System and method for predicting traffic signal phases and timing

Abstract

The present disclosure provides a system and method for predicting traffic signal phases and timing. A traffic signal phase and timing (SpaT) prediction system is disclosed. The system may include a transceiver configured to receive historical traffic signal information associated with traffic lights and real-time traffic signal information. The system may also include a memory configured to store training data and a trained machine model. The trained machine model may be trained using the training data including the historical traffic signal information. The system may also include a processor configured to execute instructions stored in the trained machine model to predict traffic signal information (e.g., SPaT) associated with a future traffic signal period based on the real-time traffic signal information. The processor may also output the traffic signal information associated with the future traffic signal period to a vehicle.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for predicting traffic signal phase and timing of traffic lights based on historical and real-time traffic signal information. Background Art

[0002] It is known that vehicles consume a significant amount of fuel and / or battery power at traffic lights when the traffic signal phase is red or yellow. If the vehicle spends less time at the traffic light waiting for the traffic signal phase to turn green, or if the vehicle passes the traffic light when the traffic signal phase is green, a significant improvement in vehicle operating efficiency may be achieved.

[0003] To achieve higher vehicle operating efficiency, the traffic signal phase and timing (SPaT) associated with traffic lights must be accurately predicted. Conventional SPaT prediction systems are able to predict SPaT when traffic lights operate on a fixed schedule; however, conventional systems have limited utility in predicting SPaT for traffic lights that are dynamically actuated based on traffic conditions.

[0004] Therefore, there is a need for a system that accurately predicts the SPaT of a dynamically actuated traffic signal. Summary of the invention

[0005] The present disclosure describes a traffic signal phase and timing (SPaT) prediction system configured to predict traffic signal information (e.g., SPaT) associated with a traffic signal for a future (e.g., next) traffic signal cycle. The system may predict SPaT information for a future traffic signal cycle based on historical and real-time traffic signal information associated with the traffic signal. The historical traffic signal information may be SPaT information associated with a traffic signal within a first predefined count of a predefined past duration or traffic signal cycle. The real-time traffic signal information may be SPaT information associated with a traffic signal within a second predefined count of a "recent" traffic signal cycle. The first predefined count may be significantly greater than the second predefined count.

[0006] The system may include a trained machine module that may be trained using historical traffic signal information and a supervised machine learning algorithm (e.g., a Gaussian process regression (GPR) supervised machine learning algorithm). The system may input real-time traffic signal information into the trained machine module to output predicted SPaT information for future traffic signal cycles.

[0007] In an exemplary aspect, the SPaT information for a future traffic signal cycle can include the expected timing or duration of the green and red phases of the traffic signal, and the expected total duration of the future traffic signal cycle. In response to the SPaT information for predicting the future traffic signal cycle, the system can transmit the SPaT information to the vehicle.

[0008] The vehicle may obtain SPaT information from the system, and may control vehicle movement based on the SPaT information and real-time vehicle information (e.g., current vehicle speed toward a traffic light, vehicle geographic location, etc.). For example, based on the SPaT information, real-time vehicle information, and the geographic location of the traffic light, the vehicle may determine whether the vehicle can reach a traffic light that is in a green light phase or a red light phase. In response to determining that the vehicle may not be able to reach the traffic light that is in a green light phase at the current vehicle speed, the vehicle may modify (e.g., increase or decrease) the vehicle speed, or maintain its current speed, so that the vehicle can pass the traffic light that is in a green light phase.

[0009] The present disclosure discloses a system that assists a vehicle to pass a traffic light in a green phase, thereby saving vehicle energy that would have been consumed if the vehicle had waited at a traffic light in a red phase. In addition, the system predicts SPaT information for future traffic signal cycles based on both historical and real-time traffic signal information; therefore, the prediction is more accurate than the prediction output by a conventional SPaT prediction system.

[0010] These and other advantages of the present disclosure are provided in detail herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Detailed description is set forth with reference to the accompanying drawings. The use of the same reference numerals may indicate similar or identical items. Various embodiments may utilize elements and / or components other than those shown in the accompanying drawings, and some elements and / or components may not be present in various embodiments. The elements and / or components in the accompanying drawings are not necessarily drawn to scale. Throughout this disclosure, singular and plural terms may be used interchangeably, depending on the context.

[0012] Figure 1 An example environment is depicted in which techniques and structures for providing the systems and methods disclosed herein may be implemented.

[0013] Figure 2 Depicted is a block diagram of an example traffic signal phase and timing (SPaT) prediction system and vehicle according to the present disclosure.

[0014] Figure 3 Depicted is a schematic diagram of a trained machine module in accordance with the present disclosure.

[0015] Figure 4 Depicted are example messages displayed on a vehicle infotainment system in accordance with the present disclosure.

[0016] Figure 5 Depicted is a flow chart of an example method for predicting SPaT according to the present disclosure. DETAILED DESCRIPTION

[0017] The present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the disclosure are shown and which are not intended to be limiting.

[0018] Figure 1 An example environment 100 is depicted in which techniques and structures for providing the systems and methods disclosed herein may be implemented. The environment 100 may include a vehicle 102 that may travel on a road including one or more traffic lights 104 (or traffic lights 104). The vehicle 102 may take the form of any passenger or commercial vehicle, such as, for example, a car, a work vehicle, a crossover vehicle, a truck, a van, a minivan, a taxi, a bus, etc. The vehicle 102 may be a manually driven vehicle, and / or may be configured to operate in a partially or fully autonomous mode, and may include any powertrain, such as a gasoline engine, one or more electric actuation motors, a hybrid powertrain, etc.

[0019] The traffic light 104 may be configured to operate on a fixed schedule, or may be dynamically actuated by a traffic light control server (not shown) based on traffic, weather, and the like conditions on the road near the traffic light 104. In some aspects, when the traffic light 104 operates on a fixed schedule, the traffic signal phase (e.g., a green light phase when the traffic light 104 emits green light, a yellow light phase when the traffic light 104 emits yellow light, or a red light phase when the traffic light 104 emits red light) may follow a fixed schedule. For example, when the traffic light 104 operates on a fixed schedule, the traffic light 104 may be in a green light phase for 90 seconds, then in a yellow light phase for 5 seconds, and then in a red light phase for 60 seconds. In this case, the timing or duration of each phase (e.g., a green light phase of 90 seconds, a yellow light phase of 5 seconds, and a red light phase of 60 seconds) remains constant or fixed, regardless of the traffic flow or vehicle flow on the road. In addition, in this case, the total duration of the "traffic signal cycle" may also remain constant. In some aspects, a traffic signal cycle as described in the present disclosure refers to a period of traffic signal operation that begins with the beginning of a green light phase and ends at the beginning of the next green light phase. Continuing with the example described below, when the traffic signal 104 can operate according to a fixed schedule, the total duration of the traffic signal cycle associated with the traffic signal 104 can be 155 seconds (i.e., the sum of 90 seconds + 60 seconds + 5 seconds).

[0020] On the other hand, when the traffic signal 104 can be dynamically actuated (e.g., by a traffic signal control server) based on traffic on the road, the actuation of the traffic signal phase (i.e., green light phase, red light phase, or yellow light phase) may not follow a fixed pattern. In addition, the duration that the traffic signal 104 can operate in each phase may also be unfixed. Furthermore, in this case, the total duration of the traffic signal cycle may or may not be fixed. For the description of the present disclosure, it is assumed that the traffic signal 104 is a dynamically actuated traffic signal. In addition, for simplicity, the red light phase and the yellow light phase are considered to be the red light phase.

[0021] In addition to the vehicle 102 and the traffic light 104, the environment 100 may also include a traffic signal phase and timing (SPaT) prediction system 106 (system 106). The system 106 may be part of the vehicle 102, or may be a separate system hosted on a server (not shown). If the system 106 is hosted on a server, the system 106 may be communicatively coupled to the vehicle 102, such as Figure 1 as shown in .

[0022] The system 106 may be an artificial intelligence / machine learning (AI / ML) based system that may be configured to predict traffic signal information associated with future traffic signal cycles of a plurality of traffic lights (including the traffic light 104). Specifically, the system 106 may be an AI / ML based system that may be configured to predict information associated with the SPaT of a future traffic signal cycle of the traffic light 104 based on historical traffic signal information and real-time traffic signal information associated with the traffic light 104. For example, the system 106 may predict the total duration, the phase actuation pattern, and the timing or duration associated with each phase of a future (e.g., next) traffic signal cycle of the traffic light 104 based on historical and real-time traffic signal information associated with the traffic light 104. In response to predicting the SPaT information of the future traffic signal cycle, the system 106 may transmit the SPaT information to the vehicle 102. The vehicle 102 may use the SPaT information to increase or decrease vehicle speed, change travel direction, etc., so that the vehicle 102 may pass the traffic light 104 when the traffic light 104 may operate in the green light phase. In this manner, the vehicle 102 may not be required to stop at the traffic light 104 , thereby saving energy that may have been consumed waiting at the traffic light 104 .

[0023] In some aspects, the system 106 may generate a trained machine learning module using historical traffic signal information associated with the traffic signal 104 as training data and a supervised machine learning algorithm (e.g., a Gaussian process regression (GPR) supervised machine learning algorithm). The historical traffic signal information may be SPaT information associated with the traffic signal 104 within a predefined past duration (e.g., the past 3 months, 6 months, 12 months, etc.), or a first predefined count of past traffic signal cycles (e.g., 10,000, 15,000, 25,000, etc. cycles). As an example, the historical traffic signal information may include information associated with the total duration of each historical traffic signal cycle, the timing or duration of each phase (green light phase and red light phase) in the historical traffic signal cycle, and the like. In response to generating the trained machine learning module, the system 106 may store the trained machine learning module in a system memory (in Figure 2 Shown as system memory 222).

[0024] The system 106 may also be configured to obtain real-time traffic signal information from a traffic signal control server and / or a traffic signal controller (not shown) installed in the traffic signal 104. In some aspects, the real-time traffic signal information may be SPaT information associated with the traffic signal 104 for a second predefined count of "recent" traffic signal cycles (e.g., the last 5 or 10 traffic signal cycles). As is apparent from the above description, the first predefined count of past traffic signal cycles may be significantly greater than the second predefined count of "recent" traffic signal cycles.

[0025] In response to obtaining the real-time traffic signal information, the system 106 may execute instructions stored in the trained machine learning module and the real-time traffic signal information to predict information associated with the SPaT of a future traffic signal period of the traffic light 104. The system 106 may then transmit the information to the vehicle 102, which may control vehicle movement based on the received information. For example, if the vehicle 102 is an autonomous vehicle, the vehicle 102 may automatically control vehicle movement based on the received SPaT information so that the vehicle 102 may pass the traffic light 104 when the stage may be green. On the other hand, if the vehicle 102 is a manually driven vehicle or a partially autonomous vehicle, the vehicle 102 may display a signal in the vehicle infotainment system (in the example of FIG. 1 ) based on the received SPaT information. Figure 2 The vehicle movement recommendation may be displayed on the vehicle infotainment system 216 (shown in FIG. 1 ). The vehicle movement recommendation may include a recommendation to increase or decrease the vehicle speed to assist the vehicle 102 in passing the traffic light 104 that is in the green phase. The vehicle driver (not shown) may see or hear the vehicle movement recommendation on the vehicle infotainment system and may control the vehicle movement accordingly.

[0026] Combined with the following Figure 2 Further details of the system 106 and the vehicle 102 are described.

[0027] The vehicle 102, system 106, and vehicle driver implement and / or perform operations as described herein in the present disclosure in accordance with the owner's manual and safety guidelines. In addition, any actions taken by the vehicle or vehicle driver should comply with all rules specific to the location (e.g., federal, state, country, city, etc.) and operation of the vehicle 102. Notifications or recommendations such as those provided by the vehicle 102 or system 106 should be considered suggestions and followed only in accordance with any rules specific to the location and operation of the vehicle 102.

[0028] Figure 2 A block diagram of an example traffic signal phase and timing (SPaT) prediction system 200 (system 200) and a vehicle 202 according to the present disclosure is depicted. System 200 may be the same as system 106, and vehicle 202 may be combined with the above. Figure 1 The vehicle 102 described above is the same as the vehicle 102 described above. Figure 2 When Figure 3 and Figure 4 .

[0029] The system 200 and the vehicle 202 may be communicatively coupled to each other via one or more networks 204 (or networks 204). The system 200 and the vehicle 202 may also be communicatively coupled to one or more servers 206 (or servers 206) via the networks 204. In some aspects, the servers 206 may be part of a cloud-based computing infrastructure and may be associated with and / or include a telematics service delivery network (SDN) that provides services to the vehicle 202 and other vehicles that may be part of a commercial vehicle fleet ( Figure 2 In another aspect, the server 206 may be a traffic signal light control server, which may be configured to store historical traffic signal information associated with a plurality of traffic signals (including the traffic signal light 104), and transmit the historical traffic signal information to the system 200 at a predefined frequency or when the system 200 transmits a request to extract / obtain the historical traffic signal information to the server 206. The server 206 may also be configured to determine real-time traffic signal information associated with a plurality of traffic signals (including the traffic signal light 104), and transmit the real-time traffic signal information to the system 200 when the system 200 transmits a request to extract / obtain the real-time traffic signal information to the server 206. Figure 1The concepts of historical traffic signal information and real-time traffic signal information are described. In some aspects, the server 206 can transmit the historical traffic signal information and the real-time traffic signal information associated with the traffic signal 104 to the system 200 via the network 204 .

[0030] The network 204 illustrates an example communication infrastructure in which the connected devices discussed in various embodiments of the present disclosure may communicate. The network 204 may be and / or include the Internet, a private network, a public network, or other configurations operating using any one or more known communication protocols, such as, for example, Transmission Control Protocol / Internet Protocol (TCP / IP), BLE, Wi-Fi based on the Institute of Electrical and Electronics Engineers (IEEE) standard 802.11, ultra-wideband (UWB), and cellular technologies such as time division multiple access (TDMA), code division multiple access (CDMA), high-speed packet access (HSPDA), long-term evolution (LTE), global system for mobile communications (GSM), and fifth generation (5G), to name a few examples.

[0031] The vehicle 202 may include a plurality of units, including but not limited to a vehicle transceiver 208, a vehicle memory 210, a vehicle processor 212, a vehicle control unit (VCU) 214, an infotainment system 216, etc., which may be communicatively coupled to one another. The vehicle transceiver 208 may be configured to receive / transmit data / information / signals from / to external systems and devices via the network 204. For example, the vehicle transceiver 208 may transmit or receive information / signals to / from the system 200 and / or the server 206. The vehicle transceiver 208 may also be configured to transmit / receive signals / information to / from the above-mentioned vehicle units.

[0032] The VCU 214 may include a plurality of electronic control units (ECUs, not shown) and / or a plurality of vehicle sensors (not shown) that may enable the VCU 214 to control aspects of vehicle operation and implement one or more instruction sets received from the vehicle processor 212. For example, when the vehicle 202 may be a fully or partially autonomous vehicle, the VCU 214 may control vehicle speed, direction of movement of the vehicle, etc. The VCU 214 may also be configured to determine real-time vehicle information. In some aspects, the real-time vehicle information may include real-time (or current) vehicle speed, direction of movement of the vehicle, geographic location of the vehicle, etc.

[0033] The vehicle processor 212 may be configured to communicate with one or more memory devices (e.g., the vehicle memory 210 and / or Figure 2The vehicle processor 212 may utilize the vehicle memory 210 to store programs and / or store data in code to perform various aspects of the present disclosure. The vehicle memory 210 may be a non-transitory computer-readable storage medium or memory that stores program code that enables the vehicle processor 212 to perform operations in accordance with the present disclosure. The vehicle memory 210 may include any one or combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.), and may include any one or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).

[0034] The infotainment system 216 may include a touch screen interface portion, and may include voice recognition features, biometric recognition capabilities that may identify a user based on facial recognition, voice recognition, fingerprint recognition, or other biometric recognition means. In other aspects, the infotainment system 216 may also be configured to receive user commands via the touch screen interface portion, and / or output or display notifications (including visual and / or audible alert notifications), navigation maps, recommendations, messages, etc. on the touch screen interface portion.

[0035] The system 200 may include a plurality of units, including but not limited to a system transceiver 218, a system processor 220, and a system memory 222. The system transceiver 218 may be configured to receive / transmit information / signals from / to an external system or device via the network 204. For example, the system transceiver 218 may receive / transmit information / signals from / to the vehicle 202 and the server 206.

[0036] The system processor 220 may utilize the system memory 222 to store programs and / or store data in code form to perform various operations according to the present disclosure. The system memory 222 may be a non-transitory computer-readable storage medium or memory that stores program code that may enable the system processor 220 to perform operations according to the present disclosure. In some aspects, the system memory 222 may include a plurality of databases and modules, including but not limited to a training data database 224 (or training data 224), a real-time traffic signal information database 226 (or real-time traffic signal database 226), a machine learning module 228, and a trained machine module 230. The functionality of these memory databases and modules may be understood in conjunction with the description provided below.

[0037] In operation, the system transceiver 218 may receive historical traffic signal information associated with the traffic signal 104 from the server 206 via the network 204. In some aspects, the system transceiver 218 may receive the historical traffic signal information from the server 206 when the system processor 220 transmits a request to extract / obtain the historical traffic signal information to the server 206 via the system transceiver 218. The system transceiver 218 may additionally receive the geographic location of the traffic signal 104 from the server 206. The system transceiver 218 may transmit the traffic signal geographic location to the system memory 222 for storage purposes.

[0038] In response to obtaining the historical traffic signal information associated with the traffic signal 104 , the system transceiver 218 can transmit the historical traffic signal information to the training data 224 for storage purposes. In other words, the training data 224 can include the historical traffic signal information associated with the traffic signal 104 .

[0039] In some aspects, the system processor 220 may be an AI / ML-based processor that may generate a trained machine module 230 by using the training data 224 (i.e., historical traffic signal information) and a supervised machine learning algorithm pre-stored in the machine learning module 228. In an exemplary aspect, the supervised machine learning algorithm may be a Gaussian process regression (GPR) supervised machine learning algorithm. In response to generating the trained machine module 230, the system processor 220 may execute instructions stored in the trained machine module 230 to "predict" the traffic signal information or SPaT information for future traffic signal cycles associated with the traffic light 104 by using the real-time traffic signal information as input. In other words, when the trained machine module 230 may be generated, the system processor 220 may obtain the real-time traffic signal information from the server 206 and input the real-time traffic signal information to the trained machine module 230 to predict the SPaT information for future traffic signal cycles.

[0040] As will be appreciated by those of ordinary skill in the art, machine learning is an application of artificial intelligence (AI) using which a system or processor (e.g., system processor 220) may have the ability to automatically learn and enhance based on experience without explicit programming. Machine learning focuses on using data and algorithms to mimic the way humans learn. In some aspects, machine learning algorithms may be created for classification and / or prediction. Machine learning-based systems may be used for a variety of applications, including but not limited to speech recognition, image or video processing and statistical analysis, probability determination, traffic signal phase and timing determination, and the like.

[0041] Machine learning can be of various types based on the data or signals available to the learning system. For example, machine learning methods can include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Supervised learning is a method that can be supervised by humans. In this method, a machine learning algorithm can use labeled training data (e.g., training data 224) and defined variables. In the case of supervised learning, both the input and output of the algorithm can be specified / defined, and the algorithm can be trained to accurately classify the data and / or predict the results.

[0042] Generally, supervised learning can be of two types: "regression" and "classification". In classification learning, a learning algorithm can help to divide a data set into multiple categories based on different parameters. In this case, a computer program can be trained on a training data set, and based on the training, the computer program can classify the input data into different categories. Some of the known methods used in classification learning include logistic regression, K-nearest neighbors, support vector machine (SVM), kernel SVM, naive Bayes, decision tree classification, and random forest classification.

[0043] In regression learning, a learning algorithm can predict output values ​​that may be of a continuous nature or real value. Some known methods used in regression learning include simple linear regression, multivariate linear regression, polynomial regression, support vector regression, decision tree regression, and random forest regression. In the present disclosure, the machine learning module 228 can be based on a Gaussian process regression (GPR) supervised machine learning algorithm, and the system processor 220 can execute the GPR supervised machine learning algorithm and the training data 224 (i.e., historical traffic signal information) stored in the machine learning module 228 to generate a trained machine module 230.

[0044] Unsupervised learning is a method that involves algorithms that can be trained on unlabeled data. Unsupervised learning algorithms can analyze data by themselves and find patterns in the input data. Furthermore, semi-supervised learning is a combination of supervised learning and unsupervised learning. Semi-supervised learning algorithms involve labeled training data; however, semi-supervised learning algorithms can still find patterns in the input data. Reinforcement learning is a multi-step or dynamic process. The model is similar to supervised learning, but may not be trained using sample data. The model can "learn as it goes" by using trial and error. A series of successful outcomes can be reinforced to develop the best recommendation or strategy for a given problem in reinforcement learning.

[0045] As described above, in the present disclosure, the system processor 220 generates the trained machine module 230 using a regression-based supervised learning algorithm by using the training data 224. In other words, the trained machine module 230 may be trained by using the training data 224. When the system 200 may receive new training data, the system processor 220 may continuously modify and update the trained machine module 230 to make the trained machine module 230 more accurate over time. In response to generating the trained machine module 230, the system processor 220 may store the trained machine module 230 in the system memory 222.

[0046] Figure 3 An example schematic diagram of a trained machine module 230 is depicted in FIG. Figure 3 is depicted as a traffic light symbol. In an exemplary aspect, Figure 3 As shown in , the trained machine module 230 can be trained by using multiple sets 302a, 302b, 302n of training data or historical traffic signal information. Specifically, when "training" or generating the trained machine module 230, the system processor 220 can execute the machine learning module 228 to "predict" the SPaT information 304 associated with the future traffic signal cycle by using the set 302a, and then the predicted SPaT information 304 can be compared with the actual SPaT information. Based on the comparison, the system processor 220 can determine the accuracy level of the machine module, and can fine-tune the module to enhance the accuracy. In addition, in response to the fine-tuning, the system processor 220 can similarly cause the machine learning module 228 to predict the SPaT information 306, 308 of the future traffic signal cycle by using the sets 302b, 302n and perform the comparison as described above until the accuracy level of the machine module can be above a predefined threshold. In response to determining that the level of accuracy is above the predefined threshold, system processor 220 may deem the machine module “trained” and may store the trained machine module 230 in system memory 222 , as described above.

[0047] In response to generating and storing the trained machine module 230, the system processor 220 may obtain real-time traffic signal information associated with the traffic signal 104 from the server 206 via the system transceiver 218 and / or from a traffic signal controller installed in the traffic signal 104 via the network 204. Specifically, in this case, the system processor 220 may transmit a request to obtain the real-time traffic signal information to the server 206 and / or the traffic signal controller via the system transceiver 218. In response to transmitting the request, the system transceiver 218 may receive the real-time traffic signal information, and may transmit the received information to the system processor 220 and the real-time traffic signal database 226 for storage purposes.

[0048] In response to obtaining the real-time traffic signal information from the system transceiver 218, the system processor 220 may execute instructions stored in the trained machine module 230 to predict traffic signal information (i.e., SPaT information) associated with a future traffic signal cycle of the traffic signal 104 based on the real-time traffic signal information. Figure 3 As shown in , the system processor 220 may input real-time traffic signal information 310 associated with the traffic signal 104 (e.g., SPaT information for the past 8 or 10 traffic signal cycles) into the trained machine module 230 and execute instructions stored in the trained machine module 230 to predict SPaT information 312 associated with a future (or next) traffic signal cycle. As an example, the system processor 220 may input the timing and duration associated with the green phase of the traffic signal 104 for the past 10 traffic signal cycles into the trained machine module 230 and output a predicted or estimated timing and duration associated with the green phase of the traffic signal 104 for the future (e.g., next) traffic signal cycle.

[0049] An example mathematical expression associated with the trained machine module 230 is shown below. The mathematical expression should not be construed as limiting the scope of the present disclosure, and is provided as an example expression that may be associated with the trained machine module 230. Any other mathematical expression may also be associated with the trained machine module 230 without departing from the scope of the present disclosure.

[0050] A Gaussian process (associated with the trained machine module 230) may be defined as: f(x) ≈ GP[m(x), k(x, x')], where m(x) may be a mean function and k(x, x') may be a covariance function of (x, x'). Further, in an exemplary aspect, m(x) = E[f(x)] and k(x, x') = E[(f(x) - m(x))(f(x') - m(x'))], where E[X] represents the expected value of X.

[0051] Example steps performed by the system processor 220 and / or the trained machine module 230 to model SPaT information using Gaussian process regression may include a first step in which a model or trained machine module 230 (or "model formula") may be formed, where y = f(x), f(x) ~ GP[m(x), k(x, x'|θ)], where y is the model and θ is the model parameter vector. The model parameters θ may be estimated using maximum likelihood.

[0052] As a second step, the last “k” durations (d n-k ~d n-1) to predict the nth duration d n (e.g., duration of the green phase). In some aspects, the input to the model can be x n =[d n-k …d n -k+1 …d n-1 ], and the output from the model can be In addition, as mentioned above, the model can be trained using the collated historical SPaT data D (or training dataset D), where D = [X, Y]. Here, X = [x 1 ,x 2 ,x 3 …], and Y = [y 1 ,y 2 ,y 3 …].

[0053] In response to predicting the SPaT information 312 associated with a future traffic signal period, the system processor 220 may output / transmit the SPaT information 312 to the vehicle transceiver 208 via the network 204 via the system transceiver 218. The system processor 220 may also transmit the geographic location of the traffic signal 104 (which the system 200 may have received from the server 206, as described above) to the vehicle transceiver 208 via the system transceiver 218.

[0054] The vehicle transceiver 208 may receive the SPaT information 312 and the traffic light geographic location from the system transceiver 218, and may transmit the received information to the vehicle memory 210 for storage purposes, and to the vehicle processor 212. The vehicle processor 212 may obtain the SPaT information 312 and the traffic light geographic location from the vehicle transceiver 208 or the vehicle memory 210, and may also obtain real-time vehicle information from the VCU 214. As described above, the real-time vehicle information may include real-time (or current) vehicle speed, vehicle moving direction, vehicle geographic location, etc. In response to obtaining the above information, the vehicle processor 212 may correlate the SPaT information 312 and the traffic light geographic location with the real-time vehicle information to determine whether the vehicle 202 will pass the traffic light 104 in the green phase if the vehicle 202 continues to travel at the current speed (or in the same direction of travel). In response to determining that the vehicle 202 cannot pass the traffic light 104 in the green phase based on the correlation, the vehicle processor 212 may generate a command signal. The vehicle processor 212 may then output / transmit the generated command signal to the infotainment system 216 and / or the VCU 214 .

[0055] In response to receiving the command signal from the vehicle processor 212, the infotainment system 216 may output (audibly or visually) a predefined message based on the command signal. Figure 4 As shown in view 402 of , the infotainment system 216 may display “Reduce vehicle speed to pass next traffic light in green phase”. In response to hearing or seeing the message, the vehicle driver (not shown) may reduce vehicle speed (while taking existing traffic into account and complying with local rules and laws) to pass the traffic light 104 in the green phase. In this way, the system 200 assists the vehicle 202 in passing the traffic light 104 in the green phase, thereby saving vehicle energy that may have been consumed if the vehicle 202 were to wait at the traffic light 104 in the red phase. While the above examples describe aspects in which the command signal instructs the vehicle driver to reduce vehicle speed, in some aspects, the command signal may instruct the vehicle driver to increase vehicle speed and / or change the direction of vehicle movement (e.g., take a detour) to assist the vehicle 202 in passing the traffic light 104 in the green phase, while taking existing traffic into account and complying with local rules and laws.

[0056] In another aspect, when the vehicle 202 may be a fully autonomous or partially autonomous vehicle, in response to obtaining a command signal from the vehicle processor 212, the VCU 214 may autonomously control vehicle movement (e.g., reduce or increase vehicle speed or change vehicle movement direction, while complying with all local rules and laws) so that the vehicle 202 can pass the traffic light 104 in the green phase. In some embodiments, the vehicle may be aware of certain local traffic laws, such as speed limits, and take them into account when generating the output. For example, the system 200 may incorporate a maximum legal speed in the output, or recommend a reduced speed when the vehicle will not be able to pass the next signal light in the green phase without exceeding the local speed limit.

[0057] Although the above description describes an aspect in which the system processor 220 generates a trained machine module 230 and predicts the SPaT information 312 of the traffic light 104 by executing instructions stored in the trained machine module 230, it can be appreciated by one of ordinary skill in the art that the system processor 220 can generate similar trained machine modules for multiple traffic lights (one trained machine module for each traffic light). The system processor 220 can then determine the SPaT information associated with each traffic light in the travel path of the vehicle 202, and can transmit the determined SPaT information to the vehicle 202. Then, as described above, the vehicle 202 can control vehicle movement based on the SPaT information so that the vehicle 202 can pass most (or all) of the traffic lights in the vehicle's route during the corresponding green light phase.

[0058] Figure 5 A flow chart of an example method 500 for predicting SPaT according to the present disclosure is depicted. Figure 5 The following process is exemplary and is not limited to the steps described below. Furthermore, alternative embodiments may include more or fewer steps than shown or described herein, and may include the steps in an order different from that described in the example embodiments below.

[0059] The method 500 begins at step 502. At step 504, the method 500 may include obtaining, by the system processor 220, real-time traffic signal information from the server 206 or a traffic light controller. At step 506, the method 500 may include executing, by the system processor 220, instructions stored in the trained machine module 230 to predict the SPaT 312 based on the real-time traffic signal information. At step 508, the method 500 may include outputting, by the system processor 220, the SPaT 312 to the vehicle 202.

[0060] Method 500 may end at step 510 .

[0061] In the above disclosure, reference has been made to the accompanying drawings that form a part of the above disclosure, which illustrate specific embodiments in which the present disclosure can be practiced. It should be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. References in this specification to "one embodiment," "embodiment," "example embodiment," etc. indicate that the described embodiment may include specific features, structures, or characteristics, but each embodiment may not necessarily include the specific features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when features, structures, or characteristics are described in conjunction with an embodiment, whether or not explicitly described, those skilled in the art will recognize such features, structures, or characteristics in conjunction with other embodiments.

[0062] In addition, where appropriate, the functions described herein may be performed in one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and programs described herein. Certain terms are used throughout the specification and claims to refer to specific system components. As will be appreciated by those skilled in the art, components may be referred to by different names. This document is not intended to distinguish between components that have different names but the same function.

[0063] It should also be understood that the word "example" as used herein is intended to be non-exclusive and non-limiting in nature. More specifically, the word "example" as used herein indicates one of several examples, and it should be understood that no undue emphasis or preference is placed on the specific example described.

[0064] Computer-readable media (also referred to as processor-readable media) include any non-transitory (e.g., tangible) media that participate in providing data (e.g., instructions) that can be read by a computer (e.g., by a processor of a computer). Such media may take many forms, including, but not limited to, non-volatile media and volatile media. A computing device may include computer-executable instructions, where the instructions may be executable by one or more computing devices (such as those listed above) and stored on a computer-readable medium.

[0065] With respect to the processes, systems, methods, heuristics, etc. described herein, it should be understood that although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes may be practiced by performing the described steps in an order different from that described herein. It should also be understood that certain steps may be performed simultaneously, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of the processes herein are provided for the purpose of illustrating various embodiments and should in no way be construed as limiting the claims.

[0066] Therefore, it should be understood that the above description is intended to be illustrative rather than restrictive. Upon reading the above description, many embodiments and applications other than the examples provided will be apparent. The scope should not be determined with reference to the above description, but should be determined with reference to the entire scope of the appended claims and equivalents to which such claims are entitled. It is anticipated and expected that the technology discussed herein will develop in the future, and the disclosed systems and methods will be incorporated into such future embodiments. In short, it should be understood that the present application is capable of modification and change.

[0067] Unless explicitly indicated to the contrary in this article, all terms used in the claims are intended to be given their ordinary meanings as understood by the skilled person described herein. Specifically, unless the claim states an explicit limitation to the contrary, the use of singular articles such as "one", "the", "said" and the like should be interpreted as describing one or more of the indicated elements. Unless otherwise specifically stated or understood in other ways within the context when used, conditional language such as, in particular, "can", "may", "can" or "may" is generally intended to express that certain embodiments may include certain features, elements and / or steps, while other embodiments may not include certain features, elements and / or steps. Therefore, such conditional language is generally not intended to imply that one or more embodiments require each feature, element and / or step in any way.

[0068] In one aspect of the invention, each of the historical traffic signal information, the real-time traffic signal information, and the traffic signal information associated with the future traffic signal cycle includes information associated with a traffic signal phase and timing (SPaT) associated with each traffic signal cycle.

[0069] In one aspect of the invention, the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of the most recent traffic signal cycle.

[0070] In one aspect of the invention, the method comprises generating the trained machine model by using the training data and a Gaussian process regression (GPR) supervised machine learning algorithm.

[0071] In one aspect of the present invention, obtaining the real-time traffic signal information includes obtaining the real-time traffic signal information from a traffic light control server.

[0072] In one aspect of the present invention, outputting the traffic signal information includes outputting the traffic signal information to a vehicle.

Claims

1. A system comprising: A system transceiver, the system transceiver being configured to: receiving historical traffic signal information associated with the traffic signal; as well as receiving real-time traffic signal information associated with said traffic signal for a first predefined count of recent traffic signal cycles; a system memory configured to store training data and a trained machine model, wherein the trained machine model is trained using the training data including the historical traffic signal information; a system processor communicatively coupled to the system transceiver and the system memory, wherein the system processor is configured to: obtaining the real-time traffic signal information from the system transceiver; executing instructions stored in the trained machine model to predict traffic signal information associated with a future traffic signal period based on the real-time traffic signal information; as well as The traffic signal information associated with the future traffic signal period is output.

2. The system of claim 1 , wherein each of the historical traffic signal information, the real-time traffic signal information, and the traffic signal information associated with the future traffic signal cycle includes information associated with a traffic signal phase and timing (SPaT) associated with each traffic signal cycle.

3. The system of claim 1, wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of a most recent traffic signal cycle.

4. The system of claim 1, wherein the system processor is further configured to generate the trained machine model by using the training data and a Gaussian process regression (GPR) supervised machine learning algorithm.

5. The system of claim 1, wherein the system transceiver receives the historical traffic signal information and the real-time traffic signal information from a traffic signal control server.

6. The system of claim 1, wherein the system processor outputs the traffic signal information associated with the future traffic signal period to a vehicle.

7. A vehicle comprising: a vehicle transceiver configured to receive, from a server, traffic signal information associated with a future traffic signal cycle of a traffic signal, wherein the traffic signal information associated with the future traffic signal cycle is based on historical traffic signal information associated with the traffic signal and real-time traffic signal information associated with the traffic signal for a first predefined count of recent traffic signal cycles; a vehicle control unit configured to determine real-time vehicle information; as well as a vehicle processor communicatively coupled to the vehicle transceiver and the vehicle control unit, wherein the vehicle processor is configured to: obtaining the traffic signal information associated with the future traffic signal period from the vehicle transceiver and obtaining the real-time vehicle information from the vehicle control unit; generating a command signal based on the traffic signal information and the real-time vehicle information; as well as The command signal is output.

8. The vehicle of claim 7, wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, wherein the second predefined count of traffic signal cycles is greater than the first predefined count of the most recent traffic signal cycle.

9. The vehicle of claim 7, wherein each of the historical traffic signal information, the real-time traffic signal information, and the traffic signal information associated with the future traffic signal cycle includes information associated with a traffic signal phase and timing (SPaT) associated with each traffic signal cycle.

10. The vehicle of claim 7, wherein the traffic signal information associated with the future traffic signal period is determined using a Gaussian Process Regression (GPR) supervised machine learning algorithm.

11. The vehicle of claim 7, wherein the real-time vehicle information includes a current vehicle speed and a vehicle moving direction.

12. The vehicle of claim 7, wherein the vehicle processor is further configured to: correlating the traffic signal information associated with the future traffic signal period with the real-time vehicle information; and The command signal is generated based on the correlation.

13. The vehicle of claim 7, wherein the vehicle processor outputs the command signal to a vehicle infotainment system, and wherein the vehicle infotainment system outputs a predefined message in response to receiving the command signal.

14. The vehicle of claim 7, wherein the vehicle processor outputs the command signal to the vehicle control unit, wherein the vehicle control unit autonomously controls vehicle movement in response to receiving the command signal.

15. A method comprising: obtaining, by a processor, real-time traffic signal information, wherein the real-time traffic signal information is associated with a traffic signal of a first predefined count of a recent traffic signal cycle; executing, by the processor, instructions stored in a trained machine model to predict traffic signal information associated with a future traffic signal cycle based on the real-time traffic signal information, wherein the trained machine model is trained using training data including historical traffic signal information associated with the traffic signal; and The traffic signal information associated with the future traffic signal period is output by the processor.

Images