Methods and systems for estimating lane-level traffic jam using lane change signals of connected vehicles

Abstract

To provide a system for estimating a lane-level traffic jam.SOLUTION: A system includes one or more processors. The one or more processors are programmed to: acquire information on lane changes of vehicles in a road section including a traffic jam section, the road section including a plurality of lanes; collect driving data of the vehicles after the lane changes; estimate lane-level traffic jam distribution of the plurality of lanes on the basis of the information on the lane changes and the driving data; and transmit the lane-level traffic jam distribution to vehicles approaching the traffic jam section.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present specification relates to systems and methods for estimating lane-level traffic congestion, and more particularly, to systems and methods for estimating lane-level traffic congestion using lane change signals in connected vehicles. [Background technology]

[0002] Lane-level traffic, where the average speeds of vehicles in different lanes vary, can increase collision risk, especially rear-end collisions. In addition, when different traffic levels exist, drivers may miss the rear of the traffic queue and attempt to cut in. Existing navigation systems do not provide lane-level traffic. For example, when a right exit is congested, existing navigation systems do not show the congested right lane, but instead show the entire road section with no traffic due to driving data from vehicles driving at normal speeds in other lanes.

[0003] Therefore, a need exists for a system and method that accurately estimates lane-level traffic information. Summary of the Invention

[0004] The present disclosure provides systems and methods for estimating congested lanes using lane change signals in connected vehicles.

[0005] In one embodiment, a system for estimating lane-level traffic congestion is provided, the system including one or more processors programmed to: obtain information regarding lane changes of vehicles on a road section including a traffic congestion section, the road section including a plurality of lanes, collect driving data of the vehicles after the lane changes, estimate a lane-level traffic congestion distribution for the plurality of lanes based on the information regarding the lane changes and the driving data, and transmit the lane-level traffic congestion distribution to vehicles approaching the traffic congestion section.

[0006] In another embodiment, a method for determining lane-level traffic congestion is provided, the method including: obtaining information regarding lane changes of vehicles on a road section including a traffic congestion section, the road section including a plurality of lanes; collecting driving data of the vehicles after the lane changes; estimating a lane-level traffic congestion distribution for the plurality of lanes based on the information regarding the lane changes and the driving data; identifying lanes with traffic congestion based on the lane-level traffic congestion distribution; and transmitting information regarding the identified lanes to vehicles approaching the traffic congestion section.

[0007] These and further features provided by the embodiments of the present disclosure will be more fully understood when the following detailed description is considered in conjunction with the drawings. [Brief explanation of the drawings]

[0008] The embodiments set forth in the drawings are illustrative and exemplary in nature and are not intended to limit the present disclosure. The following detailed description of illustrative embodiments can be understood when read in conjunction with the following drawings, in which like structure is designated with like reference numerals and in which:

[0009] [Figure 1A] FIG. 1A schematically depicts a system for estimating lane-level traffic congestion using lane change signals in connected vehicles, according to one or more embodiments shown and described herein. [Figure 1B] FIG. 1B depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 1C] FIG. 1C illustrates an exemplary lane-level traffic distribution image for a road according to one or more embodiments shown and described herein. [Figure 2]FIG. 2 schematically depicts a system for estimating lane-level traffic congestion using lane change signals in connected vehicles, according to one or more embodiments shown and described herein. [Figure 3] FIG. 3 depicts a flowchart for estimating lane-level traffic congestion according to one or more embodiments shown and described herein. [Figure 4] FIG. 4 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 5] FIG. 5 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 6] FIG. 6 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 7] FIG. 7 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 8] FIG. 8 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 9] FIG. 9 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. [Figure 10] FIG. 10 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. DETAILED DESCRIPTION OF THE INVENTION

[0010] Embodiments disclosed herein include systems and methods for estimating lane-level traffic congestion, according to one or more embodiments shown and described herein. In particular, as used herein, lane-level traffic congestion refers to a situation in which the average speed of vehicles in one lane of a road substantially differs from the average speed of vehicles in another lane of the road. More specifically, lane-level traffic congestion may refer to a situation in which the average speed of vehicles in one lane of a road within a particular region differs from the average speed of vehicles in another lane of the road within the particular region by more than a threshold amount.

[0011] When a lane-level traffic congestion occurs, the lane-level traffic congestion can result in inefficient or dangerous driving conditions. Therefore, it may be desirable to detect lane-level traffic congestion. If a lane-level traffic congestion can be detected, a driver and an autonomous vehicle can be alerted to the lane-level traffic congestion. Thus, the driver or autonomous vehicle can plan a navigation route taking the lane-level traffic congestion into account. For example, a driver can avoid an area with lane-level traffic or change lanes before reaching a lane-level traffic congestion.

[0012] Many modern vehicles are connected vehicles, meaning that they can send and / or receive data to or from external computing devices (e.g., other vehicles, traffic infrastructure, edge servers, or cloud servers). Thus, when a cloud server or other computing device receives driving data from many connected vehicles, the cloud server may use the received driving data to determine traffic information based on the aggregated driving data. However, while many vehicles may receive GPS data indicating the location of the vehicle, the GPS data is often noisy and not accurate enough to determine which lane the vehicle is located in on the road. Thus, it may not be possible to determine lane-level traffic directly from the GPS data.

[0013] In the embodiments disclosed herein, a server obtains information about a vehicle's lane change on a road section including a traffic congestion section, such as the lane change of vehicle 110 in FIG. 1B or the lane change of vehicle 110 in FIG. 6. The server collects driving data of the vehicle after the lane change, such as acceleration or deceleration. The server then estimates a lane-level traffic congestion distribution for multiple lanes of the road section, such as lane-level traffic congestion distribution 140 in FIG. 1B or lane-level traffic congestion distribution 620 in FIG. 6, based on the information about the lane change and the driving data. The server transmits the lane-level traffic congestion distribution to vehicles approaching the traffic congestion section, so that the vehicles approaching the traffic congestion section can use the lane-level traffic congestion distribution to avoid lanes with traffic congestion.

[0014] According to the present disclosure, the system identifies the lane ID of a traffic jam by, for example, analyzing changes in the state of a connected vehicle from a congested state to a free-flowing state and tracking the lane changes of the connected vehicle on a road segment. The system identifies the lane ID of a traffic jam without requesting the lane ID from the vehicle.

[0015] 1A schematically depicts a system for estimating lane-level traffic congestion using lane-change signals in connected vehicles, according to one or more embodiments shown and described herein. In an embodiment, the system includes first and second connected vehicles 110 and 120 and a server 240. Server 240 may be a local server, including, but not limited to, a roadside unit, an edge server, and the like. In some embodiments, server 240 may be a remote server, such as a cloud server.

[0016] Each of the first and second connected vehicles 110 and 120 may be a vehicle, including an automobile or any other passenger or non-passenger vehicle, such as a land vehicle, an underwater vehicle, and / or an air vehicle, etc. In some embodiments, one or more of the first and second connected vehicles 110 and 120 may be an unmanned aerial vehicle (UAV), commonly known as a drone.

[0017] The first and second connected vehicles 110 and 120 may be autonomous connected vehicles, each navigating its environment with limited or no human input. The first and second connected vehicles 110 and 120 are equipped with internet access and share data with other devices both inside and outside the first and second connected vehicles 110 and 120. Each of the first and second connected vehicles 110 and 120 may include actuators, such as engines, motors, and the like, that drive the vehicle. The first and second connected vehicles 110 and 120 may communicate with a server 240. The server 240 may communicate with vehicles in an area covered by the server 240. The server 240 may communicate with other servers covering different areas. The server 240 may communicate with a remote server and transmit information collected by the server 240 to the remote server.

[0018] 1A , connected vehicles 110 and 120 are traveling on road 100, which includes multiple lanes, e.g., lanes 101, 103, and 105. Connected vehicles 110 and 120 transmit driving data of connected vehicles 110 and 120 to server 240, including, but not limited to, location, speed, acceleration, heading, wheel angle, turn signal status, and the like. While FIG. 1A depicts two connected vehicles 110 and 120, server 240 may receive driving data from more than two connected vehicles 110 and 120. Based on the driving data from the connected vehicles, particularly the speeds of the connected vehicles, server 240 may identify a congested traffic section 250 on road 100.

[0019] Connected vehicles 110 and 120 may not be equipped with high-precision GPS sensors, and as a result, connected vehicles 110 and 120 may not have information regarding which lane connected vehicles 110 and 120 are driving in. For example, connected vehicle 110 has information that connected vehicle 110 is driving on road 100, but connected vehicle 110 is not certain which of lanes 101, 103, and 105 connected vehicle 110 is traveling in. Similarly, connected vehicle 120 is not certain regarding lane-level trajectory information. Thus, when connected vehicles 110 and 120 transmit driving data of connected vehicles 110 and 120 to server 240, the driving data does not include lane ID information, i.e., identification information of the lane in which the corresponding vehicle is driving. In this regard, server 240 may identify traffic congestion section 250, but server 240 cannot identify which lanes among lanes 101, 103, and 105 include traffic congestion and which lanes do not include traffic congestion.

[0020] In embodiments, the system may estimate lane-level traffic congestion conditions using a connected vehicle's lane-change signals. Figure 1B depicts estimating the probability of traffic congestion in each of the lanes using a connected vehicle's lane-change signals, according to one or more embodiments shown and described herein.

[0021] 1B , connected vehicle 110 is initially in traffic congestion 130. Server 240 may determine that connected vehicle 110 is in a traffic congestion based on the speed of connected vehicle 110. For example, if the speed of connected vehicle 110 is less than a threshold speed, such as 5 mph (approximately 8.047 km / h), 10 mph (approximately 16.09 km / h), or 20 mph (approximately 32.19 km / h), server 240 may determine that connected vehicle 110 is in a traffic congestion. As another example, if the speed of connected vehicle 110 significantly deviates from the speed limit of road 100, such as 20 mph (approximately 32.19 km / h) or 30 mph (approximately 48.28 km / h) below the speed limit, server 240 may determine that connected vehicle 110 is in a traffic congestion.

[0022] 1B depicts connected vehicle 110 in lane 101 and traffic congestion 130 located in lane 101, but connected vehicle 110 and server 240 do not have information that connected vehicle 110 and traffic congestion 130 are in lane 101. Server 240 may monitor the driving behavior of connected vehicles in traffic congestion section 250. For example, server 240 receives driving data from connected vehicle 110 that connected vehicle 110 changes lanes to the right in the traffic congestion and accelerates. Server 240 may monitor the driving behavior of other connected vehicles in traffic congestion section 250 and receive that there is no driving data indicating that the connected vehicle changes lanes to the left and accelerates during a certain period of time, such as a few minutes.

[0023] Based on the driving data of connected vehicles in traffic congestion section 250, server 240 may determine that the leftmost lane has the highest probability of having a corresponding traffic congestion. Specifically, server 240 may estimate lane-level traffic congestion distribution 140 including a probability of traffic congestion in each of lanes 101, 103, and 105. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 101, 103, and 105, and may update the initial lane-level traffic congestion distribution based on the driving data of the connected vehicles as driving data is received from the connected vehicles. For example, when more connected vehicles send driving data to server 240 indicating that corresponding vehicles in the traffic congestion change lanes to the right and accelerate, the probability of traffic congestion in the leftmost lane 141 relatively increases, and the probability of traffic congestion in the rightmost lane 145 relatively decreases. As the time period during which the server 240 receives no driving data indicating the corresponding vehicle is changing lanes to the left and accelerating increases, the probability 143 of traffic congestion in the middle lane decreases relatively.

[0024] Server 240 may transmit information regarding the updated lane-level traffic congestion distribution to connected vehicles. In an embodiment, server 240 may transmit information regarding the updated lane-level traffic congestion distribution to connected vehicles approaching traffic congestion section 250, and the connected vehicles approaching traffic congestion section 250 may autonomously drive out of the lane with the traffic congestion. For example, if a connected vehicle approaching traffic congestion section 250 is driving in lane 101, the connected vehicle may proactively change lanes to the right to avoid getting stuck in traffic congestion 130.

[0025] In some embodiments, as shown in FIG. 1C , a connected vehicle receiving an updated lane-level traffic congestion distribution from server 240 may display the lane-level traffic congestion distribution on an output device, such as the vehicle's head unit or a navigation app on a user's smartphone in the vehicle. FIG. 1C shows an example lane-level traffic distribution image for road 100. Lane 101 includes bars 430, 432, and 434. Bar 432 indicates a traffic congestion, and bars 430 and 434 indicate relatively slow-speed driving sections. Lane 103 includes bar 436, and lane 105 includes bar 438. Bars 436 and 438 indicate lanes without traffic congestion.

[0026] 2 is a schematic depiction of a system for estimating lane-level traffic congestion using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein. The system for estimating traffic congestion lanes includes a first connected vehicle system 200, a second connected vehicle system 220, and a server 240.

[0027] It should be noted that while first connected vehicle system 200 and second connected vehicle system 220 are depicted separately, in some embodiments, each of first connected vehicle system 200 and second connected vehicle system 220 may be included within a vehicle, e.g., within each of connected vehicles 110 and 120 of FIG. 1A , respectively. In embodiments in which first connected vehicle system 200 and second connected vehicle system 220 are included within a vehicle, the vehicle may be an automobile or any other passenger or non-passenger vehicle, such as a land vehicle, an underwater vehicle, and / or an air vehicle. In some embodiments, the vehicle is an autonomous vehicle that navigates its environment with limited or no human input.

[0028] The first connected vehicle system 200 includes one or more processors 202. Each of the one or more processors 202 may be any device capable of executing machine-readable executable instructions. Accordingly, each of the one or more processors 202 may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. The one or more processors 202 are connected to a communication path 204 that provides signal interconnection between various modules of the system. Thus, the communication path 204 may communicatively connect any number of processors 202 to each other, enabling the modules connected to the communication path 204 to operate in a distributed computing environment. Specifically, each of the modules may operate as a node that can send and / or receive data. As used herein, the term "communicatively connected" means that the connected components are capable of exchanging data signals with each other, such as, for example, electrical signals over a conductive medium, electromagnetic signals over the air, optical signals over optical waveguides, and the like.

[0029] Thus, communication path 204 may be formed of any medium capable of transmitting a signal, such as, for example, a conductive wire, a conductive trace, an optical waveguide, or the like. In some embodiments, communication path 204 may facilitate the transmission of wireless signals, such as WiFi, Bluetooth, near field communication (NFC), and the like. Furthermore, communication path 204 may be formed of a combination of media capable of transmitting a signal. In one embodiment, communication path 204 comprises a combination of conductive traces, conductive wires, connectors, and buses that cooperate to enable the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices. Thus, communication path 204 may comprise, for example, a vehicle bus, such as a LIN bus, a CAN bus, a VAN bus, and the like. Furthermore, it should be noted that the term “signal” refers to a waveform (e.g., an electrical waveform, an optical waveform, a magnetic waveform, a mechanical waveform, or an electromagnetic waveform), such as DC, AC, a sine wave, a triangular wave, a square wave, a vibration, and the like, that is capable of traveling through a medium.

[0030] The first connected vehicle system 200 includes one or more memory modules 206 coupled to the communication path 204. The one or more memory modules 206 may comprise RAM, ROM, flash memory, a hard drive, or any device capable of storing machine-readable executable instructions such that the machine-readable executable instructions can be accessed by the one or more processors 202. The machine-readable executable instructions may comprise logic or algorithms written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL), such as a machine language that can be executed directly by a processor, or logic or algorithms written in assembly language, object-oriented programming (OOP), scripting language, microcode, or the like that can be compiled or assembled into machine-readable executable instructions and stored in the one or more memory modules 206. Alternatively, the machine-readable executable instructions may be written in a hardware description language (HDL), such as logic implemented via a field programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), or equivalent. Thus, the methods described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components.

[0031] The one or more memory modules 206 may include machine-readable instructions that, when executed by the one or more processors 202, obtain information regarding a vehicle's lane change on a road section including a traffic congestion section, collect driving data of the vehicle after the lane change, estimate a lane-level traffic congestion distribution for a plurality of lanes based on the information regarding the lane change and the driving data, and transmit the lane-level traffic congestion distribution to a vehicle approaching the traffic congestion section.

[0032] Still referring to FIG. 2 , the first connected vehicle system 200 includes one or more sensors 208. The one or more sensors 208 may be any device having an array of sensing devices capable of detecting radiation in the ultraviolet, visible, or infrared wavelength bands. The one or more sensors 208 may have any resolution. In some embodiments, one or more optical components, such as mirrors, fisheye lenses, or any other type of lens, may be optically coupled to the one or more sensors 208. In some embodiments, the one or more sensors 208 may also provide navigation support. That is, data captured by the one or more sensors 208 may be used to navigate the connected vehicle 110 autonomously or semi-autonomously.

[0033] In some embodiments, the one or more sensors 208 include one or more imaging sensors configured to operate in the visible and / or infrared spectrum to detect visible and / or infrared light. Furthermore, while certain embodiments described herein are described with reference to hardware that detects light in the visible and / or infrared spectrum, it should be understood that other types of sensors are contemplated. For example, the systems described herein may include one or more LIDAR, radar, sonar, or other types of sensors, the data of which may be incorporated into or supplement the data collection described herein to develop a more complete real-time traffic picture. A ranging sensor, such as radar, may be used to obtain rough depth and speed information about the field of view of the first connected vehicle system 200. The first connected vehicle system 200 may use one or more imaging sensors to capture road boundaries, static objects, moving objects, and the like.

[0034] In operation, one or more sensors 208 capture image data and communicate the image data to one or more processors 202 and / or other systems communicatively connected to the communication path 204. The image data may be received by one or more processors 202, which may process the image data using one or more image processing algorithms. Any known or undeveloped video and image processing algorithms may be applied to the image data to identify objects or situations. Exemplary video and image processing algorithms include, but are not limited to, kernel-based tracking (e.g., mean-shift tracking) and contour processing algorithms. Generally, video and image processing algorithms may detect objects and movement from sequential or individual frames of image data. One or more object recognition algorithms may be applied to the image data to extract objects and determine their relative locations to one another. Any known or undeveloped object recognition algorithms may be used to extract objects or even optically extract characters and images from the image data. Exemplary object recognition algorithms include, but are not limited to, scale invariant feature transform ("SIFT"), speedup of robust features ("SURF"), and edge detection algorithms.

[0035] First connected vehicle system 200 includes a satellite antenna 214 connected to communication path 204 such that communication path 204 communicatively connects satellite antenna 214 to other modules of first connected vehicle system 200. Satellite antenna 214 is configured to receive signals from Global Positioning System satellites. Specifically, in one embodiment, satellite antenna 214 includes one or more conductive elements that interact with electromagnetic signals transmitted by Global Positioning System satellites. The received signals are converted by one or more processors 202 into data signals indicative of the location (e.g., latitude and longitude) of satellite antenna 214 or objects located near satellite antenna 214.

[0036] The first connected vehicle system 200 includes one or more vehicle sensors 212. Each of the one or more vehicle sensors 212 is connected to the communication path 204 and communicatively coupled to the one or more processors 202. The one or more vehicle sensors 212 may include one or more motion sensors that detect and measure vehicle orientation, acceleration, motion, and changes in motion. The motion sensors may include an inertial measurement unit. Each of the one or more motion sensors may include one or more accelerometers and one or more gyroscopes. Each of the one or more motion sensors converts sensed physical vehicle movement into a signal indicative of the vehicle's orientation, rotation, speed, or acceleration. The one or more vehicle sensors 212 may include wheel sensors that detect wheel angle.

[0037] Still referring to FIG. 2 , first connected vehicle system 200 includes network interface hardware 216 that communicatively couples first connected vehicle system 200 to second connected vehicle system 220 and / or server 240. Network interface hardware 216 may be any device that can be communicatively coupled to communication path 204 and capable of transmitting and / or receiving data over a network. Thus, network interface hardware 216 may include a communications transceiver that transmits and / or receives any wired or wireless communications. For example, network interface hardware 216 may include an antenna, a modem, a LAN port, a Wi-Fi card, a WiMax card, mobile communications hardware, short-range communications hardware, satellite communications hardware, and / or any wired or wireless hardware that communicates with other networks and / or devices. In one embodiment, network interface hardware 216 includes hardware configured to operate according to the Bluetooth® wireless communications protocol. Network interface hardware 216 of first connected vehicle system 200 may transmit its data to server 240. For example, the network interface hardware 216 of the first connected vehicle system 200 may transmit captured point clouds, vehicle data, location data, and the like generated by the first connected vehicle system 200 to other connected vehicles or to the server 240.

[0038] First connected vehicle system 200 may connect with one or more external vehicles and / or external processing devices (e.g., server 240) via direct connections. The direct connections may be vehicle-to-vehicle (“V2V connection”) or vehicle-to-everything (“V2X connection”). V2V or V2X connections may be established using any suitable wireless communication protocol described above. Vehicle-to-vehicle connections may utilize sessions that are time-based and / or location-based. In embodiments, connections between vehicles or between vehicles and infrastructure elements may utilize one or more connecting networks (e.g., network 252), which may be alternatives to or in addition to direct connections (e.g., V2V or V2X) between vehicles or between vehicles and infrastructure. As a non-limiting example, vehicles may act as infrastructure nodes to form a mesh network and dynamically connect on an ad-hoc basis. In this manner, vehicles may freely enter and / or exit the network, allowing the mesh network to self-organize and self-modify over time. Other non-limiting network examples include vehicles forming peer-to-peer networks with other vehicles or utilizing centralized networks that rely on specific vehicle and / or infrastructure elements. Still other examples include networks that use centralized servers and other central computing devices to store and / or relay information between vehicles.

[0039] Still referring to FIG. 2 , first connected vehicle system 200 may be communicatively coupled to server 240 by network 252. In one embodiment, network 252 may include one or more computer networks (e.g., personal area networks, local area networks, or wide area networks), cellular networks, satellite networks, and / or global positioning systems, and combinations thereof. Thus, first connected vehicle system 200 may be communicatively coupled to network 252 via a wide area network, local area network, personal area network, cellular network, satellite network, etc. Suitable local area networks may include wired Ethernet and / or wireless technologies such as, for example, Wireless Fidelity (Wi-Fi). Suitable personal area networks may include wireless technologies such as, for example, IrDA, Bluetooth, Wireless USB, Z-Wave, ZigBee, and / or other short-range communication protocols. Suitable cellular networks include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM.

[0040] 2 , server 240 includes one or more processors 242, one or more memory modules 246, network interface hardware 248, and communication path 244. One or more processors 242 may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. One or more memory modules 246 may comprise RAM, ROM, flash memory, a hard drive, or any device capable of storing machine-readable executable instructions such that the machine-readable executable instructions can be accessed by one or more processors 242. In some embodiments, communication path 244 may be similar to communication path 204.

[0041] The one or more memory modules 246 may include machine-readable instructions that, when executed by the one or more processors 242, obtain information regarding vehicle lane changes on a road section including a traffic congestion section, collect driving data of the vehicle after the lane change, estimate a lane-level traffic congestion distribution for multiple lanes of the road section based on the lane change information and the driving data, and transmit the lane-level traffic congestion distribution to vehicles approaching the traffic congestion section.

[0042] Still referring to FIG. 2 , the second connected vehicle system 220 includes one or more processors 222, one or more memory modules 226, one or more sensors 228, one or more vehicle sensors 232, a satellite antenna 234, network interface hardware 236, and a communication path 224 communicatively coupled to other components of the second connected vehicle system 220. The components of second connected vehicle system 220 may be structurally similar and have similar functionality to the corresponding components of first connected vehicle system 200 (e.g., one or more processors 222 correspond to one or more processors 202, one or more memory modules 226 correspond to one or more memory modules 206, one or more sensors 228 correspond to one or more sensors 208, one or more vehicle sensors 232 correspond to one or more vehicle sensors 212, satellite antenna 234 corresponds to satellite antenna 214, network interface hardware 236 corresponds to network interface hardware 216, and communication path 224 corresponds to communication path 204).

[0043] The one or more memory modules 226 may include machine-readable instructions that, when executed by the one or more processors 222, obtain information regarding vehicle lane changes on a road section including a traffic congestion section, collect driving data of the vehicle after the lane change, estimate a lane-level traffic congestion distribution for multiple lanes of the road section based on the lane change information and the driving data, and transmit the lane-level traffic congestion distribution to a vehicle approaching the traffic congestion section.

[0044] FIG. 3 depicts a flowchart for estimating lane-level traffic congestion according to one or more embodiments shown and described herein.

[0045] In step 310, the server obtains information about vehicle lane changes on a road section that includes a congestion section. Referring to FIG. 1A , server 240 may obtain map data about road 100. The map data may indicate that road 100 includes three lanes 101, 103, and 105. The server may receive driving data from connected vehicles on road 100, assign locations of the connected vehicles to the map data, and identify congestion section 250 based on the driving data of the connected vehicles, including the speed of the connected vehicles. Server 240 may detect the front and rear of the congestion section based on the speed of the connected vehicles and identify a congestion section that extends from the front to the rear. For example, the front of a traffic jam may be the location of a connected vehicle located at the front of connected vehicles whose speed is below a threshold speed, such as, for example, 5 mph (approximately 8.047 km / h), 10 mph (approximately 16.09 km / h), 20 mph (approximately 32.19 km / h), etc. The rear of a traffic jam may be the location of a connected vehicle located at the rear of connected vehicles whose speed is below a threshold speed, such as, for example, 5 mph (approximately 8.047 km / h), 10 mph (approximately 16.09 km / h), 20 mph (approximately 32.19 km / h), etc.

[0046] Server 240 may communicate with connected vehicles in or near the traffic congestion section and receive driving data including GPS coordinates, speed, and signals that can be used to detect vehicle lane changes, including wheel angle, accelerometer data, lane crossings, and turn signal status. Based on the driving data, server 240 may identify vehicles changing lanes in or near the traffic congestion section and obtain information about the vehicle's lane change, such as the location of the lane change, the direction of the lane change, and the like. For example, with reference to FIG. 1B , the server may determine that connected vehicle 110 in traffic congestion section 250 changed lanes to the right based on data such as wheel angle data, accelerometer data, lane crossing data, turn signal status data, and the like.

[0047] Referring again to FIG. 3 , in step 320, server 240 collects driving data of vehicle 110 after a lane change. For example, referring to FIG. 1B , server 240 identifies that vehicle 110 changes lanes to the right and continues to collect driving data of vehicle 110 immediately after the lane change. The driving data of vehicle 110 may include acceleration or deceleration information. In this example, server 240 collects driving data of vehicle 110 indicating that vehicle 110 changed lanes to the right and accelerated.

[0048] Referring again to FIG. 3 , in step 330, server 240 estimates a lane-level traffic congestion distribution for multiple lanes of the road section based on the lane-changing information and the driving data. Referring to FIG. 1B , server 240 may estimate lane-level traffic congestion distribution 140, which includes a probability of traffic congestion in each of lanes 101, 103, and 105. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 101, 103, and 105, and may update the initial lane-level traffic congestion distribution based on connected vehicle driving data as the driving data is received from connected vehicles. For example, when more connected vehicles send driving data to server 240 indicating that a corresponding vehicle in the traffic congestion situation changes lanes to the right and accelerates, the probability of traffic congestion in the leftmost lane 141 increases relatively, and the probability of traffic congestion in the rightmost lane 145 decreases relatively. As the period during which the server 240 does not receive any maneuver data indicating that the corresponding vehicle is changing lanes to the left and accelerating increases, the probability 143 of a traffic jam in the middle lane relatively decreases. If any vehicle changes lanes to the left and accelerates, it means that the traffic jam may not be in the leftmost lane. Therefore, if the server 240 does not receive any maneuver data indicating that the corresponding vehicle is changing lanes to the left and accelerating, it means that the traffic jam is likely to be in the leftmost lane.

[0049] Referring again to Figure 3, at step 340, the server may transmit the lane-level traffic congestion distribution to a vehicle approaching the traffic congestion section. Referring to Figure 1B, server 240 may transmit information about the updated lane-level traffic congestion distribution to a connected vehicle approaching traffic congestion section 250, and the connected vehicle approaching traffic congestion section 250 may autonomously drive out of the lane with the traffic congestion. In some embodiments, as shown in Figure 1C, a connected vehicle receiving the updated lane-level traffic congestion distribution from server 240 may display the lane-level traffic congestion distribution on an output device, for example, a vehicle head unit or a navigation app on a user's smartphone in the vehicle.

[0050] In some embodiments, server 240 may identify lanes with traffic congestion based on the lane-level traffic congestion distribution and transmit information about the identified lanes to vehicles approaching the traffic congestion section. For example, based on lane-level traffic congestion distribution 140, server 240 identifies lane 101 as a lane with traffic congestion and transmits information about lane 101 to connected vehicles approaching traffic congestion section 250.

[0051] FIG. 4 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0052] 4 , connected vehicle 110 is initially in traffic congestion 410. Server 240 may determine that connected vehicle 110 is in traffic congestion 410 based on the speed of connected vehicle 110. Although FIG. 4 depicts connected vehicle 110 in lane 105 and traffic congestion 410 located in lane 105, connected vehicle 110 and server 240 do not have information that connected vehicle 110 and traffic congestion 410 are in lane 105. Server 240 may monitor the driving behavior of the connected vehicle in traffic congestion section 420. For example, server 240 receives driving data from connected vehicle 110 that the connected vehicle 110 in the traffic congestion changes lanes to the left and accelerates. Server 240 may monitor the driving behavior of other connected vehicles in traffic congestion section 420 and receive no driving data indicating the connected vehicle changes lanes to the right and accelerates.

[0053] Based on the driving data of connected vehicles in traffic congestion section 250, server 240 may determine that the rightmost lane has the highest probability of having a corresponding traffic congestion. Specifically, server 240 may estimate lane-level traffic congestion distribution 140 including a probability of traffic congestion in each of lanes 101, 103, and 105. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 101, 103, and 105, and may update the initial lane-level traffic congestion distribution based on the driving data of the connected vehicles as driving data is received from the connected vehicles. For example, when more connected vehicles send driving data to server 240 indicating that corresponding vehicles in the traffic congestion change lanes to the left and accelerate, the probability 425 of traffic congestion in the rightmost lane will relatively increase, and the probability 421 of traffic congestion in the leftmost lane will relatively decrease. As the time period during which the server 240 does not receive any driving data indicating that the corresponding vehicle is changing lanes to the right and accelerating increases, the probability of traffic congestion in the middle lane 423 decreases relatively. The server 240 may send information regarding updated lane-level traffic congestion distribution to the connected vehicles.

[0054] FIG. 5 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0055] 5, connected vehicle 110 is driving on a road including lanes 501, 503, 505, and 507. Connected vehicle 110 is approaching traffic congestion 510. Server 240 may determine that connected vehicle 110 is approaching traffic congestion 510 based on the location of connected vehicle 110. Although FIG. 5 depicts connected vehicle 110 in lane 503 and traffic congestion 510 located in lane 503, connected vehicle 110 and server 240 do not have information that connected vehicle 110 and traffic congestion 510 are in lane 503. Server 240 may monitor the driving behavior of connected vehicles approaching traffic congestion section 512. For example, the server 240 receives driving data from the connected vehicle 110 that indicates that a connected vehicle 110 approaching a traffic jam changes lanes to the left and accelerates, and another connected vehicle approaching the traffic jam changes lanes to the right and accelerates.

[0056] Based on driving data of the connected vehicle approaching or in traffic congestion section 512, server 240 may determine that intermediate lanes 503 and 505 have a relatively high probability of having a corresponding traffic congestion. Specifically, server 240 may estimate lane-level traffic congestion distribution 520 that includes a probability of traffic congestion in each of lanes 501, 503, 505, and 507. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 501, 503, 505, and 507, and may update the initial lane-level traffic congestion distribution based on connected vehicle driving data as the driving data is received from the connected vehicle. For example, as more connected vehicles transmit driving data to server 240 indicating that corresponding vehicles in a traffic jam change lanes to the left or right and accelerate, the probability of traffic jam in each of the intermediate lanes 523 and 525 increases relatively, and the probability of traffic jam in each of the leftmost and rightmost lanes decreases relatively. Server 240 may transmit information regarding the updated lane-level traffic jam distribution to connected vehicles approaching traffic jam section 512.

[0057] FIG. 6 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0058] 6 , connected vehicle 110 is in a traffic jam section 612 but is initially driving at a normal speed. Server 240 may determine that connected vehicle 110 is driving at a normal speed based on the speed of connected vehicle 110. Although FIG. 6 depicts connected vehicle 110 in lane 103 and traffic jam 610 located in lane 101, connected vehicle 110 and server 240 do not have information that connected vehicle 110 is in lane 103 and traffic jam 610 is in lane 101. Server 240 may monitor the driving behavior of the connected vehicle in traffic jam section 612. For example, server 240 receives driving data from connected vehicle 110 that connected vehicle 110, driving at a normal speed, changes lanes to the left and slows down. Server 240 may monitor the driving behavior of other connected vehicles in heavy traffic section 420 and receive no driving data indicating that the connected vehicle should change lanes to the right and slow down.

[0059] Based on the driving data of connected vehicles in the traffic congestion section 612, server 240 may determine that the leftmost lane has the highest probability of having a corresponding traffic congestion. Specifically, server 240 may estimate a lane-level traffic congestion distribution 620 that includes a probability of a traffic congestion in each of lanes 101, 103, and 105. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of a traffic congestion in each of lanes 101, 103, and 105, and may update the initial lane-level traffic congestion distribution based on the driving data of the connected vehicles as driving data is received from the connected vehicles. For example, when more connected vehicles send driving data to server 240 indicating that corresponding vehicles in the traffic congestion change lanes to the left and slow down, the probability 621 of a traffic congestion in the leftmost lane will relatively increase, and the probability 625 of a traffic congestion in the rightmost lane will relatively decrease. As the time period during which server 240 does not receive any driving data indicating a corresponding vehicle changing lanes to the right and slowing down increases, the probability of traffic congestion in the middle lanes 623 decreases relatively. Server 240 may send information regarding the updated lane-level traffic congestion distribution to connected vehicles approaching the traffic congestion section 612.

[0060] FIG. 7 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0061] 7, connected vehicle 110 is in a traffic jam section 712 but is initially driving at a normal speed. Server 240 may determine that connected vehicle 110 is driving at a normal speed based on the speed of connected vehicle 110. Although FIG. 7 depicts connected vehicle 110 in lane 103 and traffic jam 710 located in lane 105, connected vehicle 110 and server 240 do not have information that connected vehicle 110 is in lane 103 and traffic jam 710 is in lane 105. Server 240 may monitor the driving behavior of the connected vehicle in traffic jam section 712. For example, server 240 receives driving data from connected vehicle 110 that indicates connected vehicle 110, driving at a normal speed, changes lanes to the right and slows down. Server 240 may monitor the driving behavior of other connected vehicles in heavy traffic section 712 and receive no driving data indicating the connected vehicle should change lanes to the left and slow down.

[0062] Based on the driving data of connected vehicles in the traffic congestion section 712, server 240 may determine that the rightmost lane has the highest probability of having a corresponding traffic congestion. Specifically, server 240 may estimate a lane-level traffic congestion distribution 720 that includes a probability of a traffic congestion in each of lanes 101, 103, and 105. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of a traffic congestion in each of lanes 101, 103, and 105, and may update the initial lane-level traffic congestion distribution based on the driving data of the connected vehicles as the driving data is received from the connected vehicles. For example, when more connected vehicles send driving data to server 240 indicating that corresponding vehicles in the traffic congestion change lanes to the right and slow down, the probability 725 of a traffic congestion in the rightmost lane will relatively increase, and the probability 721 of a traffic congestion in the leftmost lane will relatively decrease. As the time period during which server 240 does not receive any driving data indicating a corresponding vehicle changing lanes to the left and slowing down increases, the probability of traffic congestion in the middle lanes 723 relatively decreases. Server 240 may send information regarding the updated lane-level traffic congestion distribution to connected vehicles approaching the traffic congestion section 712.

[0063] FIG. 8 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0064] 8 , connected vehicle 110 is driving on a road including lanes 801, 803, 805, and 807. Connected vehicles 110 and 120 are driving at a normal speed, i.e., are not in a traffic jam. Server 240 may determine that connected vehicles 110 and 120 are driving at a normal speed based on the speed data of connected vehicles 110 and 120. Although FIG. 8 depicts connected vehicle 110 initially in lane 805, connected vehicle 120 initially in lane 801, and traffic jam 810 located in lane 803, connected vehicles 110 and 120 and server 240 do not have information that connected vehicle 110 is in lane 805, connected vehicle 120 is in lane 801, and traffic jam 810 is in lane 803. Server 240 may monitor the driving behavior of connected vehicles approaching or within traffic jam section 812. For example, server 240 receives driving data from connected vehicle 110 that connected vehicle 110 within traffic jam section 812 changes lanes to the left and slows down, and connected vehicle 120 within traffic jam section 812 changes lanes to the right and slows down.

[0065] Based on the connected vehicle's driving data in traffic congestion section 812, server 240 may determine that intermediate lanes 803 and 805 have a relatively high probability of having a corresponding traffic congestion. Specifically, server 240 may estimate lane-level traffic congestion distribution 820 that includes a probability of traffic congestion in each of lanes 801, 803, 805, and 807. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 801, 803, 805, and 807, and may update the initial lane-level traffic congestion distribution based on connected vehicle driving data as the driving data is received from the connected vehicle. For example, as more connected vehicles transmit driving data to server 240 indicating that corresponding vehicles in traffic congestion section 812 are changing lanes to the left or right and slowing down, the probabilities 823 and 825 of traffic congestion in each of the intermediate lanes relatively increase, and the probabilities of traffic congestion in each of the leftmost and rightmost lanes relatively decrease. Server 240 may transmit information regarding the updated lane-level traffic congestion distribution to connected vehicles approaching traffic congestion section 812.

[0066] FIG. 9 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0067] 9, connected vehicle 110 is driving on a road including lanes 901, 903, 905, and 907. Connected vehicle 110 is approaching a traffic congestion section 912. Server 240 may determine that connected vehicle 110 is approaching traffic congestion section 912 based on the location of connected vehicle 110. Although FIG. 9 depicts connected vehicle 110 initially in lane 901 and traffic congestion 910 located in lane 903, connected vehicle 110 and server 240 do not have information that connected vehicle 110 initially in lane 901 and traffic congestion 910 in lane 903. Server 240 may monitor the driving behavior of the connected vehicle as it approaches traffic congestion section 912. For example, server 240 receives driving data from connected vehicle 110 that indicates connected vehicle 110 is approaching a traffic jam and changes lanes to the right three times. The driving data may indicate connected vehicle 110 slows down after making one lane change to the right, then makes another lane change to the right and accelerates.

[0068] Based on driving data of connected vehicles approaching or in traffic congestion section 912, server 240 may determine that middle lane 903 has a relatively high probability of having a corresponding traffic congestion. Specifically, server 240 may estimate lane-level traffic congestion distribution 920 including a probability of traffic congestion in each of lanes 901, 903, 905, and 907. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 901, 903, 905, and 907, and may update the initial lane-level traffic congestion distribution based on the driving data of the connected vehicles as driving data is received from the connected vehicles. For example, when more connected vehicles send driving data to server 240 indicating that a corresponding vehicle changes lanes to the right three times and slows down after the first lane change, the probability of traffic congestion in a middle lane 923 relatively increases, the probability of traffic congestion in another middle lane 925 decreases, and the probability of traffic congestion in each of the leftmost and rightmost lanes decreases. This is because there are four lanes, and if a connected vehicle is making three consecutive right lane changes, it must first be traveling in the leftmost lane, i.e., lane 901. Server 240 may send information regarding the updated lane-level traffic congestion distribution to connected vehicles approaching traffic congestion section 912.

[0069] FIG. 10 depicts estimating the probability of traffic congestion in each of the lanes using lane change signals in a connected vehicle, according to one or more embodiments shown and described herein.

[0070] 10 , connected vehicle 110 is driving on a road including lanes 1001, 1003, 1005, and 1007. Connected vehicles 110 and 120 are in a traffic jam. Server 240 may determine that connected vehicles 110 and 120 are in a traffic jam based on speed data of connected vehicles 110 and 120. Although FIG. 10 depicts connected vehicles 110 and 120 initially in lane 1003 and traffic jam 1010 located in lane 1003, connected vehicles 110 and 120 and server 240 do not have information that connected vehicles 110 and 120 are in lane 1003 and that traffic jam 1010 is in lane 1003. Server 240 may monitor the driving behavior of connected vehicles approaching or within traffic congestion section 1012. For example, server 240 receives driving data from connected vehicle 110 that, within traffic congestion section 1012, connected vehicle 110 changes lanes to the right and accelerates, then changes lanes to the right again, and connected vehicle 120, within traffic congestion section 1012, changes lanes to the left and accelerates.

[0071] Based on the connected vehicle's driving data in the traffic congestion section 1012, server 240 may determine that the middle lane 1003 has a relatively high probability of having a corresponding traffic congestion. Specifically, server 240 may estimate a lane-level traffic congestion distribution 1020 that includes a probability of traffic congestion in each of lanes 1001, 1003, 1005, and 1007. Server 240 may generate an initial lane-level traffic congestion distribution that may have an equal probability of traffic congestion in each of lanes 1001, 1003, 1005, and 1007, and may update the initial lane-level traffic congestion distribution based on connected vehicle driving data as the driving data is received from the connected vehicle. For example, when more connected vehicles send driving data to server 240 indicating that a corresponding vehicle in traffic congestion section 1012 changes lanes to the right and accelerates, and that a corresponding vehicle in traffic congestion section 1012 changes lanes to the left and accelerates, the probabilities 1023 and 1025 of traffic congestion in the middle lanes relatively increase, and the probabilities of traffic congestion in the leftmost lane and the rightmost lane, respectively, relatively decrease. When more connected vehicles send driving data to server 240 indicating that a corresponding vehicle in traffic congestion section 1012 changes lanes to the right and accelerates, and then changes lanes to the right again, the probability 1023 of traffic congestion in the middle lanes relatively increase, and the probability 1025 of traffic congestion in the middle lanes relatively decrease. Server 240 may send information regarding the updated lane-level traffic congestion distribution to connected vehicles approaching traffic congestion section 1012.

[0072] It should be appreciated that embodiments described herein are directed to a method and system for estimating lane-level traffic congestion, according to one or more embodiments shown and described herein, which obtain information regarding lane changes by vehicles on a road section including a traffic congestion section, collect driving data of the vehicles after the lane changes, estimate a lane-level traffic congestion distribution for multiple lanes of the road section based on the information regarding the lane changes and the driving data, and transmit the lane-level traffic congestion distribution to vehicles approaching the traffic congestion section.

[0073] According to the present disclosure, the system identifies the lane ID of a traffic jam by, for example, analyzing changes in the state of a connected vehicle from a congested state to a free-flowing state and tracking the lane changes of the connected vehicle on a road segment. The system identifies the lane ID of a traffic jam without requesting the lane ID from the vehicle.

[0074] It should be noted that the terms "substantially" and "about" may be used herein to express the degree of inherent uncertainty that may result from any quantitative comparison, value, measurement, or other representation. These terms are also used herein to express the degree to which a quantitative representation may vary from the stated basis without resulting in a change in the basic functionality of the subject matter at issue.

[0075] While particular embodiments have been shown and described herein, it should be understood that various other changes and modifications can be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter are described herein, such aspects need not be utilized in combination. It is therefore intended that the appended claims cover all such changes and modifications that are within the scope of the claimed subject matter.

Claims

1. 1. A system comprising one or more processors, The one or more processors: obtaining information regarding lane changes of vehicles on a road section including a traffic jam section, the road section including a plurality of lanes; collecting driving data of the vehicle after the lane change; estimating a lane-level traffic congestion distribution for the plurality of lanes based on the information regarding the lane changes and the driving data; transmitting the lane-level traffic congestion distribution to vehicles approaching the traffic congestion section; A system programmed to do the following:

2. The system of claim 1 , wherein the lane-level traffic congestion distribution comprises a probability of traffic congestion in each of the plurality of lanes.

3. The one or more processors further include: Identifying vehicles in traffic jams; Acquire information that the vehicle has moved into the right lane and accelerated; 3. The system of claim 2, programmed to increase the probability of traffic congestion in the leftmost lane of the plurality of lanes based on the information that the vehicle has moved into the right lane and accelerated.

4. The one or more processors further include: obtaining information that no vehicle has moved into the left lane and accelerated in the traffic jam during a predetermined period of time; 4. The system of claim 3, programmed to increase the probability of the traffic congestion in the leftmost lane of the plurality of lanes based on the information that no vehicles have moved into and accelerated into the left lane in the traffic congestion during the predetermined period of time.

5. The one or more processors further include: Identifying vehicles in traffic jams; acquiring information that the vehicle has moved into the left lane and accelerated; 3. The system of claim 2, programmed to increase the probability of traffic congestion in the rightmost lane of the plurality of lanes based on the information that the vehicle has moved into the left lane and accelerated.

6. The one or more processors further include: Identifying a first vehicle and a second vehicle in the traffic jam; Obtaining information that the first vehicle has moved into a left lane and accelerated; obtaining information that the second vehicle has moved into the right lane and accelerated; 3. The system of claim 2, further programmed to increase the probability of traffic congestion in one or more intermediate lanes of the plurality of lanes based on the information that the first vehicle has moved into the left lane and accelerated and the second vehicle has moved into the right lane and accelerated.

7. The one or more processors further include: Identify vehicles that are not in traffic jams, Acquire information that the vehicle has moved into the left lane and decelerated; 3. The system of claim 2, programmed to increase the probability of traffic congestion in the leftmost lane of the plurality of lanes based on the information that the vehicle has moved into the left lane and decelerated.

8. The one or more processors: Identify vehicles that are not in traffic jams, Acquire information that the vehicle has moved to the right lane and decelerated; 3. The system of claim 2, programmed to increase the probability of traffic congestion in the rightmost lane of the plurality of lanes based on the information that the vehicle has moved into the right lane and decelerated.

9. The one or more processors: Identifying a first vehicle and a second vehicle that are not in a traffic jam; Obtaining information that the first vehicle has moved into a left lane and decelerated; Obtaining information that the second vehicle has moved into the right lane and decelerated; 3. The system of claim 2, further programmed to increase the probability of traffic congestion in one or more intermediate lanes of the plurality of lanes based on the information that the first vehicle has moved into the left lane and slowed down and the second vehicle has moved into the right lane and slowed down.

10. The one or more processors: obtaining a number of lane changes by the vehicle before the vehicle enters a traffic jam; obtaining a number of lane changes by the vehicle after the vehicle enters the traffic jam; 3. The system of claim 2, further programmed to adjust the lane-level traffic congestion distribution based on a number of lane changes by the vehicle before the vehicle enters the traffic congestion and a number of lane changes by the vehicle after the vehicle enters the traffic congestion.

11. The one or more processors: Identifying vehicles in traffic jams; obtaining a number of lane changes by the vehicle after entering the traffic jam and a direction of the lane changes; 3. The system of claim 2, programmed to adjust the lane-level traffic congestion distribution based on the number of lane changes by the vehicle after being in the traffic congestion and the direction of the lane changes.

12. The one or more processors: Identifying a first vehicle and a second vehicle in the traffic jam; obtaining a number of leftward lane changes by the first vehicle after being in the traffic jam; obtaining a number of rightward lane changes by the second vehicle after the second vehicle has been in the traffic jam; 3. The system of claim 2, further programmed to adjust the lane-level traffic congestion distribution based on a number of lane changes to the left by the first vehicle after entering the traffic congestion and a number of lane changes to the right by the second vehicle after entering the traffic congestion.

13. The system of claim 1 , wherein the driving data includes an acceleration or deceleration of the vehicle.

14. 1. A method for determining lane-level traffic congestion, the method comprising: obtaining information regarding lane changes of vehicles on a road section including a traffic jam section, the road section including a plurality of lanes; collecting driving data of the vehicle after the lane change; estimating a lane-level traffic congestion distribution for the plurality of lanes based on the information regarding the lane changes and the driving data; identifying lanes with traffic congestion based on the lane-level traffic congestion distribution; transmitting information about the identified lanes to vehicles approaching the congestion section; A method comprising:

15. The method of claim 14 , wherein the lane-level traffic congestion distribution comprises a probability of traffic congestion in each of the plurality of lanes.

16. Identifying vehicles in a traffic jam; obtaining information that the vehicle has moved into a right lane and accelerated; increasing the probability of traffic congestion in the leftmost lane of the plurality of lanes based on the information that the vehicle has moved into the right lane and accelerated; obtaining information that no vehicle has moved into the left lane and accelerated in the traffic jam during a predetermined period of time; increasing the probability of the traffic congestion in the leftmost lane of the plurality of lanes based on the information that no vehicles have moved into and accelerated into a left lane in the traffic congestion during the predetermined time period; 16. The method of claim 15, further comprising:

17. identifying a first vehicle and a second vehicle in the traffic jam; Obtaining information that the first vehicle has moved into a left lane and accelerated; Obtaining information that the second vehicle has moved into a right lane and accelerated; increasing the probability of traffic congestion in one or more intermediate lanes of the plurality of lanes based on the information that the first vehicle has moved into the left lane and accelerated and the second vehicle has moved into the right lane and accelerated; 16. The method of claim 15, further comprising:

18. Identifying vehicles that are not in a traffic jam; Obtaining information that the vehicle has moved into a left lane and decelerated; increasing the probability of traffic congestion in the leftmost lane of the plurality of lanes based on the information that the vehicle has moved into the left lane and decelerated; 16. The method of claim 15, further comprising:

19. identifying a first vehicle and a second vehicle that are not in a traffic jam; Obtaining information that the first vehicle has moved into a left lane and decelerated; Obtaining information that the second vehicle has moved into a right lane and decelerated; increasing the probability of traffic congestion in one or more intermediate lanes of the plurality of lanes based on the information that the first vehicle has moved into the left lane and decelerated and the second vehicle has moved into the right lane and decelerated; 16. The method of claim 15, further comprising:

20. Obtaining a number of lane changes by the vehicle before the vehicle enters a traffic jam; obtaining a number of lane changes by the vehicle after the vehicle enters the traffic jam; adjusting the lane-level traffic congestion distribution based on a number of lane changes by the vehicle before the vehicle enters the traffic congestion and a number of lane changes by the vehicle after the vehicle enters the traffic congestion; 16. The method of claim 15, further comprising:

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