Traffic condition judgment

Abstract

A server accesses vehicle data from each connected vehicle of a subset of the plurality of connected vehicles (CVs) on the road segment, the vehicle data including at least one of position, speed, or following distance. The server generates a long-term shared world model based on the accessed vehicle data. The server generates a data structure representing future predicted speeds of the road segment by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model. The server transmits a control signal to a connected autonomous vehicle (CAV) based on the generated data structure to control operation of the connected autonomous vehicle.

Description

[Technical field]

[0001] The present disclosure relates generally to connected and autonomous vehicles, and more specifically to the creation and use of long-term shared world models of roads for travel by connected and autonomous vehicles. [Background technology]

[0002] Traffic congestion has high costs, e.g., lost man-time, excess carbon emissions, and increased likelihood of vehicle accidents. An increasing proportion of vehicles on the road are connected vehicles (CVs), which can transmit data over a network, and connected and autonomous vehicles (CAVs), which can both transmit data over the network and receive control signals over the network. Summary of the Invention

[0003] Disclosed herein are aspects, features, elements, implementations, and embodiments that use belief state determination for real-time decision making while a vehicle is traveling through a vehicular traffic network.

[0004] An aspect of the disclosed embodiment is a method for generating and transmitting control signals to a vehicle. The method includes accessing vehicle data from each connected vehicle of a subset of a plurality of connected vehicles (CVs) on a road segment, the vehicle data including at least one of a position, a speed, or a vehicle-to-vehicle distance. The method includes generating a long-term shared world model based on the accessed vehicle data. The method includes generating a data structure representing future predicted speeds for the road segment by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model. The method includes transmitting a control signal to a connected autonomous vehicle (CAV) for controlling operation of the connected autonomous vehicle based on the generated data structure.

[0005] An aspect of the disclosed embodiment is an apparatus for generating and transmitting control signals to a vehicle. The apparatus includes a processor and a memory. The memory stores instructions. The processor executes instructions to access vehicle data from each connected vehicle of a subset of a plurality of connected vehicles (CVs) on a road segment, the vehicle data including at least one of position, speed, or following distance. The processor executes instructions to generate a long-term shared world model based on the accessed vehicle data. The processor executes instructions to generate a data structure representing future predicted speeds of the road segment by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model. The processor executes instructions to transmit a control signal to a connected autonomous vehicle (CAV) for controlling operation of the connected autonomous vehicle based on the generated data structure.

[0006] One aspect of the disclosed embodiment is a computer readable medium for generating and transmitting a control signal to a vehicle. The computer readable medium has instructions stored thereon. The instructions include code for accessing vehicle data from each connected vehicle of a subset of a plurality of connected vehicles (CVs) on a road segment, the vehicle data including at least one of a position, a speed, or a vehicle-to-vehicle distance. The instructions include code for generating a long-term shared world model based on the accessed vehicle data. The instructions include code for generating a data structure representing a future predicted speed of the road segment by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model. The instructions include code for transmitting a control signal to a connected automated vehicle (CAV) based on the generated data structure for controlling operation of the connected automated vehicle.

[0007] These and other aspects, features, elements, implementations and embodiment variations of the methods, apparatus, procedures and algorithms disclosed herein are described in further detail below. [Brief description of the drawings]

[0008] The various aspects disclosed herein will become more apparent with reference to the following description and examples provided in the drawings, in which like reference numerals refer to like elements.

[0009] [Figure 1] FIG. 1 illustrates an example of a vehicle in which aspects, features, and elements disclosed herein may be implemented.

[0010] [Diagram 2] FIG. 1 is an example diagram of a portion of a vehicle traffic and communication system in which aspects, features, and elements disclosed herein may be implemented.

[0011] [Diagram 3] FIG. 2 is a data flow diagram of a technique for generating control signals using vehicle data, according to an embodiment of the disclosure.

[0012] [Figure 4] FIG. 1 illustrates a data flow diagram of a long-term shared world model architecture, according to an embodiment of the present disclosure.

[0013] [Diagram 5] 1 illustrates a flow graph over space and time, according to an embodiment of the present disclosure.

[0014] [Figure 6] 4 illustrates speed and distance values ​​measured for a probe vehicle according to an embodiment of the present disclosure.

[0015] [Figure 7] FIG. 2 is a data flow diagram for traffic state determination according to an embodiment of the present disclosure.

[0016] [Figure 8] 1 illustrates an example vehicle communication and control architecture, according to an embodiment of the present disclosure.

[0017] [Figure 9]FIG. 2 is a data flow diagram for vehicle communication and control according to an embodiment of the disclosure.

[0018] [Figure 10] 1 is a flowchart of a method for operating a vehicle using a long-term shared world model according to an embodiment of the present disclosure.

[0019] [Figure 11] 1 is a flowchart of a method for predicting future traffic data according to an embodiment of the present disclosure.

[0020] [Figure 12] 1 is a flowchart of a method for controlling vehicles using a multi-vehicle policy selector according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] As discussed above, traffic congestion has high costs, for example, lost man-time, excess carbon emissions, and increased likelihood of vehicle accidents. An increasing proportion of vehicles on the road are connected vehicles (CVs) that can transmit data over a network, and connected and autonomous vehicles (CAVs) that can transmit data over a network and receive control signals over a network. Technologies that leverage CV or CAV technologies to reduce traffic congestion may be desirable.

[0022] According to some embodiments, a vehicle control server (which may be implemented as one or more physical or virtual machines) accesses vehicle data from each CV of a subset of the plurality of CVs in the road portion. The vehicle data may be accessed from road sensors. The vehicle data may include a position of the vehicle or other vehicles providing the data, a speed of the vehicle or other vehicles providing the data, and / or a following distance of the vehicle or other vehicles providing the data. The vehicle control server stores the accessed vehicle data in a long-term shared world model. The vehicle control server generates a data structure representing future predicted speeds of the road portion by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model. The traffic flow model may be an artificial intelligence model trained using at least one of, for example, supervised learning, unsupervised learning, reinforcement learning, online learning, and the like. The vehicle control server transmits control signals to the CAV for controlling operation of the CAV based on the generated data structure.

[0023] As used herein, the term "model" may include at least one of a classical planning model, an artificial intelligence (AI) model, or a machine learning (ML) model using supervised learning, unsupervised learning, reinforcement learning, etc., among others. A model may be based on data generated in the past and may be used to predict future data. For example, a long-term shared world model of a road segment may store data on the average speed and congestion (e.g., number of vehicles per unit distance) of a road segment in the past (e.g., multiple times in the past three years) and may be used to predict the average speed and congestion of the road segment in the future (e.g., 9 a.m. next Monday). The prediction may be made, for example, using AI or ML techniques or other mathematical modeling techniques.

[0024] As used herein, the term "shared" may indicate, among other things, that a shared item is accessed by at least two entities. For example, at least two vehicles may provide data to and / or receive data derived from a sharing model. As used herein, the term "long term" may indicate that data is based on a time period that exceeds a threshold. For example, a long term model may be based on data generated over a time period that exceeds one hour, two hours, one day, three days, one week, five weeks, one month, six months, one year, etc. As used herein, the term "world" may indicate, among other things, data based on two or more points in space.

[0025] As used herein, a road portion may include a continuous portion of a road (e.g., a roadway that lasts 1-2 kilometers). A road portion may be subdivided into multiple road segments. For example, a 1 kilometer road portion may be divided into 10 road segments of 100 meters each, without gaps or overlaps.

[0026] It should be noted that all CAVs are CVs. However, a vehicle may be a CV but not a CAV. For example, a vehicle operated by a human driver that can transmit over a network is a CV but not a CAV. In some implementations, with appropriate authorization, CV technology may be implemented by a computing device (e.g., a mobile phone) onboard the vehicle. For example, the computing device may determine the speed of the vehicle (e.g., using the device's onboard Global Positioning System (GPS) and the device's onboard clock) and transmit the vehicle's speed over the network.

[0027] FIG. 1 illustrates an example of a vehicle in which aspects, features, and elements disclosed herein may be implemented. As illustrated, the vehicle 100 includes a chassis 110, a powertrain 120, a controller 130, and wheels 140. For simplicity, the vehicle 100 is shown to include four wheels 140, but any other propulsion device, such as one or more propellers or treads, may be used. In FIG. 1, lines interconnecting elements, such as the powertrain 120, the controller 130, and the wheels 140, indicate that information, such as data or control signals, forces, such as power or torque, or both information and power, may be transferred between the elements. For example, the controller 130 may receive power from the powertrain 120 and communicate with the powertrain 120, the wheels 140, or both, to control the vehicle 100, which may include accelerating, decelerating, steering, or otherwise controlling the vehicle 100.

[0028] As shown, powertrain 120 includes a power source 121, a transmission 122, a steering system 123, and an actuator 124. Other elements or combinations of elements of the powertrain may also be included, such as suspension, drive shafts, axles, or an exhaust system. Although shown separately, wheels 140 may also be included in powertrain 120.

[0029] The power source 121 may include an engine, a battery, or a combination thereof. The power source 121 may be any device or combination of devices that operates to provide energy, such as electrical energy, thermal energy, or kinetic energy. For example, the power source 121 may include an engine, such as an internal combustion engine, an electric motor, or a combination of an internal combustion engine and an electric motor, and may operate to provide kinetic energy as motive power to one or more wheels 140. The power source 121 may include a potential energy device, such as one or more dry batteries, such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), solar cells, fuel cells, or any other device capable of providing energy.

[0030] The transmission 122 may receive energy, such as kinetic energy, from the power source 121 and may send energy to the wheels 140 to provide motive power. The transmission 122 may be controlled by the controller 130, the actuator 124, or both. The steering device 123 may be controlled by the controller 130, the actuator 124, or both and may control the wheels 140 to steer the vehicle. The actuator 124 may receive signals from the controller 130 and may operate or control the power source 121, the transmission 122, the steering device 123, or any combination thereof to operate the vehicle 100.

[0031] As shown, the controller 130 may include a positioning device 131, an electronic communication device 132, a processor 133, a memory 134, a user interface 135, a sensor 136, an electronic communication interface 137, or any combination thereof. Although shown as a single device, any one or more elements of the controller 130 may be incorporated into any number of separate physical devices. For example, the user interface 135 and the processor 133 may be incorporated into a first physical device and the memory 134 may be incorporated into a second physical device. Although not shown in FIG. 1, the controller 130 may include a power source 1210, such as a battery. Although shown as separate elements, the positioning device 131, the electronic communication device 132, the processor 133, the memory 134, the user interface 135, the sensor 136, the electronic communication interface 137, or any combination thereof, may be incorporated into one or more electronic devices, circuits, or chips.

[0032] The processor 133 may include any device or combination of devices now existing or later developed capable of manipulating or processing signals or other information, including optical processors, quantum processors, molecular processors, or combinations thereof. For example, the processor 133 may include one or more special purpose processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more integrated circuits, one or more application specific integrated circuits, one or more field programmable gate arrays, one or more programmable logic arrays, one or more programmable logic controllers, one or more state machines, or any combination thereof. The processor 133 may be operatively coupled to the positioning device 131, the memory 134, the electronic communication interface 137, the electronic communication device 132, the user interface 135, the sensor 136, the powertrain 120, or any combination thereof. For example, the processor may be operatively coupled to the memory 134 via a communication bus 138.

[0033] Memory 134 may include any tangible, non-transitory computer usable or computer readable medium capable of, for example, holding, storing, transmitting or carrying machine-readable instructions or any information associated therewith for use by or in connection with processor 133. Memory 134 may be, for example, one or more solid-state drives, one or more memory cards, one or more removable media, one or more read-only memories, one or more random access memories, one or more disks including hard disks, floppy disks, optical disks, magnetic or optical cards, or any type of non-transitory medium suitable for storing electronic information, or any combination thereof.

[0034] The communications interface 137 may be a wireless antenna as shown, a wired communications port, an optical communications port, or any other wired or wireless device capable of interfacing with a wired or wireless electronic communications medium 150. Although Figure 1 illustrates communications interface 137 communicating over a single communications link, the communications interface may be configured to communicate over multiple communications links. Although Figure 1 illustrates a single communications interface 137, the vehicle may include any number of communications interfaces.

[0035] The communication device 132 may be configured to transmit or receive signals over a wired or wireless electronic communication medium 150, such as via a communication interface 137. Although not explicitly shown in FIG. 1, the communication device 132 may be configured to transmit, receive, or both over any wired or wireless communication medium, such as radio frequency (RF), ultraviolet (UV), visible light, optical fiber, wired line, or a combination thereof. Although FIG. 1 shows a single communication device 132 and a single communication interface 137, any number of communication devices and any number of communication interfaces may be used. In some embodiments, the communication device 132 may include a Dedicated Short Range Communications (DSRC) device, an On-Board Vehicle Unit (OBU), or a combination thereof.

[0036] Positioning device 131 may determine geographic information such as the longitude, latitude, altitude, heading, or speed of vehicle 100. For example, positioning device may include a Global Positioning System (GPS) device, such as a Wide Area Augmentation System (WAAS)-enabled National Marine Electronics Association (NMEA) device, radio triangulation device, or a combination thereof. Positioning device 131 may be used to obtain information representing, for example, the current heading of vehicle 100, the current location of vehicle 100 in two or three dimensions, the current angular heading of vehicle 100, or a combination thereof.

[0037] The user interface 135 may include any device capable of interfacing with a person, such as a virtual or physical keypad, a touch pad, a display, a touch display, a heads-up display, a virtual display, an augmented reality display, a tactile display, a feature tracker such as an eye-tracking device, a speaker, a microphone, a video camera, a sensor, a printer, or any combination thereof. The user interface 135 may be operatively coupled to the processor 133 as shown, or to any other element of the controller 130. Although shown as a single device, the user interface 135 may include one or more physical devices. For example, the user interface 135 may include an audio interface for voice communication with the person, and a touch display for visual and touch-based communication with the person. The user interface 135 may include multiple displays, such as multiple physically separate devices, multiple defined portions of a single physical device, or a combination thereof.

[0038] Sensors 136 may include one or more sensors, such as an array of sensors operable to provide information that can be used to control the vehicle. Sensors 136 may provide information regarding the current operating characteristics of vehicle 100. Sensors 136 may include, for example, speed sensors, acceleration sensors, steering angle sensors, traction-related sensors, braking-related sensors, steering wheel position sensors, eye tracking sensors, seating position sensors, or any sensor or combination of sensors operable to report information regarding some aspect of the current dynamic situation of vehicle 100.

[0039] Sensor 136 may include one or more sensors operable to obtain information about the physical environment surrounding vehicle 100. For example, one or more sensors may detect road features and shapes, such as lanes, and obstacles, such as fixed obstacles, vehicles, and pedestrians. Sensor 136 may be or include one or more video cameras, laser sensing systems, infrared sensing systems, acoustic sensing systems, or any other suitable type of on-board environmental sensing device or combination of devices, now known or later developed. In some embodiments, sensor 136 and positioning device 131 may be a combined device.

[0040] Although not separately shown, the vehicle 100 may include a trajectory controller. For example, the controller 130 may include a trajectory controller. The trajectory controller may be operable to obtain information describing the current state of the vehicle 100 and a planned path for the vehicle 100, and to determine and optimize a trajectory for the vehicle 100 based on this information. In some embodiments, the trajectory controller may output signals operable to control the vehicle 100 such that the vehicle 100 follows a trajectory determined by the trajectory controller. For example, the output of the trajectory controller may be an optimized trajectory that may be provided to the powertrain 120, the wheels 140, or both. In some embodiments, the optimized trajectory may be a control input, such as a set of steering angles, each steering angle corresponding to a time or position. In some embodiments, the optimized trajectory may be one or more paths, lines, curves, or combinations thereof.

[0041] One or more of the wheels 140 may be steered wheels that may be pivoted to a steering angle under the control of the steering device 123, propelled wheels that may be given a torque to propel the vehicle 100 under the control of the transmission 122, or steered and propelled wheels that may both steer and propel the vehicle 100.

[0042] The vehicle may include devices or elements not explicitly shown in FIG. 1 , such as an enclosure, a Bluetooth® module, a frequency modulation (FM) radio device, a near field communication (NFC) module, a liquid crystal display (LCD) display device, an organic light emitting diode (OLED) display device, a speaker, or any combination thereof.

[0043] Vehicle 100 may be an autonomous vehicle that is autonomously controlled without direct human intervention to navigate a portion of a transportation network. Although not separately shown in FIG. 1 , the autonomous vehicle may include an autonomous vehicle controller that may perform routing, navigation, and control of the autonomous vehicle. The autonomous vehicle controller may be integrated with other devices of the vehicle. For example, controller 130 may include an autonomous vehicle controller. The teachings herein are equally applicable to semi-autonomous vehicles.

[0044] The autonomous vehicle controller may control or operate the vehicle 100 to move through a portion of the vehicle traffic network according to current vehicle operation parameters. The autonomous vehicle controller may control or operate the vehicle 100 to perform a defined operation or maneuver, such as parking the vehicle. The autonomous vehicle controller may generate a travel route from a starting point, such as the current location of the vehicle 100, to a destination based on vehicle information, environmental information, vehicle traffic network data representing the vehicle traffic network, or a combination thereof, and may control or operate the vehicle 100 to move through the vehicle traffic network according to the route. For example, the autonomous vehicle controller may output the travel route to a trajectory controller, and the trajectory controller may operate the vehicle 100 to move from the starting point to the destination using the generated route.

[0045] Figure 2 is an example diagram of a portion of a vehicle traffic and communication system in which aspects, features, and elements disclosed herein may be implemented. The vehicle traffic and communication system 200 may include one or more vehicles 210 / 211, such as the vehicle 100 shown in Figure 1, which may travel in one or more portions of one or more vehicle traffic networks 220 and may communicate via one or more electronic communication networks 230. Although not explicitly shown in Figure 2, the vehicles may travel in areas not explicitly or completely included in the vehicle traffic network, such as off-road areas.

[0046] Electronic communications network 230 may be, for example, a multiple access system and may provide communications, such as voice communications, data communications, video communications, messaging communications, or combinations thereof, between vehicles 210 / 211 and one or more communications devices 240. For example, vehicles 210 / 211 may receive information, such as information describing vehicle traffic network 220, from communications devices 240 via network 230.

[0047] In some embodiments, the vehicles 210 / 211 may communicate via a wired communication link (not shown), a wireless communication link 231 / 232 / 237, or any combination of any number of wired or wireless communication links. For example, as shown, the vehicles 210 / 211 may communicate via a land-based wireless communication link 231, a non-land-based wireless communication link 232, or a combination thereof. The land-based wireless communication link 231 may include an Ethernet link, a serial link, a Bluetooth link, an infrared (IR) link, a UV link, or any link capable of providing electronic communications.

[0048] Vehicles 210 / 211 may communicate with another vehicle 210 / 2110. For example, a host or target vehicle (HV) 210 may receive one or more autonomous vehicle-to-vehicle messages, such as a basic safety message (BSM), from a remote or target vehicle (RV) 211 via a direct communication link 237 or via a network 230. For example, a remote vehicle 211 may broadcast a message to a host vehicle within a defined broadcast range, such as 300 meters. In some embodiments, the host vehicle 210 may receive the message via a third party, such as a signal repeater (not shown) or another remote vehicle (not shown). Vehicles 210 / 211 may transmit one or more autonomous vehicle-to-vehicle messages periodically based on a defined interval, such as 100 milliseconds.

[0049] The automatic vehicle-to-vehicle messages may include vehicle identification information, geospatial state information such as longitude, latitude or altitude information, geospatial position accuracy information, motion state information such as vehicle acceleration information, yaw rate information, speed information, vehicle heading information, braking system state information, throttle information, steering angle information or vehicle path information, or vehicle operating state information such as vehicle size information, headlight state information, turn signal information, wiper state information, transmission information or any other information or combination of information related to the transmitting vehicle state. For example, the transmission state information may indicate whether the transmission of the transmitting vehicle is in neutral, parked, forward or reverse.

[0050] The vehicle 210 may communicate with the communication network 230 via an access point 233. The access point 233, which may include a computing device, may be configured to communicate with the vehicle 210, the communication network 230, one or more communication devices 240, or combinations thereof, via wireless or wired communication links 231 / 234. For example, the access point 233 may be a base station, a base transceiver station (BTS), a Node-B, an enhanced Node-B (eNode-B), a Home Node-B (HNode-B), a wireless router, a wired router, a hub, a relay, a switch, or any similar wired or wireless device. Although shown as a single device in FIG. 2, the access point may include any number of interconnection elements.

[0051] Vehicle 210 may communicate with communication network 230 via satellite 235 or other non-terrestrial communication device. Satellite 235, which may include a computing device, may be configured to communicate with vehicle 210, communication network 230, one or more communication devices 240, or combinations thereof, via one or more communication links 232 / 236. Although shown as a single device in Figure 2, a satellite may include any number of interconnecting elements.

[0052] Electronic communications network 230 may be any type of network configured to provide voice, data, or any other type of electronic communications. For example, electronic communications network 230 may include a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), a mobile or cellular telephone network, the Internet, or any other electronic communications system. Electronic communications network 230 may use communications protocols such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Internet Protocol (IP), Real-time Transport Protocol (RTP), Hypertext Transport Protocol (HTTP), or combinations thereof. Although shown as a single device in FIG. 2, electronic communications network may include any number of interconnected elements.

[0053] Vehicle 210 may identify a portion or state of vehicle network 220. For example, vehicle 210 may include one or more on-board sensors, such as sensors 136 shown in FIG. 1, which may include a speed sensor, a wheel speed sensor, a camera, a gyroscope, an optical sensor, a laser sensor, a radar sensor, an acoustic sensor, or any other sensor or device or combination thereof capable of determining or identifying a portion or state of vehicle network 220. The sensor data may include lane data, remote vehicle position data, or both.

[0054] Vehicle 210 may navigate one or more portions of one or more vehicle traffic networks 220 using information communicated over network 230, such as information describing traffic network 220, one or more onboard sensors, or a combination thereof.

[0055] For simplicity, Figure 2 shows two vehicles 210, 211, one vehicle traffic network 220, one electronic communication network 230, and one communication network 240, although any number of networks or computer devices may be used. The vehicle traffic and communication system 200 may include devices, units, or elements not shown in Figure 2. Although the vehicle 210 is shown as a single device, the vehicle may include any number of interconnected elements.

[0056] Although vehicle 210 is shown communicating with communication device 240 via network 230, vehicle 210 may communicate with communication device 240 via any number of direct or indirect communication links. For example, vehicle 210 may communicate with communication device 240 via a direct communication link, such as a Bluetooth® communication link.

[0057] In some embodiments, a vehicle 210 / 211 may be associated with an entity 250 / 260, such as a driver, operator, or owner of the vehicle. In some embodiments, an entity 250 / 260 associated with a vehicle 210 / 211 may be associated with one or more personal electronic devices 252 / 254 / 262 / 264, such as a smartphone 252 / 262 or a computer 254 / 264. In some embodiments, a personal electronic device 252 / 254 / 262 / 264 may communicate with a corresponding vehicle 210 / 211 via a direct or indirect communication link. Although one entity 250 / 260 is shown as being associated with each vehicle 210 / 211 in FIG. 2, any number of vehicles may be associated with an entity, and any number of entities may be associated with a vehicle.

[0058] Although vehicular network 220 shows only passable areas (e.g., roads), the vehicular network may include one or more non-passable areas, such as buildings, one or more partially passable areas, such as parking areas or pedestrian walkways, or a combination thereof. Vehicular network 220 may include one or more interchanges between one or more passable or partially passable areas. A portion of vehicular network 220, such as a road, may include one or more lanes and may be associated with one or more directions of travel.

[0059] A vehicle network or a portion thereof may be represented as vehicle network data. For example, the vehicle network data may be represented as a hierarchy of elements, such as markup language elements, which may be stored in a database or file. For simplicity, the figures herein show vehicle network data that represents a portion of a vehicle network as a diagram or map. However, the vehicle network data may be represented in any computer-usable format capable of representing a vehicle network or a portion thereof. The vehicle network data may include heading information, speed limit information, toll booth information, gradient information, such as slope or angle information, surface material information, aesthetic information, defined hazard information, or a combination thereof.

[0060] A portion or combination of portions of vehicle network 220 may be identified as a particular location or destination. For example, vehicle network data may identify a building as a particular location or destination. A particular location or destination may be identified using a distinct, uniquely identifiable geographic location. For example, vehicle network 220 may include a defined location, such as a street address, an address, a vehicle network address, a GPS address, or a combination thereof, for a destination.

[0061] FIG. 3 is a data flow diagram of a technique 300 for generating control signals using vehicle data, according to an embodiment of the disclosure.

[0062] As shown, a connected vehicle (CV) 302 transmits vehicle data 304 to a long-term shared world model 306. Although a single CV 302 is shown, multiple CVs, including the CV 302, may transmit vehicle data 304 to the long-term shared world model 306. The vehicle data 304 may include at least one of position data, speed data, following distance (e.g., following distance) data, and the like. The vehicle data 304 may relate to the CV or other vehicles in proximity to the CV. Although the vehicle data 304 is shown transmitted from the CV 302, all or a portion of the vehicle data may be transmitted from road sensors (e.g., radar, lidar, or computer vision based sensors). The vehicle data 304 is used to generate or modify the long-term shared world model 306. The long-term shared world model 306 may store historical traffic speed, congestion, and / or roadway data for multiple road segments and may be used to predict future traffic speed, congestion, and / or roadway data for all or a portion of the multiple road segments. As shown, a prediction engine 308 accesses the long-term shared world model 306 to predict future traffic speeds, congestion, and / or roadway data for road segments. The predictions are stored in a prediction data structure 310. Control signals 312 are generated based on the prediction data structure 310 and transmitted to a connected and automated vehicle (CAV) 314. The control signals 312 control the speed or travel path of the CAV 314.

[0063] The location data may represent the geographic location of the vehicle. For example, the location data may indicate the latitude and longitude of the vehicle, the vehicle's street address, or another identifier of the geographic location. The location data may be obtained at the vehicle using road sensors, using Global Positioning System (GPS) technology, and / or using cellular tower triangulation. The speed data represents the speed and heading of the vehicle. The speed data may include measurements in units of speed (e.g., kilometers per hour) and direction (e.g., n degrees north, where n is a number). The following distance data represents the distance between the front end of the vehicle and the front end (or rear end) of another vehicle that the vehicle is following. The following distance data may be measured in units of distance (e.g., meters).

[0064] The CV 302 and / or CAV 314 may correspond to one or more of the vehicles 100, 210, 211. The long-term shared world model 306, the prediction engine 308, and / or the prediction data structure 310 may be implemented using software and / or hardware resident on a server connected to the vehicle traffic network 220 and / or the electronic communication network 230. For example, the long-term shared world model 306, the prediction engine 308, and / or the prediction data structure 310 may be implemented using software and / or hardware resident on a communication device 240.

[0065] According to some implementations, the long-term shared world model 306 accesses and stores vehicle data 304 from the CVs 302. The prediction engine 308 uses the long-term shared world model 306 to generate a forecast data structure 310, which represents future predicted speeds on road portions by location and time by applying a traffic flow model to the long-term shared world model 306. The traffic flow model is based on at least one of an average vehicle speed on at least one road segment during a time slice, a vehicle density on at least one road segment during the time slice, or a flow rate or net flow rate of vehicles entering and exiting at least one road segment during the time slice. A control signal 312 is generated based on the forecast data structure 310 and transmitted to the CAV 314 to control its operation (e.g., speed or travel path). The term "time slice" may include, among other things, a continuous period of time, for example, a period from 4:00 PM to 4:02 PM on February 1, 2022.

[0066] 4 is a data flow diagram of a long-term shared world model architecture 400, according to an embodiment of the disclosure. As shown, a vehicle 402 provides vehicle data (e.g., GPS data) to a forwarding engine 404. The forwarding engine 404 forwards the vehicle data 406 to a localization engine 408. The localization engine 408 calculates the vehicle's position, speed, and following distance 410 and provides the position, speed, and following distance 410 to an estimation engine 412. The estimation engine 412 estimates flow rate, density, and speed 414 and provides that data to the forwarding engine 404 and the vehicle 402 to control the vehicle (e.g., by an autonomous engine or a human driver).

[0067] Some implementations relate to reducing traffic congestion. The long-term shared world model 306, 400 may include a traffic state estimator that collects and aggregates data from probe vehicles across the network to generate meaningful macro predictions of traffic conditions and changes. This information is maintained in a database (or other data store) and provided to control policies at a later stage to assist in improved decision-making and coordination by CVs and CAVs.

[0068] The forwarding engine 404 aggregates data from the vehicle 402 (which may be a CV or CAV) into vehicle data 406, and possibly data from other vehicles, into a shared data repository stored on the network and accessible in real time. The forwarding engine 404 may collect GPS, controller area network (CAN), and other available data sources from the vehicle 402 and transmit these data to a server. The forwarding engine 404 also stores data that can be accessed by the vehicle 402. The structure of the available shared repository may be optimized to reduce the overhead associated with storing and accessing individual vehicle data points.

[0069] The localization engine 408 extracts and processes relevant information from the vehicle data 406. The vehicle data 406 may be collected from a shared data repository. The localization engine 408 may perform filtering operations to reduce noise. The information collected by the localization engine 408 for each vehicle i at time t may include the information shown in Table 1. [Table 1] x i (t): Position of vehicle i (relative to a fixed reference point) v i (t): Vehicle i's speed h i (t): Distance (between front bumpers) between vehicle i and the vehicle preceding vehicle i v i,lead (t): The speed of the preceding vehicle in the same lane as vehicle i v L i,lead (t): The speed of the vehicle ahead of vehicle i in the left lane h L i,lead (t): Distance between vehicle i and the vehicle ahead in the left lane v L i,follow (t): The speed of the vehicle following vehicle i in the left lane h L i,follow (t): Distance between vehicle i and the following vehicle in the left lane v R i,lead (t): The speed of the vehicle ahead of vehicle i in the right lane h R i,lead (t): Distance between vehicle i and the vehicle ahead in the right lane v R i,follow (t): The speed of the vehicle following vehicle i in the right lane h R i,follow (t): Distance between vehicle i and the following vehicle in the right lane

[0070] The Estimation Engine 412 collects information stored by the Forwarding Engine 404 and modified by the Localization Engine 408 (e.g., position, speed, and following distance 410) and uses this information to approximate the macroscopic state of traffic on different road segments. Specifically, consider a road section of length L and total time T seconds, where N and M are integers. The Estimation Engine 412 scales time and space into N steps J of length Δt=T / N. j M-space cell I with (0<=j<=N) and length Δx=L / M i (0<=i<=M) and calculate the metrics shown in Table 2 for each time-space domain. [Table 2] v i j : the average speed of a vehicle on road segment i over time interval j ρ i j : vehicle density on road segment i over time interval j q i j : the flow rate of vehicles from road segment i to road segment i+1 over time interval j

[0071] The information calculated by the estimation engine 412, i.e., flow rate, density, and speed 414, is provided to the forwarding engine 404 for storage in a data repository or for use by vehicles (e.g., vehicle 402) in communication with the forwarding engine 404.

[0072] One advantage of the architecture 400 is the flexibility provided by the modularity of the different subcomponents. This allows various features to be generalized to many different vehicles and estimation algorithms in some implementations. For example, if multiple vehicles with different sensing / communication specifications are utilized, the error associated with each vehicle type can be accommodated by utilizing a different localization module for each vehicle type while keeping all other modules fixed. Furthermore, if market penetration effects or other specifications require that a different estimated traffic state estimation algorithm be used, this may simply be substituted without changing anything else.

[0073] The data repository in the forwarding engine 404 may be a database (e.g., SQL, MongoDB, etc.) or other data storage unit. The type of information shared between vehicles (e.g., vehicle 402) accessing the forwarding engine 404 may depend on the sensors available on-board. The localization engine 408 may use different filtering techniques to access and process historical data. The estimation engine 412 may utilize different traffic condition estimators. For example, a statistical or data-driven estimator may be used to generate reasonably granular results when less than a threshold amount of data is available. A model-based estimator (e.g., AI or ML estimator) may be used to generate more granular estimations when more than a threshold amount of data is available.

[0074] A long-term shared world model 306 (or other shared model of traffic conditions) may be used for CVs and CAVs to efficiently cooperate and regulate traffic flow. Traffic state estimation algorithms may provide techniques for defining such models. In some techniques, local vehicle sensor data, i.e. vehicle position, distance, and speed, are converted into quantities that can define a shared state between vehicles in the spatial and temporal domain (e.g., one or more road sections). Some techniques rely on a small amount of data, especially due to limitations in the sensing infrastructure. The rise of vehicle automation has significantly improved the quality of on-board vehicle sensing, producing vehicles that can recognize not only what is in front of the vehicle, but also what is happening in adjacent lanes. In some implementations, procedures may be defined that leverage improvements that are generated in the sensing infrastructure and use these additional observations to improve the quality of existing traffic state estimation methods.

[0075] Some implementations involve reconstructing the Edie flow from the perspective of the probe vehicle. However, the estimators utilized (to reconstruct the Edie flow) may be prone to errors. Errors in the estimation may arise from biases in the sample (probe) vehicle data. In some situations, some vehicles may travel faster or slower (or with a larger or smaller headway) than others, and additional corrections may be used to produce more accurate results. In some implementations, r1 is a speed correction factor and r2 is a time-space domain A corresponding to spatial interval i and time interval j. i j It may be a distance correction factor (for example, for a road portion, a road segment, multiple road portions, or multiple road segments). The relationship between the true traffic state and the estimated traffic state may be as follows:

number

[0076] In the above formula, q i j and q_hat ij are the true and predicted mean flows in spatial interval i and time interval j, respectively. i j and v_hat i j are the true and predicted average velocities in spatial interval i and time interval j, respectively. i j and ρ_hat i j are the true and predicted mean densities in spatial interval i and time interval j, respectively.

[0077] Some implementations involve defining values ​​of r1 and r2. The true values ​​of r1 and r2 may depend on global state information, which may be unknown. To estimate the values ​​of r1 and r2, some implementations rely not only on data from the lane of the probe vehicle, but also on data from lanes adjacent to the lane of the probe vehicle. Some implementations collect speed (e.g., units of distance divided by units of time) and headway (e.g., units of distance) data from adjacent lanes and the probe lane. For example, the data shown in Table 1 may be collected. After the data is collected, r1 and r2 may be calculated as follows:

number

[0078] In the above formula, t i,j n vehicle n is in area A i j is the time step spent in the lane. The addition of 0.5 in the speed correction is designed to balance the influence of each lane on the cumulative estimate. Note that whenever only a subset of the above data is available, values ​​are ignored and the smaller subset is averaged.

[0079] Figure 5 shows a graph of flow rates over space and time. Graph 502 shows the time-space domain and graph 504 shows the data used in the prediction techniques disclosed herein. Figure 6 shows speed and distance values ​​600 measured for a probe vehicle 602.

[0080] This correction can be important in many cases because variations between driving behaviors between lanes can reduce the accuracy of the generated estimate. Examples of situations where such concerns may arise include the presence of trucks (e.g., larger and slower than passenger cars) downstream of one lane, or the presence of high occupancy vehicles (HOVs) or other fast moving lanes not used by the probe vehicle. Recognizing and correcting for the behavior of vehicles in adjacent lanes can reduce the impact of lane-level features on the returned estimate in some implementations.

[0081] In some implementations, the correction coefficients r1 and r2 may be calculated from local real-time sensor data. Some implementations include a Bayesian prior that models the distribution of lane-level dynamics to improve accuracy in the presence of sparse data. In some implementations, the flow and density corrections are expressed as a function of the change in average speed. This is relevant when either the leading or trailing vehicle in the adjacent lane is missing, thereby providing a quantification of the distance between vehicles. For example, using a triangular base diagram to define the form of the flow-density relationship and calibrating the model of this relationship can help generate approximations for the correction coefficients r1 and r2.

[0082] FIG. 7 is a data flow diagram of traffic state determination 700 according to an embodiment of the present disclosure. As shown in FIG. 7, CV 702 (e.g., corresponding to CV 302) provides speed data 704 and following distance data 706 (e.g., all or part of the speed data and following distance data listed in Table 1) to vehicle control server 708. Vehicle control server 708 calculates speed correction factor 710 (r1) and following distance correction factor 712 (r2). Prediction engine 714 (e.g., corresponding to prediction engine 308) accesses speed data 704 and following distance data 706 and predicts (e.g., using artificial intelligence, machine learning, and / or mathematical formulas) future flow rate 716, future average speed 718, and future density 720 for at least one road segment or road portion. The predicted future flow rate 716, future average speed 718, and future density 720, along with the calculated speed correction factor 710 and following distance correction factor 712, are provided to an adjustment engine 722 of the vehicle control server 708. The adjustment engine adjusts the predicted future flow rate 716, future average speed 718, and future density 720 and provides an output to a control signal generator 724 based on the adjustment. The control signal generator 724 generates a control signal 726 for controlling a CAV 728 (e.g., corresponding to CAV 314) and provides the control signal 726 to the CAV 728. The CAV 728 adjusts operation of the CAV 728 (e.g., speed or path of travel) in response to the control signal 726.

[0083] Traffic congestion increases fuel consumption and greenhouse gas emissions and reduces driving comfort and driving safety. Techniques that use connected and / or automated vehicle technologies to reduce traffic congestion may be desirable.

[0084] In some implementations, some of the vehicles are connected vehicles (vehicles that can share information with the cloud but are manually driven by a human driver), some of the vehicles are connected and autonomous vehicles (vehicles that may share information and are automatically controlled by the cloud), and some of the vehicles are human-driven vehicles that are not connected or automated (no connection to the cloud). CVs and CAVs may share traffic-related measurements from on-board sensors with the cloud, including vehicle speed, acceleration, relative distance and relative velocity, with surrounding vehicles, etc.

[0085] FIG. 8 illustrates an example of a vehicle communication and control architecture 800. As illustrated, the vehicle communication and control architecture 800 includes a non-connected vehicle 802, a CV 804, a CAV 806, and a roadside unit (RSU) 808 in a road portion 810. The non-connected vehicle 802 may be a legacy vehicle operated by a human driver and may not be able to transmit traffic state information (e.g., speed information, driving path information, or information obtained by an on-board sensor) to a network. The RSU 808 may include a router for sending and receiving network communications between the CV 804 and the CAV 806. The RSU 808 may include road sensors (e.g., a camera system, a radar system, or a lidar system). As illustrated, the CV 804, the CAV 806, and the RSU 808 communicate state and perception data 812 to generate a long-term shared world model 814. The long-term shared world model 814 may correspond to the long-term shared world model 306. As shown, the long-term shared world model 814 is accessed by a cooperative multi-vehicle policy selector 816, which sends control signals 818 to the CAV 806. The control signals 818 are adjusted and optimized to achieve traffic control objectives, such as reducing traffic inflow 820 or reducing stop-go traffic 822 on various portions of the road portion 810. As shown, the long-term shared world model 814 and the cooperative multi-vehicle policy selector 816 are stored on a network 824, such as a server or data repository connected to the network 824.

[0086] According to some implementations, via wireless communication, engines of the network 824 (e.g., the long-term shared world model 814 or the cooperative multi-vehicle policy selector 816) send and receive information to and from the CV 804 and / or CAV 806. Once state and perception data 812 from the CV 804 and / or CAV 806 is processed, control signals 818 are sent to the CAV 806.

[0087] The long-term shared world model 814 is a real-time, accurate traffic condition model based on state and perception data 812 from the CVs 804 and CAVs 806. The long-term shared world model 814 is used to provide the cooperative multi-vehicle policy selector 816 with the necessary information to generate control signals 818 for the CAVs 806. Data from the long-term shared world model 814 may be used in two ways: to share traffic conditions directly with the CAVs 806 to allow each CAV 806 to adapt to traffic conditions using its on-board computer, or to have the cooperative multi-vehicle policy selector 816 determine optimal actions for the CAVs 806 based on macroscopic metrics (e.g., network throughput, traffic speed variance, greenhouse gas emissions, etc.). The optimal actions for each CAV 806 are communicated from the cooperative multi-vehicle policy selector 816 to the associated CAVs 806 using control signals 818. By adjusting the driving behavior of the CAVs 806, traffic flow may be more effectively controlled and optimized, traffic inflow in certain areas of the road may be reduced, and / or stop-go traffic may be reduced.

[0088] 9 is a data flow diagram for vehicle communication and control 900 according to an embodiment of the disclosure. As shown, a CV 902 generates traffic state information 904 and provides the traffic state information 904 to a long-term shared world model 906. The CV 902 may correspond to one of the CVs 804 or one of the CAVs 806. The traffic state information 904 may correspond to state and perception data 812, or other information generated by the CV 902 or available to the CV. The long-term shared world model 906 may correspond to at least one of the long-term shared world models 306, 814.

[0089] As shown, the long-term shared world model 906 communicates data to a multi-vehicle policy selector 908 (e.g., a cooperative multi-vehicle policy selector 816). The multi-vehicle policy selector 908 utilizes an optimization engine 910 to optimize traffic conditions (e.g., reduce traffic inflow into high traffic areas and / or reduce stop-go traffic) to generate a control signal 912 (e.g., from among a plurality of control signals 818) for a CAV 914 (e.g., one of the plurality of CAVs 806). The multi-vehicle policy selector 908 transmits the control signal 912 to the CAV 914. The CAV 914 adjusts its operation (e.g., its speed or its route of travel) based on the control signal 912.

[0090] 10 is a flowchart of a method 1000 of operating a vehicle using a long-term shared world model, according to an embodiment of the disclosure. The method 1000 may be implemented using a server. The server may include a server connected to the vehicle traffic network 220 and / or the electronic communication network 230. The server may include a single computing device or multiple computing devices operating together.

[0091] In block 1002, the server accesses vehicle data from each CV of a subset of the plurality of CVs in the road portion. The vehicle data may include at least one of position, speed, or following distance. The server may access additional data from road sensors. The additional data may include vehicle position data, vehicle speed data, and / or vehicle following distance data. The additional data may be sensed using computer vision, radar, and / or lidar at the road sensors. The vehicle data may be associated with the CV itself or with another vehicle (e.g., another CV or a non-connected vehicle) in proximity to the CV whose data is accessible by the CV's on-board sensors. The additional data may be associated with either the CV or the non-connected vehicle.

[0092] In block 1004, the server generates a long-term shared world model (e.g., one of the long-term shared world models 306, 814, 906) based on the accessed vehicle data and / or additional data. In some cases, the accessed vehicle data is received from a first vehicle and includes a position, speed, or distance of a second vehicle. The first vehicle is different from the second vehicle. For example, the second vehicle may be traveling immediately before the first vehicle, immediately after the first vehicle, or in a travel lane adjacent to the travel lane of the first vehicle.

[0093] In block 1006, the server generates a data structure representing future predicted speeds for road portions by location and time using the long-term shared world model by applying the traffic flow model to the long-term shared world model. For example, the data structure may be a two-dimensional array with one dimension representing road segments and another dimension representing time slices. The value in each cell of the two-dimensional array may represent the future predicted speed for the corresponding road segment and time slice.

[0094] According to some implementations, the road portion is subdivided into a plurality of road segments. The traffic flow model is based on at least one of an average vehicle speed on at least one road segment during a time slice, a vehicle density on at least one road segment during the time slice, or a flow rate or net flow rate of vehicles entering or exiting at least one road segment during the time slice. The average vehicle speed, vehicle density, flow rate, or net flow rate may be calculated using a long-term shared world model.

[0095] In block 1008, the server sends a control signal to the CAV to control operation of the CAV based on the generated data structure. The CAV may adjust its operation based on the control signal. For example, the CAV may adjust its speed or its path of travel based on the control signal.

[0096] 11 is a flowchart of a method 1100 for predicting future traffic data according to an embodiment of the present disclosure. The server may include the vehicle control server 708, or a server connected to the vehicle traffic network 220 and / or the electronic communication network 230. The server may include a single computing device or multiple computing devices operating together.

[0097] In block 1102, the server receives speed data from the CV representing the speeds of the CV and additional vehicles proximate to the CV. The server calculates a speed correction factor based on the speed data, for example, using Equation 4 above. The additional vehicles proximate to the CAV may include vehicles ahead of the CV in a lane adjacent to the CV, or vehicles behind the CV in a lane adjacent to the CV.

[0098] In block 1104, the server receives distance data from the CV representing distances between the CV and additional vehicles proximate to the CV. The server calculates a distance correction factor based on the distance data, for example, using Equation 5 above. In some embodiments, the server receives speed data and distance data from multiple CVs including the CV. The speed correction factor is calculated based on the speed data from the multiple CVs. The distance correction factor is calculated based on the distance data from the multiple CVs.

[0099] In block 1106, the server uses the server's forecasting engine to determine the future flow rate, future average speed, and future density for the road segment. In some implementations, the forecasting engine utilizes a long-term shared world model (e.g., one of the long-term shared world models 306, 814, 906) that stores data received from multiple CVs and multiple road sensors.

[0100] At block 1108, the server adjusts the determined future flow rate based on a speed correction factor and a following distance correction factor. The server adjusts the determined future average speed based on the speed correction factor. The server adjusts the determined future density based on the following distance correction factor. Adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor may include multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor. Adjusting the determined future average speed based on the speed correction factor may include multiplying the determined future average speed by the speed correction factor. Adjusting the determined future density based on the following distance correction factor may include dividing the determined future density by the following distance correction factor.

[0101] According to some implementations, the server generates a control signal for the CAV based on at least one of the determined future flow rate, the determined future average speed, or the determined future density. The server transmits the generated control signal to the CAV.

[0102] 12 is a flowchart of a method 1200 for controlling vehicles using a multi-vehicle policy selector according to an embodiment of the disclosure. The method 1200 may be implemented using a server. The server may include a server connected to the vehicle traffic network 220 and / or the electronic communication network 230. The server may include a single computing device or multiple computing devices operating together.

[0103] In block 1202, the server receives traffic condition information from the roadway sensors, CVs, and CAVs. The traffic condition information may include at least one of the following: a position of the vehicle on the roadway portion, a speed of the vehicle, a following distance between the vehicle and other vehicles, and a number of vehicles. The vehicle may be a non-connected vehicle, a CV, or a CAV. In the case of a non-connected vehicle, the traffic condition information may be determined (and transmitted to the server) by at least one of the roadway sensors, the CV, or a CAV proximate to the non-connected vehicle. The CV, CAV, and / or non-connected vehicle may be traveling on the roadway portion. Alternatively, the CV, CAV, and / or non-connected vehicle may include vehicles that are not traveling on the roadway portion (e.g., vehicles approaching the roadway portion or vehicles that have recently left the roadway portion).

[0104] In block 1204, the server generates a long-term shared world model (e.g., one of the long-term shared world models 306, 814, 906) based on the received traffic condition information. The long-term shared world model may use any modeling technique. For example, the long-term shared world model may use statistical modeling, machine learning, or artificial intelligence techniques.

[0105] In block 1206, the server uses a multi-vehicle policy selector (e.g., multi-vehicle policy selector 908) to generate control signals for controlling the CAVs based on the long-term shared world model. The multi-vehicle policy selector generates the control signals using an optimization to reduce congestion on the road portion. Each control signal is sent to an associated CAV to control the speed or travel path of the associated CAV. The optimization to reduce congestion on the road includes ensuring at least a minimum threshold following distance between each CAV and a vehicle in front of the CAV.

[0106] Some implementations are described below as numbered examples (Examples 1, 2, 3, etc.) which are provided as examples only and are not limiting of other implementations disclosed herein.

[0107] Example 1 is a method for generating and transmitting control signals to vehicles, the method including: accessing vehicle data from each connected vehicle of a subset of a plurality of connected vehicles (CVs) on a road section, the vehicle data including a position, a speed, or a vehicle-to-vehicle distance; generating a long-term shared world model based on the accessed vehicle data; generating a data structure representing future predicted speeds on the road section by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model; Transmitting a control signal to a connected and automated vehicle (CAV) based on the generated data structure for controlling operation of the connected and automated vehicle.

[0108] In Example 2, the subject matter of Example 1 includes accessing additional data from road sensors, the additional data including at least one of vehicle position data, vehicle speed data, or vehicle distance data, and storing the accessed additional data in the long-term shared world model.

[0109] In Example 3, the subject matter of Examples 1-2 includes where the vehicle position data, the vehicle speed data, or the vehicle distance data is associated with a non-connected vehicle.

[0110] In Example 4, the subject matter of Examples 1-3 includes that the road portion is subdivided into a plurality of road segments, and the traffic flow model is based on at least one of an average vehicle speed on at least one road segment during a time slice, a vehicle density on at least one road segment during the time slice, or a flow rate or net flow rate of vehicles entering or exiting at least one road segment during the time slice.

[0111] In example 5, the subject matter of example 4 includes wherein the average vehicle speed, the vehicle density, the flow rate, or the net flow rate is calculated using the long-term shared world model.

[0112] In Example 6, the subject matter of Examples 1-5 includes the accessed vehicle data being received from a first vehicle and including a position, speed, or distance of a second vehicle, and the first vehicle is different from the second vehicle.

[0113] In Example 7, the subject matter of Example 6 includes the second vehicle traveling immediately before the first vehicle, immediately after the first vehicle, or in a lane adjacent to the lane of the first vehicle.

[0114] Example 8 is an apparatus for generating and sending a control signal to a vehicle, the apparatus comprising a memory that stores instructions and a processor that executes the stored instructions, the stored instructions including: accessing vehicle data from each connected vehicle of a subset of a plurality of connected vehicles (CVs) on a road portion, the vehicle data including at least one of position, speed, or vehicle distance; generating a long-term shared world model based on the accessed vehicle data; generating a data structure representing future predicted speeds on the road portion by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model; and sending a control signal to a connected autonomous vehicle (CAV) for controlling operation of the connected autonomous vehicle based on the generated data structure.

[0115] In Example 9, the subject matter of Example 8 includes executing stored instructions by the processor to access additional data from road sensors, the additional data including at least one of vehicle position data, vehicle speed data, or vehicle distance data, and storing the accessed additional data in the long-term shared world model.

[0116] In example 10, the subject matter of examples 8-9 includes where the vehicle position data, the vehicle speed data, or the vehicle distance data is associated with a non-connected vehicle.

[0117] In Example 11, the subject matter of Examples 8-10 includes that the road portion is subdivided into a plurality of road segments, and the traffic flow model is based on at least one of an average vehicle speed on at least one road segment during a time slice, a vehicle density on at least one road segment during the time slice, or a flow rate or net flow rate of vehicles entering or exiting at least one road segment during the time slice.

[0118] In example 12, the subject matter of example 11 includes wherein the average vehicle speed, the vehicle density, the flow rate, or the net flow rate is calculated using the long-term shared world model.

[0119] In Example 13, the subject matter of Examples 8-12 includes the accessed vehicle data being received from a first vehicle and including a position, speed, or distance of a second vehicle, and the first vehicle is different from the second vehicle.

[0120] In Example 14, the subject matter of Example 13 includes the second vehicle traveling in a lane immediately before the first vehicle, immediately after the first vehicle, or adjacent to the lane of travel of the first vehicle.

[0121] Example 15 is a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations including: accessing vehicle data from each connected vehicle of a subset of a plurality of connected vehicles (CVs) on a road portion, the vehicle data including position, speed, or vehicle distance; generating a long-term shared world model based on the accessed vehicle data; generating a data structure representing future predicted speeds on the road portion by position and time using the long-term shared world model by applying a traffic flow model to the long-term shared world model; and transmitting a control signal to a connected autonomous vehicle (CAV) for controlling operation of the connected autonomous vehicle based on the generated data structure.

[0122] In Example 16, the subject matter of Example 15 further includes accessing additional data from road sensors, the additional data including at least one of vehicle position data, vehicle speed data, or vehicle distance data, and storing the accessed additional data in the long-term shared world model.

[0123] In Example 17, the subject matter of Examples 15-16 includes where the vehicle position data, the vehicle speed data, or the vehicle distance data is associated with a non-connected vehicle.

[0124] In Example 18, the subject matter of Examples 15-17 includes the road portion being subdivided into a plurality of road segments, and the traffic flow model being based on at least one of an average vehicle speed on at least one road segment during a time slice, a vehicle density on at least one road segment during the time slice, or a flow rate or net flow rate of vehicles entering or exiting at least one road segment during the time slice.

[0125] In example 19, the subject matter of example 18 includes wherein the average vehicle speed, the vehicle density, the flow rate, or the net flow rate is calculated using the long-term shared world model.

[0126] In Example 20, the subject matter of Examples 15-19 includes the accessed vehicle data being received from a first vehicle and including a position, speed, or distance of a second vehicle, and the first vehicle is different from the second vehicle.

[0127] In Example 21, the method includes receiving, at a control server and from a connected vehicle (CV), speed data representing speeds of the connected vehicle and additional vehicles in proximity to the connected vehicle, calculating a speed correction factor based on the speed data, receiving, at the control server and from the connected vehicle, vehicle-to-vehicle distance data representing vehicle-to-vehicle distances between the connected vehicle and additional vehicles in proximity to the connected vehicle, calculating a vehicle-to-vehicle distance correction factor based on the vehicle-to-vehicle distance data, and using a prediction engine in the control server to predict future flow rates, future traffic volumes, and future traffic volumes for a road segment. adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor; adjusting the determined future average speed based on the speed correction factor; adjusting the determined future density based on the following distance correction factor; generating, at the control server, a control signal for a connected autonomous vehicle (CAV) based on at least one of the determined future flow rate, the determined future average speed, or the determined future density; and transmitting the generated control signal to the connected autonomous vehicle.

[0128] In Example 22, the subject matter of Example 21 includes the additional vehicles proximate to the connected vehicle including a vehicle in front of the connected vehicle in a lane adjacent to the connected vehicle, or a vehicle behind the connected vehicle in a lane adjacent to the connected vehicle.

[0129] In Example 23, the subject matter of Examples 21 to 22 includes that the control server receives the speed data and the vehicle-to-vehicle distance data from a plurality of connected vehicles including the connected vehicle, the speed correction coefficient is calculated based on the speed data from the plurality of connected vehicles, and the vehicle-to-vehicle distance correction coefficient is calculated based on the vehicle-to-vehicle distance data from the plurality of connected vehicles.

[0130] In example 24, the subject matter of examples 21-23 includes the prediction engine utilizing a long-term shared world model that stores data received from the plurality of connected vehicles and a plurality of road sensors.

[0131] In Example 25, the subject matter of Examples 21 to 24 includes that adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor includes multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor.

[0132] In Example 26, the subject matter of Examples 21-25 is such that adjusting the determined future average speed based on the speed correction factor includes multiplying the determined future average speed by the speed correction factor.

[0133] In Example 27, the subject matter of Examples 21 to 26 is such that adjusting the determined future density based on the following distance correction factor includes dividing the determined future density by the following distance correction factor.

[0134] In Example 28, the apparatus includes a memory that stores instructions and a processor that executes the stored instructions, the stored instructions include steps of: receiving, at a control server and from a connected vehicle (CV), speed data representing speeds of the connected vehicle and an additional vehicle proximate to the connected vehicle; calculating a speed correction factor based on the speed data; receiving, at the control server and from the connected vehicle, vehicle-to-vehicle distance data representing vehicle-to-vehicle distances of the connected vehicle and an additional vehicle proximate to the connected vehicle; calculating a vehicle-to-vehicle distance correction factor based on the vehicle-to-vehicle distance data; and using a prediction engine in the control server. determining a future flow rate, a future average speed, and a future density for a road segment; adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor; adjusting the determined future average speed based on the speed correction factor; adjusting the determined future density based on the following distance correction factor; generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of the determined future flow rate, the determined future average speed, or the determined future density; and transmitting the generated control signal to the connected automated vehicle.

[0135] In Example 29, the subject matter of Example 28 includes the additional vehicles proximate to the connected vehicle including a vehicle in front of the connected vehicle in a lane adjacent to the connected vehicle, or a vehicle behind the connected vehicle in a lane adjacent to the connected vehicle.

[0136] In Example 30, the subject matter of Examples 28 to 29 includes that the control server receives the speed data and the vehicle-to-vehicle distance data from a plurality of connected vehicles including the connected vehicle, the speed correction coefficient is calculated based on the speed data from the plurality of connected vehicles, and the vehicle-to-vehicle distance correction coefficient is calculated based on the vehicle-to-vehicle distance data from the plurality of connected vehicles.

[0137] In example 31, the subject matter of examples 28-30 includes the prediction engine utilizing a long-term shared world model that stores data received from the plurality of connected vehicles and a plurality of road sensors.

[0138] In Example 32, the subject matter of Examples 28 to 31 includes that adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor includes multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor.

[0139] In Example 33, the subject matter of Examples 28-32 is such that adjusting the determined future average speed based on the speed correction factor includes multiplying the determined future average speed by the speed correction factor.

[0140] In Example 34, the subject matter of Examples 28 to 33 is such that adjusting the determined future density based on the following distance correction factor includes dividing the determined future density by the following distance correction factor.

[0141] Example 35 is a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations, the operations including receiving, at a control server and from a connected vehicle (CV), speed data representing speeds of the connected vehicle and an additional vehicle proximate to the connected vehicle, calculating a speed correction factor based on the speed data, receiving, at the control server and from the connected vehicle, vehicle-to-vehicle distance data representing vehicle-to-vehicle distances of the connected vehicle and an additional vehicle proximate to the connected vehicle, calculating a vehicle-to-vehicle distance correction factor based on the vehicle-to-vehicle distance data, and calculating a prediction error correction factor based on the vehicle-to-vehicle distance correction factor. determining a future flow rate, a future average speed, and a future density for a segment of a road using a control server; adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor; adjusting the determined future average speed based on the speed correction factor; adjusting the determined future density based on the following distance correction factor; generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of the determined future flow rate, the determined future average speed, or the determined future density; and transmitting the generated control signal to the connected automated vehicle.

[0142] In Example 36, the subject matter of Example 35 includes the additional vehicles proximate to the connected vehicle including a vehicle in front of the connected vehicle in a lane adjacent to the connected vehicle, or a vehicle behind the connected vehicle in a lane adjacent to the connected vehicle.

[0143] In Example 37, the subject matter of Examples 35 to 36 includes that the control server receives the speed data and the vehicle-to-vehicle distance data from a plurality of connected vehicles including the connected vehicle, the speed correction coefficient is calculated based on the speed data from the plurality of connected vehicles, and the vehicle-to-vehicle distance correction coefficient is calculated based on the vehicle-to-vehicle distance data from the plurality of connected vehicles.

[0144] In example 38, the subject matter of examples 35-37 includes the prediction engine utilizing a long-term shared world model that stores data received from the plurality of connected vehicles and a plurality of road sensors.

[0145] In Example 39, the subject matter of Examples 35 to 38 includes that adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor includes multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor.

[0146] In Example 40, the subject matter of Examples 35-39 is such that adjusting the determined future average speed based on the speed correction factor includes multiplying the determined future average speed by the speed correction factor.

[0147] In Example 41, a method includes receiving traffic condition information from road sensors, a connected vehicle (CV) and a connected autonomous vehicle (CAV), generating a long-term shared world model based on the received traffic condition information, and generating control signals for controlling the connected autonomous vehicles based on the long-term shared world model using a multi-vehicle policy selector, wherein the multi-vehicle policy selector generates the control signals using optimization to reduce congestion on a road portion, and each control signal controls a speed or travel path of an associated CAV.

[0148] In example 42, the subject matter of example 41 includes, wherein optimizing to reduce congestion on the road includes ensuring at least a minimum threshold following distance between each connected autonomous vehicle and a vehicle in front of the connected autonomous vehicle.

[0149] In Example 43, the subject matter of Examples 41-42 includes that the traffic condition information includes at least one of a position of the vehicle on the road portion, a speed of the vehicle, a following distance between the vehicle and other vehicles, and a number of vehicles.

[0150] In example 44, the subject matter of example 43 includes, wherein the vehicle includes a CV or a CAV.

[0151] In Example 45, the subject matter of Examples 43-44 includes the vehicle including a non-connected vehicle, and the traffic condition information for the non-connected vehicle is determined using one or more road sensors.

[0152] In Example 46, the subject matter of Examples 43-45 includes the vehicle including a non-connected vehicle, and the traffic condition information for the non-connected vehicle is determined using at least one of the connected vehicle or the connected autonomous vehicle.

[0153] In Example 47, the subject matter of Examples 41 to 46 includes the connected vehicle, the connected autonomous vehicle, and the non-connected vehicle traveling on the road portion.

[0154] In Example 48, the subject matter of Examples 41-47 includes where the connected vehicles or the connected autonomous vehicles include at least one vehicle that is not on the road portion.

[0155] Example 49 is an apparatus comprising a memory that stores instructions and a processor that executes the stored instructions, the stored instructions including receiving traffic condition information from road sensors, a connected vehicle (CV) and a connected autonomous vehicle (CAV), generating a long-term shared world model based on the received traffic condition information, and generating control signals for controlling the connected autonomous vehicles based on the long-term shared world model using a multi-vehicle policy selector, wherein the multi-vehicle policy selector generates the control signals using optimization to reduce congestion on a road portion, and each control signal controls a speed or travel path of an associated CAV.

[0156] In Example 50, the subject matter of Example 49 includes, wherein optimizing to reduce congestion on the road includes ensuring at least a minimum threshold following distance between each connected autonomous vehicle and a vehicle in front of the connected autonomous vehicle.

[0157] In Example 51, the subject matter of Examples 49-50 includes that the traffic condition information includes at least one of a position of the vehicle on the road portion, a speed of the vehicle, a following distance between the vehicle and other vehicles, and a number of vehicles.

[0158] In example 52, the subject matter of example 51 includes, wherein the vehicle includes a CV or a CAV.

[0159] In Example 53, the subject matter of Examples 51-52 includes the vehicle including a non-connected vehicle, and the traffic condition information for the non-connected vehicle is determined using one or more road sensors.

[0160] In Example 54, the subject matter of Examples 51 to 53 includes the vehicle including a non-connected vehicle, and the traffic condition information for the non-connected vehicle is determined using at least one of the connected vehicle or the connected autonomous vehicle.

[0161] In Example 55, the subject matter of Examples 49 to 54 includes the connected vehicle, the connected autonomous vehicle, and the non-connected vehicle traveling on the road portion.

[0162] In Example 56, the subject matter of Examples 49 to 55 includes the connected vehicles or the connected autonomous vehicles including at least one vehicle that is not on the road portion.

[0163] Example 57 is a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations including receiving traffic condition information from road sensors, a connected vehicle (CV), and a connected autonomous vehicle (CAV), generating a long-term shared world model based on the received traffic condition information, and generating control signals for controlling the connected autonomous vehicles based on the long-term shared world model using a multi-vehicle policy selector, wherein the multi-vehicle policy selector generates the control signals using optimization to reduce congestion on a road portion, and each control signal controls a speed or travel path of an associated CAV.

[0164] In example 58, the subject matter of example 57 includes, wherein optimizing to reduce congestion on the road includes ensuring at least a minimum threshold following distance between each connected autonomous vehicle and a vehicle in front of the connected autonomous vehicle.

[0165] In Example 59, the subject matter of Examples 57-58 includes that the traffic condition information includes at least one of a position of the vehicle on the road portion, a speed of the vehicle, a following distance between the vehicle and other vehicles, and a number of vehicles.

[0166] In example 60, the subject matter of example 59 includes, wherein the vehicle includes a CV or a CAV.

[0167] Example 61 is at least one machine-readable medium comprising instructions that, when executed by a processing circuit, cause the processing circuit to perform operations to implement any of Examples 1 to 60.

[0168] Example 62 is an apparatus including means for implementing any of Examples 1 to 60.

[0169] Example 63 is a system for implementing any one of Examples 1 to 60.

[0170] Example 64 is a method for implementing any of Examples 1 to 60.

[0171] As used herein, the term "instructions" may include instructions or expressions for performing any method disclosed herein, or any part or parts thereof, and may be implemented in hardware, software, or any combination thereof. For example, instructions may be implemented as information, such as a computer program stored in a memory, that may be executed by a processor to perform any of the methods, algorithms, aspects, or combinations thereof described herein. Instructions or portions thereof may be implemented as a dedicated processor or circuitry, which may include dedicated hardware for performing any of the methods, algorithms, aspects, or combinations thereof described herein. In some implementations, portions of instructions may be distributed across multiple devices or multiple processors on a single device, which may communicate directly or over a network, such as a local area network, a wide area network, the Internet, or combinations thereof.

[0172] As used herein, the terms "example," "embodiment," "implementation," "aspect," "feature," or "element" indicate serving as an example, example, or illustration. Unless expressly stated, any example, embodiment, implementation, aspect, feature, or element is independent of each other example, embodiment, implementation, aspect, feature, or element and may be used in combination with any other example, embodiment, implementation, aspect, feature, or element.

[0173] As used herein, the terms "determining" and "identifying" or any variation thereof includes selecting, ascertaining, calculating, retrieving, receiving, determining, establishing, obtaining, or otherwise identifying or determining in any manner using one or more of the devices shown and described herein.

[0174] As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or" unless otherwise specified or apparent from the context. Furthermore, the articles "a" and "an" as used in this application and the appended claims should generally be construed to mean "one or more" unless the context clearly indicates a singular reference or unless otherwise specified.

[0175] Furthermore, although for simplicity of description, the drawings and descriptions herein may include a series of steps or stages or sequences, elements of the methods disclosed herein may occur in various orders or simultaneously. Moreover, elements of the methods disclosed herein may occur with other elements not explicitly shown and disclosed herein. Moreover, not all elements of the methods described herein are required to implement a method according to the present disclosure. Although aspects, features, and elements are described herein in certain combinations, each aspect, feature, or element may be used independently or in various combinations with or without other aspects, features, and elements.

[0176] The above aspects, examples and implementations are described to facilitate understanding of the present disclosure and are not intended to be limiting. To the contrary, the present disclosure encompasses various modifications and equivalent arrangements that are within the scope of the appended claims, which should be interpreted broadest possible to encompass all such modifications and equivalent arrangements as are permitted by law.

Claims

1. receiving, at a control server and from a connected vehicle (CV), speed data representative of a speed of the connected vehicle and an additional vehicle in proximity to the connected vehicle; calculating a speed correction factor based on the speed data; receiving, at the control server and from the connected vehicle, distance data representative of a distance between the connected vehicle and the additional vehicle proximate to the connected vehicle; calculating a vehicle distance correction coefficient based on the vehicle distance data; determining a future flow rate, a future average speed, and a future density for a segment of a road using a forecasting engine in the control server; adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor; adjusting the determined future average speed based on the speed correction factor; adjusting the determined future density based on the vehicle distance correction factor; generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of the determined future flow rate, the determined future average speed, or the determined future density; and Transmitting the generated control signal to the connected and autonomous vehicle. A method comprising:

2. 2. The method of claim 1 , wherein the additional vehicles proximate to the connected vehicle include a vehicle ahead of the connected vehicle in a lane adjacent to the connected vehicle, or a vehicle behind the connected vehicle in a lane adjacent to the connected vehicle.

3. The control server receives the speed data and the vehicle distance data from a plurality of connected vehicles including the connected vehicle, the speed correction factor is calculated based on the speed data from the plurality of connected vehicles; The method of claim 1 , wherein the following distance correction factor is calculated based on the following distance data from the plurality of connected vehicles.

4. The method of claim 1 , wherein the prediction engine utilizes a long-term shared world model that stores data received from a plurality of connected vehicles and a plurality of road sensors.

5. Adjusting the determined future flow rate based on the speed correction factor and the vehicle distance correction factor multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor; The method of claim 1 , comprising:

6. Adjusting the determined future average speed based on the speed correction factor multiplying said determined future average speed by said speed correction factor; The method of claim 1 , comprising:

7. Adjusting the determined future density based on the vehicle distance correction factor Dividing the determined future density by the following distance correction factor. The method of claim 1 , comprising:

8. A memory for storing instructions; a processor for executing the stored instructions; Equipped with The stored instructions include: receiving, at a control server and from a connected vehicle (CV), speed data representative of a speed of the connected vehicle and an additional vehicle in proximity to the connected vehicle; calculating a speed correction factor based on the speed data; receiving, at the control server and from the connected vehicle, distance data representative of a distance between the connected vehicle and the additional vehicle proximate to the connected vehicle; calculating a vehicle distance correction coefficient based on the vehicle distance data; determining a future flow rate, a future average speed, and a future density for a segment of a road using a forecasting engine in the control server; adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor; adjusting the determined future average speed based on the speed correction factor; adjusting the determined future density based on the vehicle distance correction factor; generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of the determined future flow rate, the determined future average speed, or the determined future density; and Transmitting the generated control signal to the connected and autonomous vehicle. An apparatus for performing the above.

9. 10. The apparatus of claim 8, wherein the additional vehicles proximate to the connected vehicle include a vehicle ahead of the connected vehicle in a lane adjacent to the connected vehicle, or a vehicle behind the connected vehicle in a lane adjacent to the connected vehicle.

10. The control server receives the speed data and the vehicle distance data from a plurality of connected vehicles including the connected vehicle, the speed correction factor is calculated based on the speed data from the plurality of connected vehicles; The apparatus of claim 8 , wherein the following distance correction factor is calculated based on the following distance data from the plurality of connected vehicles.

11. The apparatus of claim 8 , wherein the prediction engine utilizes a long-term shared world model that stores data received from a plurality of connected vehicles and a plurality of road sensors.

12. Adjusting the determined future flow rate based on the speed correction factor and the vehicle distance correction factor multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor; The apparatus of claim 8 , comprising:

13. Adjusting the determined future average speed based on the speed correction factor multiplying said determined future average speed by said speed correction factor; The apparatus of claim 8 , comprising:

14. Adjusting the determined future density based on the vehicle distance correction factor Dividing the determined future density by the following distance correction factor. The apparatus of claim 8 , comprising:

15. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations, the operations including: receiving, at a control server and from a connected vehicle (CV), speed data representative of a speed of the connected vehicle and an additional vehicle in proximity to the connected vehicle; calculating a speed correction factor based on the speed data; receiving, at the control server and from the connected vehicle, distance data representative of a distance between the connected vehicle and the additional vehicle proximate to the connected vehicle; calculating a vehicle distance correction coefficient based on the vehicle distance data; determining a future flow rate, a future average speed, and a future density for a segment of a road using a forecasting engine in the control server; adjusting the determined future flow rate based on the speed correction factor and the following distance correction factor; adjusting the determined future average speed based on the speed correction factor; adjusting the determined future density based on the vehicle distance correction factor; generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of the determined future flow rate, the determined future average speed, or the determined future density; and Transmitting the generated control signal to the connected and autonomous vehicle. A non-transitory computer readable medium comprising:

16. 16. The computer-readable medium of claim 15, wherein the additional vehicles proximate to the connected vehicle include a vehicle in front of the connected vehicle in a lane adjacent to the connected vehicle, or a vehicle behind the connected vehicle in a lane adjacent to the connected vehicle.

17. The control server receives the speed data and the vehicle distance data from a plurality of connected vehicles including the connected vehicle, the speed correction factor is calculated based on the speed data from the plurality of connected vehicles; The computer-readable medium of claim 15 , wherein the following distance correction factor is calculated based on the following distance data from the plurality of connected vehicles.

18. 20. The computer-readable medium of claim 15, wherein the prediction engine utilizes a long-term shared world model that stores data received from a plurality of connected vehicles and a plurality of road sensors.

19. Adjusting the determined future flow rate based on the speed correction factor and the vehicle distance correction factor multiplying the determined future flow rate by a quotient of the speed correction factor and the following distance correction factor; 20. The computer readable medium of claim 15, comprising:

20. Adjusting the determined future average speed based on the speed correction factor multiplying said determined future average speed by said speed correction factor; 20. The computer readable medium of claim 15, comprising:

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