Patent Description:
In a paper entitled "Prediction of moving bottleneck through the use of probe vehicle: a simulation approach in the framework of three-phase theory" (DOI: <NUM>/<NUM>. <NUM>), Dominik Wegerle et al. describes a methodology for the prediction of a moving bottleneck (MB), in particular, where synchronous to free-flow phase transition points are used to find the approximation of the location and the speed of a single front boundary of the MB.

Therefore, there is a need for an approach for identifying and characterizing mobile work zones (e.g., roadway striping, pothole filling, tree trimming, etc.), for example, using probe data, other vehicle sensor data, etc..

According to one embodiment, a computer-implemented method is provided, according to independent claim <NUM>. Further embodiments of the method are provided in dependent claims <NUM>-<NUM>.

According to another embodiment, an apparatus is provided, according to independent claim <NUM>. Further embodiments of the apparatus are provided in dependent claims <NUM>-<NUM>.

According to another embodiment, a non-transitory computer-readable storage medium is provided, according to independent claim <NUM>.

In various example embodiments, the methods (or processes) can be accomplished on the service provider side or on the mobile device side or in any shared way between service provider and mobile device with actions being performed on both sides.

Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

Examples of a method, apparatus, and computer program for identifying mobile work zones are provided. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It is apparent, however, to one skilled in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention.

The embodiments described herein relate to identifying and characterizing mobile work zones, such as but not limited to work zones created for roadway striping, pothole filling, tree trimming, other roadway maintenance activities, etc. Modern location-based services and applications (e.g., autonomous driving) are increasingly demanding highly accurate and detailed digital map data and traffic incident reports across wide geographic areas. Notifying drivers that there will be an impact to the normal travel time across a road network is an important part of traffic information services. An activity that may impact the travel times is the maintenance of the road network itself or other work/activity occurring on the road segment that may lead to traffic congestion (e.g., directly by blocking traffic or indirectly whereby drivers slow down to view an incident even when the incident is not blocking their immediate path or lane). Providing vehicles (e.g., manually operated, autonomous, or semi-autonomous vehicles) with up-to-date data on traffic incidents can reduce congestion and improve safety on the road network. Currently, traffic service providers can report real-time static work zone incidents on a specific road segment and send, if appropriate, warning messages to drivers driving upstream ahead of incidents based on multiple input resources (e.g., local or community resources, service providers, regulators, etc.). As opposed to mobile work zones, static work zones have a fixed location on a roadway and are thus technically less challenging to identify. By way of example, it is estimated that <NUM>% of the US National Highway System would include work zones during summer peak construction period. Work zones may adversely impact road safety and mobility. In addition, the American public has indicated work zones as second only to poor traffic flow in causing dissatisfaction within the road networks.

A traffic congestion queue/jam can occur and start accumulating as a result of traffic volume exceeding the available road capacity. A corresponding shockwave may happen before or after a congestion forming or releasing. Notifying drivers that there will be an impact to the normal travel time across a road network is an important part of traffic information services. In general, the traffic service providers are able to report real time static incidents including reporting work zones activity on a specific road segment and warning messages to drivers driving upstream ahead of incidents based on the multiple input resources. However, such reporting is focused on stationary work zones, where the roadwork activities are constrained within a fixed work zone area.

The Manual on Uniform Traffic Control Devices for Streets and Highways (MUTCD) defines five categories of work durations: (<NUM>) long-term stationary is work that occupies a location for more than <NUM> days; (<NUM>) intermediate-term stationary is work that occupies a location for more than one daylight period up to <NUM> days, or nighttime work lasting more than <NUM> hour; (<NUM>) short-term stationary is daytime work that occupies a location for more than one hour within a single daylight period; (<NUM>) Short duration is work that occupies a location up to <NUM> hour; and (<NUM>) mobile is work that moves intermittently or continuously. Mobile work zone notifications are lacking in the existing incident collection systems due to ambiguous locations and short durations that make it difficult to accurately present the information. As such, mobile work zones are not represented in the existing incident information systems, which leads drivers on the road network to experience congestions caused by the mobile work zones. As there are constant mobile work zones slowing down traffic in the road networks, there is a need to identify, characterize, and report mobile work zones to upstream drivers to cope with the mobile work zones.

As used herein, the term "mobile work zone" refers is a road segment or area with activities where the work moves intermittently or continuously, rarely stopping for more than a few minutes at a time. Mobile work zone operations tend to be short in duration compared with stationary work zones as defined by MUTCD above. Examples of mobile roadworks include roadway striping, transporting heavy equipment, barrier transfer machines, pavement crack or joint sealing, pothole filling, street sweeping or other debris clearing, roadside mowing and vegetation control, tree trimming, storm drain cleaning, etc. Hereinafter, "mobile work zones" and "mobile roadworks" are used interchangeably.

To address the technical challenges associated with identifying mobile work zones, the system <NUM> of <FIG> introduces a capability for identifying and characterizing mobile work zones based on probe data and/or other sensor data. For example, the system <NUM> can detect a forward forming shockwave associated with a congestion front using vehicle probe data and sensor data, and identify one or more active mobile work zones <NUM>. The system <NUM> can also determine a forward forming shockwave propagation rate during the presence of a mobile work zone <NUM>.

<FIG> is a diagram of a vehicular traffic system, according to one embodiment. In this embodiment, the system <NUM> can take probe data from multiple resources as input, applying one or more algorithms to the probe data and relevant map data, and deliver flow or incident messages (indication one or more work zones <NUM>) as output through a traffic processing engine as shown in <FIG>. The messages (e.g., flow messages, incident messages, etc.) can be delivered to end users in two ways either by over the air radio interfaces or by the connected internet.

For instance, the traffic processing engine retrieves real time probe data including sensor data received from probe vehicles <NUM> (e.g., autonomous vehicles, highly autonomous driving (HAD) vehicles, semi-autonomous vehicles, etc.) and/or mobile devices <NUM>, and map artifact data which describes the road segment topology and geometry. In one embodiment, the traffic processing engine is a part of a traffic platform <NUM> having connectivity to the vehicles <NUM> and mobile devices <NUM> via a communication network <NUM>.

In one instance, the real-time probe data may be reported as probe points, which are individual data records collected at a point in time that records telemetry data for that point in time. A probe point can include attributes such as: (<NUM>) probe ID, (<NUM>) longitude, (<NUM>) latitude, (<NUM>) heading, (<NUM>) speed, and (<NUM>) time.

Upon receiving real time probe data, the traffic processing engine can ingest these probe data, perform steps such as map matching, pathing, etc., and then output an estimate of a current travel speed for a given road segment (e.g. a road link, a traffic message channel (TMC) link, etc.). Based on an output speed category, the traffic processing engine can determine a road condition as in free flow, queueing, stationary, or conditions as describe in the traffic flow theory. In the traffic flow theory, there are six types of classical traffic shockwaves each of which represents a unique transition state between a congestion and a free flow traffic, and the area bounding the shockwaves are considered as congestion in time-space-diagram (TSD) plots. From a user perception perspective, driving speeds equal to or lower than a queueing speed would be considered as a traffic congestion. The transition of the road condition into and out of the congestion state is called congestion forming and releasing, and can cause traffic shockwaves as described. The most common congestion is a triangular shape bounded by backward forming, frontal stationary, and forward recovery shockwaves, which is typical from a congestion at a bottleneck location.

Forward forming shockwaves, also known as moving bottlenecks, may be caused by a slow-moving vehicle in the traffic. The traffic capacity usually is not entirely cut-off, just temporarily reduced across the length of the roadworks. Since the roadworks is moving forward along the road, the signature of the upper left region of the polygon. While a slow-moving vehicle does not necessarily mean mobile roadwork, this phenomena along the available construction information, would make it possible to identify mobile work zones <NUM>.

While these forward forming shockwaves associated with moving bottleneck exist in traffic theory studies, they are hard to find in current traffic systems due to their transient nature. There is theoretical modeling on mobile work zones' impact on traffic and moving bottlenecks, but no practical implementation.

The propagation of the fronts separating different traffic phases look like "shock waves" observed in different other fields of science. However, these "shock-waves" have qualitatively different characteristics in traffic flow in comparison to theoretical shockwaves of the classical Lighthill-Whitham theory. The system <NUM> applies field collected vehicle data (e.g., probe data) in the framework of the three-phase traffic theory to detect mobile work zones <NUM>.

In one embodiment, the system <NUM> can collect mobile/vehicle probe, sensor data of a road network from multiple sources (e.g., community, service provides, regulators, etc.), split and track each vehicle path, detect at least one congestion front event, detect at least one forward forming shockwave (FFS) associated with the at least one congestion front event, determine a forward forming shockwave propagation rate, and identify at least one mobile work zone <NUM>.

In one embodiment, the system <NUM> can follow the flowchart in <FIG> to detect a single forward forming shockwave (FFS) event. <FIG> is a flowchart of a process for detecting a single forward forming shockwave (FFS) event, according to one embodiment. In another embodiment, the system <NUM> can apply a forward forming shockwave (FFS) event detection algorithm as shown in Table <NUM>.

In one embodiment, the system <NUM> can follow the flowchart in <FIG> to determine a forward forming shockwave speed and a backward recovery speed. <FIG> is a flowchart of a process for determining a forward forming shockwave speed and a backward recovery speed, according to one embodiment. The system <NUM> can also determine a forward forming shockwave propagation rate and a backward recovery or moving speed during the presence of a mobile work zone <NUM> using a shockwaves propagation rate algorithm shown in Table <NUM>.

In one embodiment, the system <NUM> can follow the flowchart in <FIG> to determine a mobile work zone <NUM> travel time and speed. <FIG> is a flowchart of a process for determining a mobile work zone <NUM> travel time and speed, according to one embodiment. In another embodiment, the system <NUM> can apply a mobile roadwork zone traffic time algorithm as shown in Table <NUM>.

The system <NUM> then can deliver a mobile roadwork zone message along with a confidence value, e.g., over an air radio interface, transport protocol experts group (TPEG) services by a connected hypertext transfer protocol (HTTP) or user datagram protocol (UDP), dedicated short-range communications (DSRC) broadcasting, etc..

This system <NUM> can analyze a series of mobile roadworks events to identify the shockwave characteristics of the transition into and subsequently out of congestion associated with mobile road works activities. The signatures associated with these events may then be used to update approaching drivers to the location, expected duration and severity of these activities. Additionally, the system <NUM> can provide the mobile work zone <NUM> data to third parties, such as map or navigation service providers, road authorities, etc. to analyze the impact of such activities and determine whether to adjust the timing or duration of mobile work zones <NUM>.

After identifying those signatures representative of mobile roadworks activities, the system <NUM> can compare the signatures to the other types of congestion that may occur on similar roads, using machine learning, artificial intelligence, etc. Examples of the signatures are provided for roadworks locations in the US and Germany, which can be applied world wise.

For example, the system <NUM> can operate the traffic platform <NUM> to determine additional shockwave transitions to be in a mega jam and identify and characterize associated mobile roadworks activities within the congested regions. The traffic platform <NUM> can use the above-described algorithms to determine and present the propagation rate of the transitions. The traffic platform <NUM> can also determine impact on vehicle travel times during the presence of the mobile roadworks. In this example, probe data is used to determine mobile work zones <NUM> (e.g., the location of roadworks, and other features) on the Earth. Ground sources like vehicles <NUM>, robots, user equipment (UE) <NUM> (e.g., mobiles devices) fitted with sensor systems (e.g., global positioning system (GPS), LiDAR, etc.) are also used to acquire probe data for identifying and characterizing mobile work zones <NUM> (e.g., an application <NUM>). For high definition map use (e.g., with centimeter level accuracy), the traffic platform <NUM> can map the features in an area using both top down and ground level sources (e.g., via satellites <NUM>).

In one embodiment, the traffic platform <NUM> can train one or more machine learning models to identify and characterize mobile work zones <NUM> for different regions and/or context, using a machine learning system <NUM>. By way of example, the training can be carried out using a supervised machine learning scheme, such as random forests, support vector machines, or other statistical calculation (including but not limited to averaging, determining a median, determining a minimum/maximum, etc.), etc. Embodiments of the ray intersection technical solution described herein can be very computationally simple, and easy to implement based on pure geometry. This, in turn, enables traffic platform <NUM> advantageously reduce the computing resources (e.g., processing power, memory, bandwidth, etc.) used for identifying and characterizing mobile work zones <NUM> based on a trained machine learning model. The trained machine learning models can be used to process a plurality of other probe data to detect objects or features from work zones.

In another embodiment, the traffic platform <NUM> can collect image data to identify and characterize mobile work zones <NUM>, using a computer vision system <NUM>, for example, to validate the work zone data. Objects or features refer to any feature that is photo-identifiable in the image including, but not limited to, physical features on the ground that can be used as possible candidates for survey points.

In one embodiment, the machine learning system <NUM> and/or the computer vision system <NUM> also have connectivity or access over a communication network <NUM> to a geographic database <NUM> which stores the probe data for different sources, extracted features, features correspondences, quality of sensor system pose data, derived maps, etc. generated according to the embodiments described herein. In one embodiment, the geographic database <NUM> includes representations of features and/or other related geographic features determined from feature correspondences to facilitate identifying and characterizing mobile work zones <NUM>.

Examples are provided and characterized where a mobile work zone is verified by independent means for ground truth: Federal Highway A9 in Bavaria Germany heading towards Munich and US Highway <NUM> across the Commodore Barry Bridge in Chester, Pennsylvania exhibited the behaviors on a repeatable basis. The locations also had an independent means available to confirm the characteristics shown in the time-space diagrams were the result of roadworks operations. These two sites also represent two distinctive types of moving bottlenecks: (<NUM>) slow moving vehicles are not blocking all the other vehicles, such that some other vehicles can pass over the slow moving vehicles; and (<NUM>) slow moving vehicles are blocking all the other vehicles, such that other vehicles have to follow the slow moving vehicles. The major difference between these two types are the so called "starvation zone" in the 2nd type due to the fact that no other vehicles could pass over the slow-moving vehicles in the front.

A barrier transfer operation was identified in the long-term construction zone along the A9 highway in Bavaria, Germany in the spring of <NUM>. This construction zone utilizes a barrier transfer machine to reduce the impact of congestion by changing road capacity based on morning inbound commuter traffic and switches lanes around <NUM> am in the morning to support the later outbound traffic flow. This barrier transfer machine moves the barriers in the median from one lane to another and travels at speeds between <NUM> to <NUM> kph. The operation is verified with traffic cameras on the A9 on March <NUM>, <NUM>, as shown in <FIG> (to be discussed later). The impact of this operation is clearly shown in the time-space-diagram (TSD) plot at the exact same location where the lane barrier is switched. This moving bottleneck exists every day when the lane switch is performed. The lane switch operation was stopped in late March, probably due to lower traffic volumes, and the moving bottleneck is not presented on TSD plots after that date.

In the following, the probe vehicle data was analyzed on two different days at the same location: a freeway section of the A9 in Bavaria, Germany. Connected vehicles have sent their position data with <NUM>-<NUM> sec resolution to the data server, which matches the data on the freeway segments. The penetration rate of the connected vehicle trajectories in the examples is in the order of <NUM>-<NUM> % of the traffic flow rate. If we assume a total flow rate of <NUM> vehicles/hour on the three-lane freeway at peak times, <NUM>-<NUM> connected vehicles send their position data to the data center. As a rule of thumb, this penetration rate is sufficient to reconstruct the congested traffic patterns with good quality. The number of connected probe vehicles on the study segment ranges from <NUM> to <NUM> vehicles/hour during the time, exceeding the required penetration rate. The empirical data is presented via <FIG> using vehicle trajectories in the same gray scale and/or color scheme as in <FIG>, in which the gray scale and/or color slide on the right of the figure shows how the speed changes corresponding to the gray scale and/or color.

<FIG> is a time-space-diagram of probe vehicle data, according to one embodiment. For instance, the TSD plots from March <NUM>, <NUM> on freeway A9 northbound for <NUM> and <NUM> hours, when roadworks were performed on the road segment of around <NUM>-<NUM>.

The following traffic phenomena are shown in <FIG>: (<NUM>) vehicles driving in free traffic outside the area of the roadworks, (<NUM>) a reduced speed due to speed limits (assumption of <NUM>/h) and inside the road construction area on the road segment of around <NUM>-<NUM>, (<NUM>) a dark area growing in positive direction inside the roadworks shortly after <NUM>:<NUM> am, with the detail shown in <FIG>) several dark areas propagating upstream inside the roadworks later.

<FIG> is a time-space-diagram of probe vehicle data showing a moving bottleneck, according to one embodiment. The moving bottleneck due to a slow moving construction vehicle traveling in positive direction. A forward forming region is evident in this diagram. <FIG> is a time-space-diagram of probe vehicle data showing a moving bottleneck, according to one embodiment. The moving bottleneck on the A9 in Bavaria, Germany due to slow moving construction vehicle in positive direction at a speed approximately <NUM> mph. <FIG> shows an TSD example of a mobile road work event using vehicle probe data, when the mobile road work starts, the forward forming shockwave starts as labeled by two broken lines.

To calculate the speed of the shockwaves, the following approach was used:.

<FIG> is a diagram of data points for calculating shockwave speeds, according to one embodiment. The data points in the pointing-up solid line define a forward forming data set and data points in pointing-down solid line define a backward recovery data set.

The delay is calculated based on the following procedures: <NUM>. Confine probe data bounded by the forward forming and the backward recovery shockwaves. Select probe paths that travels the entire region, and calculate the travel speed of each path. Get the average speed of all the selected paths. Compare this average speed with the average speed outside of this congestion region. <FIG> is a diagram of probe paths bounded by shockwaves, according to one embodiment.

The speed of the moving bottleneck, in this example, is calculated to be approximately <NUM>/h. referring back to <FIG>, shortly before <NUM>:<NUM> AM, a slow moving vehicle passes the roadwork section and forces the traffic flow behind it to reduce the speed of <NUM>/h.

In this example, the reason of this slow moving bottleneck is witnessed in the video observation from Ministry in Bavaria, a screen capture of which is shown in <FIG> showing a construction vehicle on the left lane moving slowly. This barrier transfer machine switched the three-lane road dynamically into a two-lane road behind it on each morning. During the initial period of this study, the transfer was done each day after the morning traffic peak in the direction towards Munich. Practically, this induced traffic congestion since the two-lane freeway becomes a bottleneck.

<FIG> is a diagram of an example user interface depicting a mobile work zone, according to one embodiment. A construction vehicle (i.e., barrier transfer machine) was a moving bottleneck reducing the normally three lane road to two lanes at <NUM>:<NUM> am on March <NUM>, <NUM>.

As a second example, the probe data at the same location was investigated later that week from March <NUM>, <NUM>. <FIG> is a time-space-diagram of probe vehicle data showing a moving bottleneck, according to one embodiment. <FIG> depicts probe data from March <NUM>, <NUM> on freeway A9 northbound for ~<NUM> and <NUM> hours, with roadworks causing narrow lanes on the road section around <NUM>-<NUM>.

<FIG> illustrates at about <NUM>:<NUM> am, that a moving bottleneck with the forward forming shockwaves induced a traffic congestion. The most visible difference is the emergence of two large congested patterns at the fixed bottleneck located at the lane reduction when the barrier transfer machine has reduced the number of lanes from three to two after <NUM>:<NUM> am.

The velocity of the barrier machine is very similar on any day: the moving bottleneck moves with about <NUM> kph in positive direction as in <FIG> is a time-space-diagram of probe vehicle data showing a moving bottleneck, according to one embodiment. <FIG> shows a moving bottleneck on the A9 in Bavaria, Germany due to a slow moving construction vehicle in positive direction causing the forward forming shock wave moving downstream at speed approximately around <NUM> kph.

<FIG> is a time-space diagram of probe vehicle data showing a fixed bottleneck, according to one embodiment. <FIG> shows a backward moving jam emerged at <NUM>:<NUM> am March <NUM>, <NUM> on road A9 northbound.

In one embodiment, the traffic platform <NUM> has connectivity over a communication network <NUM> to the services platform <NUM> that provides one or more services <NUM>. By way of example, the services <NUM> may be third party services and include mapping services, navigation services, travel planning services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information based services (e.g., weather, news, etc.), etc. In one embodiment, the services <NUM> uses the output of the traffic platform <NUM> (e.g., location corrected images, features, etc.) to localize the vehicle <NUM> or UE <NUM> (e.g., a portable navigation device, smartphone, portable computer, tablet, etc.) and/or provide services <NUM> such as navigation, mapping, other location-based services, etc..

In one embodiment, the traffic platform <NUM> may be a platform with multiple interconnected components. The traffic platform <NUM> may include multiple servers, intelligent networking devices, computing devices, components, and corresponding software for providing parametric representations of lane lines. In addition, it is noted that the traffic platform <NUM> may be a separate entity of the system <NUM>, a part of the one or more services <NUM>, a part of the services platform <NUM>, or included within the UE <NUM> and/or vehicle <NUM>.

In one embodiment, content providers 125a-<NUM> (collectively referred to as content providers <NUM>) may provide content or data (e.g., including geographic data, parametric representations of mapped features, etc.) to the geographic database <NUM>, the traffic platform <NUM>, the services platform <NUM>, the services <NUM>, the UE <NUM>, the vehicle <NUM>, and/or an application <NUM> executing on the UE <NUM>. The content provided may be any type of content, such as map content, textual content, audio content, video content, image content, etc. In one embodiment, the content providers <NUM> may provide content that may aid in identifying and characterizing mobile work zones. In one embodiment, the content providers <NUM> may also store content associated with the geographic database <NUM>, traffic platform <NUM>, machine learning system <NUM>, computer vision system <NUM>, services platform <NUM>, services <NUM>, UE <NUM>, and/or vehicle <NUM>. In another embodiment, the content providers <NUM> may manage access to a central repository of data, and offer a consistent, standard interface to data, such as a repository of the geographic database <NUM>.

In one embodiment, the UE <NUM> and/or vehicle <NUM> may execute a software application <NUM> to capture probe data or other observation data for identifying and characterizing mobile work zones according to the embodiments described herein. By way of example, the application <NUM> may also be any type of application that is executable on the UE <NUM> and/or vehicle <NUM>, such as autonomous driving applications, mapping applications, location-based service applications, navigation applications, content provisioning services, camera/imaging application, media player applications, social networking applications, calendar applications, and the like. In one embodiment, the application <NUM> may act as a client for the traffic platform <NUM> and perform one or more functions associated with estimating the quality of sensor system pose data alone or in combination with the machine learning system <NUM>.

By way of example, the UE <NUM> is any type of embedded system, mobile terminal, fixed terminal, or portable terminal including a built-in navigation system, a personal navigation device, mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, fitness device, television receiver, radio broadcast receiver, electronic book device, game device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It is also contemplated that the UE <NUM> can support any type of interface to the user (such as "wearable" circuitry, etc.). In one embodiment, the UE <NUM> may be associated with the vehicle <NUM> or be a component part of the vehicle <NUM>.

In one embodiment, the UE <NUM> and/or vehicle <NUM> are configured with various sensors for generating or collecting environmental image data (e.g., for processing by the traffic platform <NUM>), related geographic data, etc. In one embodiment, the sensed data represent sensor data associated with a geographic location or coordinates at which the sensor data was collected. By way of example, the sensors may include a global positioning sensor for gathering location data (e.g., global positioning system (GPS), LiDAR, etc.), a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication (NFC) etc.), temporal information sensors, a camera/imaging sensor for gathering image data (e.g., the camera sensors may automatically capture ground control point imagery, etc. for analysis), an audio recorder for gathering audio data, velocity sensors mounted on steering wheels of the vehicles, switch sensors for determining whether one or more vehicle switches are engaged, and the like.

Other examples of sensors of the UE <NUM> and/or vehicle <NUM> may include light sensors, orientation sensors augmented with height sensors and acceleration sensor (e.g., an accelerometer can measure acceleration and can be used to determine orientation of the vehicle), tilt sensors to detect the degree of incline or decline of the vehicle along a path of travel, moisture sensors, pressure sensors, etc. In a further example embodiment, sensors about the perimeter of the UE <NUM> and/or vehicle <NUM> may detect the relative distance of the vehicle from a lane or roadway, the presence of other vehicles, pedestrians, traffic lights, potholes and any other objects, or a combination thereof. In one scenario, the sensors may detect weather data, traffic information, or a combination thereof. In one embodiment, the UE <NUM> and/or vehicle <NUM> may include GPS or other satellite-based receivers to obtain geographic coordinates from satellites <NUM> for determining current location and time. Further, the location can be determined by visual odometry, triangulation systems such as A-GPS, Cell of Origin, or other location extrapolation technologies. In yet another embodiment, the sensors can determine the status of various control elements of the car, such as activation of wipers, use of a brake pedal, use of an acceleration pedal, angle of the steering wheel, activation of hazard lights, activation of head lights, etc..

In one embodiment, the communication network <NUM> of system <NUM> includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.

By way of example, the traffic platform <NUM>, machine learning system <NUM>, computer vision system <NUM>, services platform <NUM>, services <NUM>, UE <NUM>, vehicle <NUM>, and/or content providers <NUM> communicate with each other and other components of the system <NUM> using well known, new or still developing protocols. In this context, a protocol includes a set of rules defining how the network nodes within the communication network <NUM> interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model.

<FIG> is a diagram of a geographic database (such as the database <NUM>), according to one embodiment. In one embodiment, the geographic database <NUM> includes geographic data <NUM> used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for video odometry based on the parametric representation of lanes include, e.g., encoding and/or decoding parametric representations into lane lines. In one embodiment, the geographic database <NUM> include high resolution or high definition (HD) mapping data that provide centimeter-level or better accuracy of map features. For example, the geographic database <NUM> can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths. In one embodiment, the HD mapping data (e.g., HD data records <NUM>) capture and store details such as the slope and curvature of the road, lane markings, roadside objects such as signposts, including what the signage denotes. By way of example, the HD mapping data enable highly automated vehicles to precisely localize themselves on the road.

In one embodiment, geographic features (e.g., two-dimensional, or three-dimensional features) are represented using polygons (e.g., two-dimensional features) or polygon extrusions (e.g., three-dimensional features). For example, the edges of the polygons correspond to the boundaries or edges of the respective geographic feature. In the case of a building, a two-dimensional polygon can be used to represent a footprint of the building, and a three-dimensional polygon extrusion can be used to represent the three-dimensional surfaces of the building. It is contemplated that although various embodiments are discussed with respect to two-dimensional polygons, it is contemplated that the embodiments are also applicable to three-dimensional polygon extrusions. Accordingly, the terms polygons and polygon extrusions as used herein can be used interchangeably.

In one embodiment, the following terminology applies to the representation of geographic features in the geographic database <NUM>.

"Node" - A point that terminates a link.

"Line segment" - A straight line connecting two points.

"Link" (or "edge") - A contiguous, non-branching string of one or more line segments terminating in a node at each end.

"Shape point" - A point along a link between two nodes (e.g., used to alter a shape of the link without defining new nodes).

"Oriented link" - A link that has a starting node (referred to as the "reference node") and an ending node (referred to as the "non reference node").

"Simple polygon" - An interior area of an outer boundary formed by a string of oriented links that begins and ends in one node. In one embodiment, a simple polygon does not cross itself.

"Polygon" - An area bounded by an outer boundary and none or at least one interior boundary (e.g., a hole or island). In one embodiment, a polygon is constructed from one outer simple polygon and none or at least one inner simple polygon. A polygon is simple if it just consists of one simple polygon, or complex if it has at least one inner simple polygon.

In one embodiment, the geographic database <NUM> follows certain conventions. For example, links do not cross themselves and do not cross each other except at a node. Also, there are no duplicated shape points, nodes, or links. Two links that connect each other have a common node. In the geographic database <NUM>, overlapping geographic features are represented by overlapping polygons. When polygons overlap, the boundary of one polygon crosses the boundary of the other polygon. In the geographic database <NUM>, the location at which the boundary of one polygon intersects they boundary of another polygon is represented by a node. In one embodiment, a node may be used to represent other locations along the boundary of a polygon than a location at which the boundary of the polygon intersects the boundary of another polygon. In one embodiment, a shape point is not used to represent a point at which the boundary of a polygon intersects the boundary of another polygon.

As shown, the geographic database <NUM> includes node data records <NUM>, road segment or link data records <NUM>, POI data records <NUM>, mobile work zone data records <NUM>, HD mapping data records <NUM>, and indexes <NUM>, for example. More, fewer, or different data records can be provided. In one embodiment, additional data records (not shown) can include cartographic ("carto") data records, routing data, and maneuver data. In one embodiment, the indexes <NUM> may improve the speed of data retrieval operations in the geographic database <NUM>. In one embodiment, the indexes <NUM> may be used to quickly locate data without having to search every row in the geographic database <NUM> every time it is accessed. For example, in one embodiment, the indexes <NUM> can be a spatial index of the polygon points associated with stored feature polygons.

In exemplary embodiments, the road segment data records <NUM> are links or segments representing roads, streets, or paths, as can be used in the calculated route or recorded route information for determination of one or more personalized routes. The node data records <NUM> are end points corresponding to the respective links or segments of the road segment data records <NUM>. The road link data records <NUM> and the node data records <NUM> represent a road network, such as used by vehicles, cars, and/or other entities. Alternatively, the geographic database <NUM> can contain path segment and node data records or other data that represent pedestrian paths or areas in addition to or instead of the vehicle road record data, for example.

The road/link segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs, such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The geographic database <NUM> can include data about the POIs and their respective locations in the POI data records <NUM>. The geographic database <NUM> can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc. Such place or feature data can be part of the POI data records <NUM> or can be associated with POIs or POI data records <NUM> (such as a data point used for displaying or representing a position of a city).

In one embodiment, the geographic database <NUM> can also include mobile work zone data records <NUM> for storing mobile work zone data, training data, prediction models, annotated observations, computed featured distributions, sampling probabilities, and/or any other data generated or used by the system <NUM> according to the various embodiments described herein. By way of example, the mobile work zone data records <NUM> can be associated with one or more of the node records <NUM>, road segment records <NUM>, and/or POI data records <NUM> to support localization or visual odometry based on the features stored therein and the corresponding estimated quality of the features. In this way, the records <NUM> can also be associated with or used to classify the characteristics or metadata of the corresponding records <NUM>, <NUM>, and/or <NUM>.

In one embodiment, as discussed above, the HD mapping data records <NUM> model road surfaces and other map features to centimeter-level or better accuracy. The HD mapping data records <NUM> also include lane models that provide the precise lane geometry with lane boundaries, as well as rich attributes of the lane models. These rich attributes include, but are not limited to, lane traversal information, lane types, lane marking types, lane level speed limit information, and/or the like. In one embodiment, the HD mapping data records <NUM> are divided into spatial partitions of varying sizes to provide HD mapping data to vehicles <NUM> and other end user devices with near real-time speed without overloading the available resources of the vehicles <NUM> and/or devices (e.g., computational, memory, bandwidth, etc. resources).

In one embodiment, the HD mapping data records <NUM> are created from high-resolution 3D mesh or point-cloud data generated, for instance, from LiDAR-equipped vehicles. The 3D mesh or point-cloud data are processed to create 3D representations of a street or geographic environment at centimeter-level accuracy for storage in the HD mapping data records <NUM>.

In one embodiment, the HD mapping data records <NUM> also include real-time sensor data collected from probe vehicles in the field. The real-time sensor data, for instance, integrates real-time traffic information, weather, and road conditions (e.g., potholes, road friction, road wear, etc.) with highly detailed 3D representations of street and geographic features to provide precise real-time also at centimeter-level accuracy. Other sensor data can include vehicle telemetry or operational data such as windshield wiper activation state, braking state, steering angle, accelerator position, and/or the like.

In one embodiment, the geographic database <NUM> can be maintained by the content provider <NUM> in association with the services platform <NUM> (e.g., a map developer). The map developer can collect geographic data to generate and enhance the geographic database <NUM>. There can be different ways used by the map developer to collect data. These ways can include obtaining data from other sources, such as municipalities or respective geographic authorities. In addition, the map developer can employ field personnel to travel by vehicle (e.g., vehicles <NUM> and/or user terminals <NUM>) along roads throughout the geographic region to observe features and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography, can be used.

For example, geographic data is compiled (such as into a platform specification format (PSF) format) to organize and/or configure the data for performing navigation-related functions and/or services, such as route calculation, route guidance, map display, speed calculation, distance and travel time functions, and other functions, by a navigation device, such as by a vehicle <NUM> or a user terminal <NUM>, for example. The navigation-related functions can correspond to vehicle navigation, pedestrian navigation, or other types of navigation. The compilation to produce the end user databases can be performed by a party or entity separate from the map developer. For example, a customer of the map developer, such as a navigation device developer or other end user device developer, can perform compilation on a received geographic database in a delivery format to produce one or more compiled navigation databases.

The processes described herein for identifying and characterizing mobile work zones may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below.

<FIG> illustrates a computer system <NUM> upon which an embodiment of the invention may be implemented. Computer system <NUM> is programmed (e.g., via computer program code or instructions) to identify mobile work zones as described herein and includes a communication mechanism such as a bus <NUM> for passing information between other internal and external components of the computer system <NUM>. Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (<NUM>, <NUM>) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range.

A processor <NUM> performs a set of operations on information as specified by computer program code related to identifying mobile work zones. The computer program code is a set of instructions or statements providing instructions for the operation of the processor and/or the computer system to perform specified functions. The code, for example, may be written in a computer programming language that is compiled into a native instruction set of the processor. The code may also be written directly using the native instruction set (e.g., machine language). The set of operations include bringing information in from the bus <NUM> and placing information on the bus <NUM>. The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication or logical operations like OR, exclusive OR (XOR), and AND. Each operation of the set of operations that can be performed by the processor is represented to the processor by information called instructions, such as an operation code of one or more digits. A sequence of operations to be executed by the processor <NUM>, such as a sequence of operation codes, constitute processor instructions, also called computer system instructions or, simply, computer instructions. Processors may be implemented as mechanical, electrical, magnetic, optical, chemical or quantum components, among others, alone or in combination.

Computer system <NUM> also includes a memory <NUM> coupled to bus <NUM>. The memory <NUM>, such as a random access memory (RAM) or other dynamic storage device, stores information including processor instructions for identifying mobile work zones. Dynamic memory allows information stored therein to be changed by the computer system <NUM>. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory <NUM> is also used by the processor <NUM> to store temporary values during execution of processor instructions. The computer system <NUM> also includes a read only memory (ROM) <NUM> or other static storage device coupled to the bus <NUM> for storing static information, including instructions, that is not changed by the computer system <NUM>. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus <NUM> is a non-volatile (persistent) storage device <NUM>, such as a magnetic disk, optical disk, or flash card, for storing information, including instructions, that persists even when the computer system <NUM> is turned off or otherwise loses power.

Information, including instructions for identifying mobile work zones, is provided to the bus <NUM> for use by the processor from an external input device <NUM>, such as a keyboard containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system <NUM>. Other external devices coupled to bus <NUM>, used primarily for interacting with humans, include a display device <NUM>, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), or plasma screen or printer for presenting text or images, and a pointing device <NUM>, such as a mouse or a trackball or cursor direction keys, or motion sensor, for controlling a position of a small cursor image presented on the display <NUM> and issuing commands associated with graphical elements presented on the display <NUM>. In some embodiments, for example, in embodiments in which the computer system <NUM> performs all functions automatically without human input, one or more of external input device <NUM>, display device <NUM> and pointing device <NUM> is omitted.

Computer system <NUM> also includes one or more instances of a communications interface <NUM> coupled to bus <NUM>. Communication interface <NUM> provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners, and external disks. In general the coupling is with a network link <NUM> that is connected to a local network <NUM> to which a variety of external devices with their own processors are connected. For example, communication interface <NUM> may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface <NUM> is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface <NUM> is a cable modem that converts signals on bus <NUM> into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface <NUM> may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. For wireless links, the communications interface <NUM> sends or receives or both sends and receives electrical, acoustic, or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface <NUM> includes a radio band electromagnetic transmitter and receiver called a radio transceiver. In certain embodiments, the communications interface <NUM> enables connection to the communication network <NUM> for identifying mobile work zones to the vehicle <NUM> and/or the UE <NUM>.

Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Signals include man-made transient variations in amplitude, frequency, phase, polarization, or other physical properties transmitted through the transmission media.

Network link <NUM> typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link <NUM> may provide a connection through local network <NUM> to a host computer <NUM> or to equipment <NUM> operated by an Internet Service Provider (ISP). ISP equipment <NUM> in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet <NUM>.

A computer called a server host <NUM> connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host <NUM> hosts a process that provides information representing video data for presentation at display <NUM>. It is contemplated that the components of system can be deployed in various configurations within other computer systems, e.g., host <NUM> and server <NUM>.

<FIG> illustrates a chip set <NUM> upon which an embodiment of the invention may be implemented. Chip set <NUM> is programmed to identify mobile work zones as described herein and includes, for instance, the processor and memory components described with respect to <FIG> incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set can be implemented in a single chip.

The processor <NUM> and accompanying components have connectivity to the memory <NUM> via the bus <NUM>. The memory <NUM> includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to identify mobile work zones. The memory <NUM> also stores the data associated with or generated by the execution of the inventive steps.

<FIG> is a diagram of exemplary components of a mobile terminal (e.g., handset) capable of operating in the system of <FIG>, according to one embodiment. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the back-end encompasses all of the base-band processing circuitry. Pertinent internal components of the telephone include a Main Control Unit (MCU) <NUM>, a Digital Signal Processor (DSP) <NUM>, and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit <NUM> provides a display to the user in support of various applications and mobile station functions that offer automatic contact matching. An audio function circuitry <NUM> includes a microphone <NUM> and microphone amplifier that amplifies the speech signal output from the microphone <NUM>. The amplified speech signal output from the microphone <NUM> is fed to a coder/decoder (CODEC) <NUM>.

The MCU <NUM> receives various signals including input signals from the keyboard <NUM>. The keyboard <NUM> and/or the MCU <NUM> in combination with other user input components (e.g., the microphone <NUM>) comprise a user interface circuitry for managing user input. The MCU <NUM> runs a user interface software to facilitate user control of at least some functions of the mobile station <NUM> to identify mobile work zones. The MCU <NUM> also delivers a display command and a switch command to the display <NUM> and to the speech output switching controller, respectively. Further, the MCU <NUM> exchanges information with the DSP <NUM> and can access an optionally incorporated SIM card <NUM> and a memory <NUM>. In addition, the MCU <NUM> executes various control functions required of the station. The DSP <NUM> may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP <NUM> determines the background noise level of the local environment from the signals detected by microphone <NUM> and sets the gain of microphone <NUM> to a level selected to compensate for the natural tendency of the user of the mobile station <NUM>.

An optionally incorporated SIM card <NUM> carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card <NUM> serves primarily to identify the mobile station <NUM> on a radio network. The card <NUM> also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile station settings.

Claim 1:
A computer-implemented method comprising:
processing probe data, including a timestamp sorted list of vehicle path probe points for each of a plurality of vehicles, to determine characteristics of a forward forming shockwave associated with a congestion front of a mobile roadwork zone (<NUM>) by:
applying an algorithm for detecting a forward forming shockwave event to each of the plurality of timestamped sorted lists,
the algorithm comprising tracing the speeds of pairs of probe points of the sorted list to record a forward forming shockwave event in response to (i) a first probe point of a given pair having at most a predetermined maximum speed, (ii) the other probe point of the given pair having at least a predetermined minimum speed, and (iii) the time by which the two probe points of the given pair are separated being under a predetermined minimum time,
the recorded forward forming shockwave events having corresponding timestamps and location information;
plotting the recorded forward forming shockwave events as points in a distance-time diagram;
separating the points on the distance-time diagram into a distance-increasing group and a distance-decreasing group, wherein a distance value increases or decreases with time, in the diagram, for the distance-increasing group or the distance-decreasing group, respectively; and
performing linear regression for each of the distance-increasing group and distance-decreasing group to determine, respectively, a forward forming shockwave speed and a backward recovery speed; and
providing, as an output, data indicating location, expected duration and/or severity of roadwork activity of the mobile roadwork zone based on the determined characteristics of the forward forming shockwave.