-way driven vehicle in the image; incrementing a confidence value for detection of the wrong-way driven vehicle; and generating an alert indicating the presence of the wrong-way driven vehicle at the location in response to the confidence value satisfying a predetermined value.

Method, apparatus, and computer program product for identifying wrong-way driven vehicles

An approach is provided for determining an autonomous vehicle reaction strategy when facing a wrong way driving vehicle. The approach, for example, involves processing mapping data, sensor data, or a combination thereof to develop a map of an area within a threshold distance of an autonomous vehicle in response to a detection of an oncoming vehicle that is driving in a wrong direction towards the autonomous vehicle. The approach also involves using a decision tree of a plurality of candidate strategies for avoiding the oncoming vehicle to select a strategy based on one or more attributes of the map. The approach further involves presenting the selected strategy in a user interface of the autonomous vehicle.

Method, apparatus, and system for determining an autonomous vehicle operational strategy when detecting wrong way driving

Embodiments described herein may provide a method for identifying the location of a hazard, evaluating how long that hazard is likely to be present at the location, and providing an indication of the time to live of the hazard. Methods may include: receiving an indication of a hazard event, where the hazard event includes a timestamp and a location; map matching the location of the hazard event to a map-matched road segment; determining a time to live of the hazard event based, at least in part, on the timestamp and the map-matched road segment; providing an indication of the time to live of the hazard event as at least one of a database entry or a map user interface element; and providing for at least one of route guidance or navigational assistance based, at least in part, on the time to live of the hazard event.

Method, apparatus and computer program product for estimating hazard duration

An apparatus, method and computer program product are provided for predicting wrong-way-driving events. A WWD event defines an event in which a vehicle travels in a direction opposing a traffic direction designated for a portion of a road. In one example, the apparatus estimates a likelihood of portion of the road inducing a wrong-way-driving (WWD) event by using a machine learning model as a function of map data, sensor data, or a combination thereof. If the likelihood satisfies a threshold, the apparatus updates a map layer to indicate the WWD event at the portion of the road.

Apparatus and methods for predicting wrong-way-driving events

A method, apparatus and computer program product for determining a rain event warning are described herein. In the context of a method, an indication of the rain event warning for one or more locations may be received. The method may obtain, for a respective rain event warning, sensor data corresponding to a weather condition within a region that includes the location with which the respective rain event warning is associated. The method may evaluate the sensor data to determine whether the sensor data is indicative of the weather condition (e.g., rain). The method may determine a rain event warning confidence based at least in part on evaluation of the sensor data. The method may cause the rain event warning to be published in an instance in which the rain event warning confidence satisfies a confidence threshold.

Method and apparatus for verifying rain event warnings

A system, a method and a computer program product are provided for evaluating quality of data, such as sensor data and map data, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain first sensor features of the first sensor data associated with a road object in a first geographic region, first map features of the first map data associated with the road object and ground truth data associated with the road object. The processor may be configured to generate the machine learning model by configuring the ground truth data and calculating first information scores for each of the first sensor features and the first map features by recursively splitting each of the first sensor features and the first map features.

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Mumbai, India

Amarnath Nayak