System and method for predicting vehicle passage probability during green light phase

Abstract

The present disclosure provides a system and method for predicting the probability of vehicle passage during a green light phase. A method for determining a probability of a first vehicle crossing a traffic light during a green light phase is disclosed. A method may include obtaining information associated with a green light duration, first vehicle information, and second vehicle information. The first vehicle information may include a historical speed profile rate of change associated with the first vehicle, and the second vehicle information may include a first duration required for the second vehicle to pass through the traffic light. The method may also include estimating a second duration required for the first vehicle to pass through the traffic light based on the first vehicle information and the second vehicle information, and comparing the second estimated duration to the green light duration. A method may determine a probability that the first vehicle traverses the traffic light for the green light duration based on the comparison, and transmit a notification to indicate the probability.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for predicting vehicle passing probability during a green phase of a traffic light. Background Art

[0002] When a vehicle arrives near a traffic light, the vehicle driver may not know when the traffic light may change its phase from red to green, or vice versa. Furthermore, while the vehicle may be traveling toward the traffic light, the vehicle driver may not know whether the vehicle can cross the traffic light during the green phase. Since the vehicle driver cannot know such information in advance, the vehicle driver may move quickly, slow down, and stop near the traffic light according to the real-time traffic light phase. Such vehicle movement may cause inconvenience to other vehicle drivers. Summary of the invention

[0003] The present disclosure describes a vehicle (e.g., a first vehicle) configured to determine a probability of a vehicle passing through a green light phase of a traffic light. The first vehicle may obtain first vehicle information (associated with the first vehicle), second vehicle information (associated with a second vehicle ahead of the first vehicle in a vehicle queue near the traffic light), and real-time traffic signal information associated with the traffic light. The first vehicle may be configured to estimate a first duration that may be required for the first vehicle to pass through the traffic light based on the first vehicle information and the second vehicle information. In response to estimating the first duration, the first vehicle may determine the probability of the vehicle passing through the green light phase of the traffic light based on the real-time traffic signal information and the estimated first duration.

[0004] In some aspects, the first vehicle information may include a historical speed profile change rate associated with the first vehicle. The historical speed profile change rate may be different for different vehicle types (e.g., electric vehicles (EVs), fuel cell vehicles (FCVs), diesel-based internal combustion engine (ICE) vehicles, gasoline-based ICE vehicles, vehicles with electric turbochargers, and / or other vehicle types). The second vehicle information may include a second duration that the second vehicle may need to cross the traffic light, a second distance that the second vehicle may have to travel to cross the traffic light. The real-time traffic signal information may include a green light duration associated with a green light phase.

[0005] In some aspects, when the first vehicle arrives near the traffic light, the first vehicle can determine the first vehicle position in the vehicle queue. For example, the first vehicle can determine whether there are other vehicles in front of the first vehicle (for example, by using input from a camera outside the vehicle). In response to determining that there may be no vehicle in front of the first vehicle, the first vehicle can determine that the first vehicle may be in the first position in the queue. In this case, the first vehicle can broadcast the first vehicle position to other vehicles that may be behind the first vehicle so that other vehicles can calculate their corresponding positions in the vehicle queue. The first vehicle can then calculate the first distance that the first vehicle may have to travel to cross the traffic light based on the first vehicle length and the distance / gap between the front portion of the first vehicle and the centerline of the intersection where the traffic light can be placed. The first vehicle can then estimate the first duration based on the first distance and the historical speed change rate associated with the first vehicle, and determine the probability of whether the first vehicle can cross the traffic light in the green light stage based on the first duration and the green light duration. The first vehicle can then broadcast the calculated first distance and first duration to other vehicles located behind the first vehicle.

[0006] On the other hand, when the first vehicle determines that the first vehicle may be placed in the second position (or any other position) in the vehicle queue, the first vehicle may calculate the first distance based on the second distance, the first vehicle length, and the gap between the first vehicle and the second vehicle. In addition, in this case, the first vehicle may estimate the first duration based on the first distance, the second duration, and the historical speed curve change rate, and determine the probability accordingly. In other words, in this case, the first vehicle may estimate the first duration based on the duration that the second vehicle in front of the first vehicle may take to cross the traffic light. In some aspects, if the second vehicle can be an EV, the second vehicle can have a higher speed curve change rate than when the second vehicle can be an ICE vehicle, and thus can pass through the traffic light faster (thereby increasing the probability of the first vehicle passing through the traffic light in the green light phase). It should be noted that the speed of the vehicle and the speed curve of the vehicle should always keep the vehicle within the legal speed limit and always be at a safe speed for given conditions. In another aspect, if the second vehicle is a diesel-based ICE, the second vehicle can have a higher speed curve change rate than when the second vehicle can be a gasoline-based ICE vehicle, and thus can pass through the traffic light faster.

[0007] The present disclosure discloses a system and method for determining the probability of a vehicle passing through a traffic light during the green phase. The system enhances the vehicle driving experience and estimated time of arrival (ETA) of the vehicle driver. The system also enables traffic control models to better estimate traffic flow in cities and urban areas. The system also reduces the likelihood that the vehicle driver will attempt to pass through a yellow light or a red light because they cannot know in advance whether there is a good chance of passing through the traffic light during the green phase.

[0008] These and other advantages of the present disclosure are provided in detail herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1 An example environment is depicted in which techniques and structures for providing the systems and methods disclosed herein may be implemented.

[0011] Figure 2 A block diagram of a system for predicting the probability of a vehicle passing through a traffic light in accordance with the present disclosure is depicted.

[0012] Figure 3 Depicted is a first example snapshot of a plurality of vehicles positioned in a queue near a traffic light in accordance with the present disclosure.

[0013] Figure 4 Depicted is a second example snapshot of a plurality of vehicles positioned in a queue near a traffic light in accordance with the present disclosure.

[0014] Figure 5 Depicted are example messages displayed on a vehicle infotainment system in accordance with the present disclosure.

[0015] Figure 6 A flow chart of an example method for predicting the probability of a vehicle passing through a traffic light according to the present disclosure is depicted. DETAILED DESCRIPTION

[0016] The present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the disclosure are shown and which are not intended to be limiting.

[0017] Figure 1An 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 one or more vehicles 102a, 102b, 102c, etc. (collectively, vehicles 102), which may be traveling 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 autonomous mode, and may include any powertrain, such as, for example, a gasoline engine, one or more electric actuation motors, a hybrid powertrain, etc.

[0018] 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, regardless of the traffic flow or vehicle flow on the road, 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. 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 this disclosure refers to a cycle of traffic signal operation that begins with the start of a green phase and ends at the start of the next green phase.

[0019] On the other hand, when the traffic light 104 can be dynamically actuated based on traffic on the road (e.g., by a traffic light control server), 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. Furthermore, the duration that the traffic light 104 can operate in each phase may also not be fixed. Furthermore, in this case, the total duration of the traffic signal cycle may or may not be fixed.

[0020] For the description of the present disclosure, it is assumed that the traffic light 104 operates on a fixed schedule. In addition, for simplicity, the red light phase and the yellow light phase are considered to be the red light phase.

[0021] In some aspects, each vehicle 102 may be configured to determine a probability that the vehicle 102 will cross a traffic light (e.g., the traffic light 104) during a green phase based on the vehicle's own information, information associated with other vehicles that may be ahead of the vehicle 102 in a queue near the traffic light 104, and traffic signal information associated with the traffic light 104. For example, if vehicle 102a (or "first vehicle") may be located near the traffic light 104 and behind vehicle 102b (or "second vehicle"), vehicle 102a may determine a probability that the vehicle will cross the traffic light 104 during a green phase based on vehicle information associated with vehicles 102a, 102b and traffic signal information associated with the traffic light 104. In response to determining the probability, vehicle 102a may output a notification to the vehicle driver (e.g., via a vehicle human machine interface (HMI) or infotainment system) so that the vehicle driver may move vehicle 102a accordingly. By knowing the probability of vehicle 102a passing through traffic light 104 during the green phase, the vehicle driver may not drive too fast (or suddenly slow down or stop vehicle movement) near traffic light 104, thereby enhancing the convenience of the vehicle driver and other drivers near vehicle 102a.

[0022] In some aspects, in order to determine the above-mentioned probability, the vehicle 102a may store or "pre-store" the vehicle information (e.g., the first vehicle information) associated with the vehicle 102a in the vehicle memory (in Figure 2 The first vehicle information may include, for example, a historical speed profile change rate associated with the vehicle 102a, a first vehicle weight, a first vehicle length, a first vehicle tire condition, a first vehicle propulsion type, and the like.

[0023] In some aspects, the historical speed profile rate of change may be based on (and may indicate) the vehicle type, e.g., whether the vehicle 102a is an electric vehicle (EV), a diesel-based internal combustion engine (ICE) vehicle, a gasoline-based ICE vehicle, a fuel cell vehicle (FCV), etc. When the historical speed rate of change may be greater than a threshold, the historical speed rate of change may indicate that the vehicle 102a may be an EV (because the speed rate of change of an EV is typically greater than that of an ICE vehicle). Similarly, when the historical speed rate of change may be less than a threshold, the historical speed rate of change may indicate that the vehicle 102a may be an ICE vehicle. In addition, the historical speed profile rate of change may be based on weather conditions, road conditions, payload weight, tire health, etc. The vehicle 102a may use the rate of change of the historical speed profile (and other parameters / information) to determine whether the vehicle 102a can pass through the traffic light 104 during the green light phase, as described below.

[0024] The vehicle 102a may also include a processor (e.g., Figure 2 ), which may obtain real-time traffic signal information associated with the traffic signal 104 from a traffic signal controller (not shown) and / or a traffic signal control server installed in the traffic signal 104. The real-time traffic signal information may be traffic signal phase and timing (SPaT) information associated with the traffic signal 104. The SPaT information may include information associated with a current signal phase (e.g., a red light phase, a green light phase, etc.) that may be displayed on the traffic signal 104 and associated timing for each phase (e.g., a green light duration during which the green light may be active / turned on, a red light duration during which the red light may be active / turned on). As an example, the real-time traffic signal information / SPaT information may indicate that the traffic signal 104 may currently be operating in a red light phase, the associated red light phase duration, and a duration associated with a green light phase that may follow the red light phase (or "green light duration").

[0025] In addition to obtaining real-time traffic signal information, the processor may also obtain information associated with other vehicles in the vehicle queue near the traffic signal 104. For example, the processor may obtain information associated with other vehicles that may be located in front of the vehicle 102a (e.g., Figure 1 The second vehicle information may include an estimated duration (e.g., a first estimated duration) required for the second vehicle 102b to cross the traffic light 104 (e.g., to cross the centerline 106 of the intersection where the traffic light 104 may be located). In addition, the second vehicle information may include, for example, a first distance that the second vehicle must travel to cross the centerline 106, a second vehicle propulsion type, and a position of the second vehicle 102b in the vehicle queue.

[0026] The processor may estimate the duration (e.g., second duration) required for the vehicle 102a to cross the traffic light 104 (e.g., center line 106) based on the first vehicle information (including the historical speed profile change rate) and the second vehicle information (including the first estimated duration). In response to estimating the second duration, the processor may compare the second duration with the green light duration. The processor may determine the probability of the vehicle 102a crossing the traffic light 104 within the green light duration based on the comparison, and transmit a notification to the vehicle HMI to indicate the probability. As an example, if the second duration may be significantly less than the green light duration, the processor may transmit a notification indicating that the probability of crossing the traffic light 104 may be high (e.g., greater than a first threshold, such as 80%). On the other hand, if the second duration may be equal to or slightly greater than the green light duration, the processor may transmit a notification indicating that the probability of crossing the traffic light 104 may be low (e.g., less than a second threshold, such as 10% or even 0%). In some aspects, the probability may depend on the difference between the second duration and the green light duration.

[0027] Combined with the following Figure 2 Further details of vehicle 102a are described.

[0028] The vehicle 102 and the 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 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 should be considered suggestions and followed only in accordance with any rules specific to the location and operation of the vehicle 102.

[0029] Figure 2 A block diagram of a system 200 for predicting the probability of a vehicle passing through a traffic light (eg, traffic light 204 described below) according to the present disclosure is depicted. Figure 2 When Figures 3 to 5 . Figure 3 and Figure 4 depicts a first example snapshot and a second example snapshot of a plurality of vehicles positioned in a queue near a traffic light 204, and Figure 5 Depicted is an example message displayed on a vehicle infotainment system.

[0030] The system 200 may include a plurality of vehicles (in a manner of communication) that may be communicatively coupled to one another via one or more networks 206 (or networks 206). Figure 22 is shown as a vehicle 202 (or first vehicle 202), which may be the same as vehicle 102 described above) and a traffic light 204. In an exemplary aspect, the vehicles may communicate with each other via vehicle-to-vehicle (V2V) communication, and the vehicles may communicate with the traffic light 104 via V2X (vehicle-to-external) communication. In some aspects, the first vehicle 202 may be configured to obtain real-time traffic signal information associated with the traffic light 204 from the traffic light 204 via V2X communication.

[0031] The system 200 may also include one or more servers 208 (or servers 208) that may be communicatively coupled to the first vehicle 202 and the traffic light 204 via the network 206. In some aspects, the server 208 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 first vehicle 202 and other vehicles that may be part of a fleet of vehicles ( Figure 2 In some aspects, the server 208 may be a traffic light control server that may be configured to determine real-time traffic signal information associated with a plurality of traffic lights (including the traffic light 204) and transmit the real-time traffic signal information to the first vehicle 202 when the first vehicle 202 may be located near the traffic light 204 or when the first vehicle 202 transmits a request to the server 208 to obtain / obtain the real-time traffic signal information.

[0032] The network 206 illustrates an example communication infrastructure in which the connected devices discussed in various embodiments of the present disclosure may communicate. The network 206 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.

[0033] The first vehicle 202 may include a plurality of units, including but not limited to a transceiver 210, a processor 212, a vehicle control unit (VCU) 214, a detection unit 216, a vehicle HMI or infotainment system 218, a memory 220, etc., which may be communicatively coupled to each other. The transceiver 210 may be configured to receive / transmit data / information / signals from / to external systems and devices via the network 206. For example, the transceiver 210 may transmit or receive information / signals to / from other vehicles, traffic lights 204, and / or servers 208. The transceiver 210 may also be configured to transmit / receive signals / information to / from the above-mentioned vehicle units.

[0034] The VCU 214 may include a plurality of electronic control units (ECUs, not shown) that may enable the VCU 214 to control aspects of vehicle operation and implement one or more instruction sets received from the processor 212. For example, when the first 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, but is not limited to, real-time (or current) vehicle speed, vehicle direction of movement, vehicle geographic location, etc.

[0035] The detection unit 216 may include one or more vehicle sensors, including, but not limited to, radio detection and ranging (RADAR or "radar") sensors configured to detect and locate objects inside and outside the first vehicle 202 using radio waves, light detection and ranging ("lidar") sensors, sound navigation and ranging (sonar) sensors, proximity sensors, vehicle interior and exterior cameras, and the like. In some aspects, the vehicle exterior cameras may be configured to capture images of other vehicles in front of the first vehicle 202 or images of a geographic area near a front portion of the vehicle. In some aspects, the detection unit 216 may be part of the VCU 214. In other aspects, the detection unit 216 may be separate from the VCU 214, such as Figure 2 shown.

[0036] The infotainment system 218 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 means. In other aspects, the infotainment system 218 may 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.

[0037] The processor 212 may be configured to communicate with one or more memory devices (eg, memory 220 and / or Figure 2 The processor 212 may utilize the memory 220 to store programs and / or store data in code to perform various aspects of the present disclosure. The memory 220 may be a non-transitory computer-readable storage medium or memory that stores program code that enables the processor 212 to perform operations according to the present disclosure.

[0038] The memory 220 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, electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).

[0039] In some aspects, the memory 220 may include a plurality of databases including, but not limited to, a vehicle information database 222 , another vehicle information database 224 , a traffic light information database 226 , and an environment information database 228 .

[0040] The vehicle information database 222 may be configured to store vehicle information (e.g., first vehicle information) associated with the first vehicle 202. In some aspects, the first vehicle information may include, but is not limited to, historical speed profile change rates, vehicle weight, vehicle length, vehicle tire conditions, vehicle propulsion type, etc. associated with the first vehicle 202. In some aspects, the historical speed profile change rates may be associated with different vehicle drivers associated with the first vehicle 202 or may be different for different vehicle drivers. For example, when the first vehicle 202 may be driven by different drivers, the vehicle information may include the historical speed profile change rates for each driver.

[0041] The other vehicle information database 224 may be configured to store information related to other vehicles (such as Figure 4 The second vehicle 302 shown in the figure, which may be the same as the second vehicle 102 b described above, may be located in front of the first vehicle 202 in the vehicle queue near the traffic light 204. In some aspects, the second vehicle information may include an estimated duration (e.g., a first duration) required for the second vehicle 302 to cross the traffic light 204. The second vehicle information may also include a first distance that the second vehicle 302 may need to travel to cross the traffic light 204, a second vehicle propulsion type, and a second vehicle position in the vehicle queue near the traffic light 204.

[0042] The traffic light information database 226 may be configured to store real-time traffic signal information associated with the traffic light 204 (e.g., SPaT information associated with the traffic light 204) that the first vehicle 202 may receive from the traffic light 204 or the server 208. The environmental information database 228 may be configured to store information associated with road conditions (associated with a road on which the first vehicle 202 may be traveling) that the first vehicle 202 may obtain from the server 208 (or the detection unit 216), weather conditions in a geographic area where the first vehicle 202 may be located, and the like.

[0043] In operation, when the first vehicle 202 arrives near the traffic light 204 and the traffic light 204 may be operating in a red light phase, the transceiver 210 may receive real-time traffic signal information associated with the traffic light 204 indicating that the traffic light 204 is operating in a red light phase. In some aspects, the transceiver 210 may receive the real-time traffic signal information from a traffic signal controller (e.g., via V2X communication) installed in the traffic light 104 and / or a traffic signal control server / server 208. In some aspects, the transceiver 210 may transmit a request to the traffic signal controller / traffic signal control server to provide the real-time traffic signal information associated with the traffic light 204 (e.g., when the first vehicle 202 arrives at the traffic light 204) to the first vehicle 202. In response to receiving the request from the transceiver 210, the traffic signal controller / traffic signal control server may transmit the real-time traffic signal information to the transceiver 210. In response to receiving the real-time traffic signal information, the transceiver 210 may store the real-time traffic signal information in the traffic light information database 226 .

[0044] In addition, the transceiver 210 can receive information from other vehicles ahead of the first vehicle 202 (e.g., Figure 4 In this case, for example, when the first vehicle 202 arrives near the traffic light 204, the transceiver 210 may transmit a request to provide the second vehicle information to the transceiver 210 to the second vehicle 302. In response to receiving the request from the transceiver 210, the second vehicle 320 may transmit the second vehicle information to the transceiver 210. The transceiver 210 may receive the second vehicle information and store it in the other vehicle information database 224. In addition, the transceiver 210 may transmit the received real-time traffic signal information and the second vehicle information to the processor 212.

[0045] The processor 212 may obtain the real-time traffic signal information and the second vehicle information from the transceiver 210 or from a corresponding memory database. In operation, the processor 212 may obtain / obtain the first vehicle information from the vehicle information database 222. In addition to obtaining the above information, the processor 212 may also obtain input from the detection unit 216 (e.g., a vehicle exterior camera) and determine the position (or "vehicle position") of the first vehicle 202 in the vehicle queue near the traffic light 204 based on the input obtained from the detection unit 216 and / or the second vehicle information. In some aspects, when the first vehicle 202 arrives near the traffic light 204, the processor 212 may obtain input from the detection unit 216 and determine the vehicle position.

[0046] As an example, based on the input obtained from the detection unit 216, the processor 212 can determine whether the first vehicle 202 is in a first position (e.g., Figure 3 ), or whether another vehicle (e.g., the second vehicle 302) can be located in front of the first vehicle 202 (e.g., Figure 4 When the detection unit 216 detects that there is no vehicle in front of the first vehicle 202, the processor 212 may determine that the first vehicle 202 may be located at a first position in the vehicle queue, such as Figure 3 In response to determining that the first vehicle 202 may be at the first position, the first vehicle 202 may broadcast the determined vehicle position (i.e., the first position) to another vehicle located behind the first vehicle 202. For example, the first vehicle 202 may broadcast a vehicle position (i.e., the first position) to another vehicle located behind the first vehicle 202. Figure 3 The second vehicle 302 may receive the vehicle position from the first vehicle 202 (and input from a corresponding detection unit of the second vehicle 302), determine its own position in the vehicle train, and may send a vehicle position message to other vehicles in the vehicle train (e.g., Figure 3 304) broadcasts the determined vehicle position, and so on. In this way, all vehicles in the vehicle train determine their own vehicle position in the vehicle train based on input from their own detection units and information received from other vehicles in front. In addition, the vehicle processor (e.g., processor 212) of each vehicle can cause the corresponding infotainment system (e.g., infotainment system 218) to indicate / display the corresponding vehicle position in the vehicle train.

[0047] In addition to determining the vehicle position, the processor 212 may also calculate the distance that the first vehicle 202 may have to travel from the vehicle's current position / location in the vehicle queue to cross the traffic light 204. Specifically, in this case, the processor 212 may calculate the distance (e.g., a "first distance") that the vehicle 202 may have to travel from its current position / location to the centerline 106.

[0048] When the first vehicle 202 may be in the first position in the vehicle queue (eg Figure 3 ), the processor 212 can calculate the first vehicle length D1 and the first gap "X1" between the first portion and the center line 106 (in Figure 3 In some aspects, the processor 212 may obtain the first vehicle length D1 from the first vehicle information and obtain the first gap "X1" from the server 208 (or from a connected infrastructure using V2X communication). In response to obtaining the first vehicle length D1 and the first gap "X1", the vehicle processor 212 may calculate the first distance ("D") by adding "D1" and "X1".

[0049] In response to calculating the first distance, the processor 212 may obtain a historical speed profile change rate associated with the first vehicle 202 from the vehicle information database 222. The processor 212 may then calculate / estimate a first duration required for the first vehicle 202 to cross the traffic light 204 (e.g., the centerline 106) based on the first distance and the historical speed profile change rate. An example mathematical expression for calculating the first duration is shown below:

[0050] (D1+X1)=1 / 2a(t1 2 ),

[0051] Wherein (D1+X1) is the first distance that the first vehicle 202 may have to travel to cross the centerline 106, D1 is the first vehicle length (i.e., the length of the first vehicle 202), and X1 is the first gap; a is the speed change rate associated with the first vehicle 202 (determined based on the historical speed profile change rate associated with the first vehicle 202); and t1 is the first duration that the first vehicle 202 may need to travel to cross the centerline 106.

[0052] In this manner, the processor 212 may estimate the first duration based on the first distance and the speed change rate associated with the first vehicle 202. In response to estimating the first duration of crossing the centerline 106, the processor 212 may compare the first duration with the green light duration (obtained from the real-time traffic signal information). Based on the comparison, the processor 212 may determine the probability (or likelihood) that the first vehicle 202 crosses the traffic light 204 during the green light duration (or during the green light phase). For example, when the first vehicle 202 estimates that the first vehicle 202 can travel the first distance within 5 seconds (the first duration) and the green light duration is 30 seconds, the processor 212 may determine that the probability that the first vehicle 202 crosses the traffic light 204 within the green light duration is high (or guaranteed, e.g., greater than 90%). On the other hand, when the first duration may be equal to or greater than 30 seconds, the processor 212 may determine that the probability of crossing the traffic light 204 during the green light duration / green light phase may be low. In other words, the processor 212 may determine that the probability of crossing the traffic light 204 may be higher when the first duration may be less than the green duration, and the probability of crossing the traffic light may be lower when the first duration may be greater than the green duration.

[0053] In response to determining the probability, the processor 212 can output / transmit a notification to the infotainment system 218 to indicate the probability to the vehicle driver. The vehicle driver can drive the first vehicle 202 accordingly based on the notification / probability.

[0054] In some aspects, the processor 212 may also be configured to send a signal to other vehicles (e.g., vehicles 302 and 304, such as vehicles 306 and 307) located behind the first vehicle 202. Figure 3 ) broadcasts the calculated first distance and the estimated first duration so that the vehicles 302, 304 can estimate their own duration of crossing the traffic light 204 and their corresponding probabilities of crossing the traffic light 204 during the green light phase.

[0055] On the other hand, when the processor 212 determines a vehicle (eg, the second vehicle 302, such as the second vehicle 302) based on the input obtained from the detection unit 216, Figure 4 ) may be present in front of the first vehicle 202, the processor 212 may determine the vehicle position associated with the first vehicle 202 in the vehicle queue based on the second vehicle information obtained from the second vehicle 302 (the second vehicle information includes the vehicle position associated with the second vehicle 302 in the vehicle queue, as described above). When the processor 212 determines based on the input obtained from the detection unit 216 that the first vehicle 202 may not be in the first position and may be in the second vehicle 302 (the second vehicle may be in the first position, as described above), the processor 212 may determine the vehicle position associated with the first vehicle 202 in the vehicle queue based on the second vehicle information obtained from the second vehicle 302 (the second vehicle information includes the vehicle position associated with the second vehicle 302 in the vehicle queue, as described above). Figure 4When the processor 212 is at a second position behind the second vehicle 302 (as shown in FIG. 1 ), the processor 212 may obtain the second vehicle information from the second vehicle 302. Specifically, in this case, the processor 212 may obtain from the second vehicle 302 a second distance (e.g., D2+X1, as described below) that the second vehicle 302 may have to travel to cross the traffic light 204 (or the center line 106), and a second duration (e.g., t2) that the second vehicle 302 may have to travel to cross the traffic light 204. The second vehicle 302 may calculate the second distance and the second duration in the same manner as the first vehicle 202 calculates the first distance and the first duration described above.

[0056] In some aspects, when the first vehicle 202 is in the second position (or any other position other than the first position, such as Figure 4 ), the processor 212 calculates the first distance that the first vehicle 202 must travel to cross the traffic light 204 based on the second distance that the second vehicle 302 must travel to cross the traffic light 204. Similarly, the processor 212 calculates the first duration that the first vehicle 202 may need to cross the traffic light 204 based on the second duration that the second vehicle 302 may need to cross the traffic light 204 (and based on the first distance).

[0057] As an example, Figure 4 As depicted, the second vehicle 302 may determine that the second vehicle 302 may have to travel a second distance D2+X1 to pass through the traffic light 204, where D2 is the second vehicle length and X1 is the distance between the front portion of the second vehicle 302 and the centerline 106 (the centerline at Figure 4 In response to determining the second distance, the second vehicle 302 may transmit the second distance to the first vehicle 202 (located behind the second vehicle 302, such as Figure 4 ), and the processor 212 can therefore obtain information associated with the second distance (D2+X1) from the second vehicle 302 (as part of the second vehicle information). The processor 212 can also determine the distance "X2" (or gap "X2") between the first vehicle 202 and the second vehicle 302 (specifically, the gap between the rear portion of the second vehicle 302 and the front portion of the first vehicle 202). In some aspects, the processor 212 can determine the distance "X2" by using input obtained from the detection unit 216 (e.g., a sonar sensor). In response to obtaining the second distance D2+X1 and determining the distance X2, the processor 212 can calculate the first distance that the first vehicle 202 may have to travel to pass through the traffic light 204 as: D2+X2+D1+X1, where D1 is the first vehicle length.

[0058] The processor 212 can then calculate / estimate a first duration that may be required for the first vehicle 202 to cross the traffic light 204 based on the calculated first distance. For example, the processor 212 can calculate the first duration by using the following mathematical expression: (D2+X1+D1+X2)=1 / 2a(t1 2 ),

[0059] Where: t1 is a first duration that may be required for the first vehicle 202 to cross the centerline 106 .

[0060] When the first vehicle 202 is in a second position (eg, Figure 4 When the first duration (t1) is calculated as described above, the processor 212 can determine the probability of the first vehicle 202 crossing the traffic light 204 (or the centerline 106) based on the first duration (t1) and the second duration (t2). As an example, when the second vehicle 302 estimates that the second vehicle 302 can travel the second distance in 6 seconds and the first vehicle 202 estimates that the first vehicle 202 can travel the first distance in 10 seconds, the processor 212 can calculate the mathematical function based on the durations of 6 seconds and 10 seconds. In an exemplary aspect, the mathematical function can be the sum or addition of 6 seconds (i.e., the first duration (t1)) and 10 seconds (i.e., the second duration (t2)).

[0061] In response to determining the mathematical function, the processor 212 may correlate or compare the output of the mathematical function with the green light duration to determine the probability that the first vehicle 202 passes through the traffic light 204 during the green light phase. As an example, the processor 212 may add the first duration (t1) and the second duration (t2), and then compare the sum to the green light duration. If the sum (e.g., 16 seconds) is likely to be less than the green light duration (e.g., 30 seconds), the processor 212 may determine that the probability is high or guaranteed. On the other hand, if the sum is likely to be equal to or greater than the green light duration, the processor 212 may determine that the probability is low.

[0062] In response to determining that the first vehicle 202 may or may not be able to cross the traffic light 204 during the green phase (i.e., in response to determining the probability, as described above), the processor 212 may generate a notification for the infotainment system 218 or a command signal for the VCU 214. The processor 212 may then output / transmit the generated notification to the infotainment system 218 or output / transmit the command signal to the VCU 214.

[0063] In response to receiving the notification from the processor 212, the infotainment system 218 may output a message (e.g., audibly and / or visually) based on the notification. Figure 5As shown in view 502 of , when the processor 212 determines that the probability of the first vehicle 202 passing through the traffic light 204 may be high (e.g., greater than a threshold of 80% to 85%), the infotainment system 218 may display “You will be able to pass through the traffic light in the upcoming green light phase”. In response to hearing or seeing the message, the vehicle driver (not shown) may manipulate the vehicle movement accordingly. In another aspect, when the first vehicle 202 may be a fully autonomous vehicle or a partially autonomous vehicle, in response to obtaining a command signal from the processor 212, the VCU 214 may autonomously control the vehicle movement (e.g., increase or decrease the vehicle speed, keep the vehicle within the legal speed limit, and always be at a safe speed for given conditions) so that the first vehicle 202 can pass through the traffic light 204 in the green light phase.

[0064] Alternatively, when the processor 212 determines that the probability of the first vehicle 202 crossing the traffic light 204 during the green phase may be low (e.g., less than a threshold), the infotainment system 218 may audibly and / or visually output a message “You cannot cross the traffic light during the upcoming green phase.” In this case, in response to seeing / hearing the message, the vehicle driver may activate the adaptive cruise control interactive mode. When the vehicle driver turns on the adaptive cruise control interactive mode, the processor 212 may transmit an instruction to the VCU 214 to activate the mode. In response to the mode being activated, the VCU 214 may cause the first vehicle 202 to coast and then stop when any object (e.g., another vehicle in front of the first vehicle 202) may be detected.

[0065] In additional aspects, the processor 212 can send a signal to a vehicle located behind the first vehicle 202 (eg, Figure 4 A third vehicle 304 (shown) broadcasts information associated with the calculated first distance and first duration to enable the third vehicle 304 to calculate its own probability in the same manner as described above (using the third vehicle 304 length "D3" and the gap "X3").

[0066] Although the above description describes a case where the processor 212 is based on the first vehicle information associated with the first vehicle 202 and the second vehicle 302 (the second vehicle may be located in front of the first vehicle 202, such as Figure 4In another aspect, the processor 212 may determine the first duration based on the second vehicle information associated with the first vehicle 202 (as shown), but in another aspect, the processor 212 may estimate the first duration based on the road conditions associated with the road where the first vehicle 202 may be located and the weather conditions in the geographical area including the traffic light 204. In this case, the processor 212 may obtain / acquire the road conditions and weather conditions from the environmental information database 228, and may estimate the first duration accordingly. The processor 212 may then determine the probability that the first vehicle 202 passes through the traffic light 204 during the green light phase based on the estimated first duration.

[0067] Furthermore, although the present disclosure describes an aspect in which the processor 212 obtains real-time traffic signal information from a traffic light controller or a traffic light control server to determine the green light duration, in some aspects, the processor 212 can “learn” and determine the green light duration associated with the traffic light 204 on its own based on the historical SPaT information of the traffic light 204. In this case, the processor 212 can obtain the historical SPaT information associated with the traffic light 204 from the server 208.

[0068] Figure 6 A flow chart depicts an example method 600 for predicting the probability of a vehicle passing through a traffic light according to the present disclosure. Figure 6 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 the order described in the following exemplary embodiments.

[0069] The method 600 starts at step 602. At step 604, the method 600 may include obtaining, by the processor 212, green light duration information (associated with the green phase of the traffic light 204), first vehicle information (associated with the first vehicle 202), and second vehicle information (associated with the second vehicle 302 located in front of the first vehicle 202). The first vehicle information may include a historical speed profile change rate associated with the first vehicle 202. The second vehicle information may include a second estimated duration required for the second vehicle 302 to pass through the traffic light 204.

[0070] At step 606, the method 600 may include estimating, by the processor 212, a first duration required for the first vehicle 202 to cross the traffic light 204 based on the first vehicle information and the second vehicle information. At step 608, the method 600 may include comparing, by the processor 212, the first duration with the green light duration. At step 610, the method 600 may include determining, by the processor 212, a probability that the first vehicle 202 crosses the traffic light 204 within the green light duration based on the comparison. At step 612, the method 600 may include transmitting, by the processor 212, a notification to indicate the probability.

[0071] Method 600 may end at step 614 .

[0072] In the above disclosure, reference has been made to the accompanying drawings that form a part of the above disclosure, which illustrate specific implementations in which the present disclosure may be practiced. It should be understood that other implementations may be utilized and structural changes may be made without departing from the scope of the present disclosure. References to "one embodiment," "embodiment," "exemplary embodiment," etc. in this specification 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] In one aspect of the invention, the method includes broadcasting the third distance to a third vehicle located behind the first vehicle.

[0080] In one aspect of the invention, estimating the second duration comprises estimating the second duration based on the third distance.

[0081] In one aspect of the invention, the method comprises broadcasting the second duration to the third vehicle.

[0082] In one aspect of the present invention, the method includes: obtaining information associated with road conditions and weather conditions; and estimating the second duration required to cross the traffic light based on the information associated with the road conditions and the weather conditions.

[0083] According to the present invention, a non-transitory computer-readable medium storing a computer program is provided, the non-transitory computer-readable medium having executable instructions, which when executed by one or more processors result in the execution of operations including: information associated with a green light duration associated with a green light phase of a traffic light, first vehicle information associated with a first vehicle, and second vehicle information associated with a second vehicle located in front of the first vehicle, wherein: the first vehicle information includes a historical speed curve change rate associated with the first vehicle, and the second vehicle information includes a first duration required for the second vehicle to cross the traffic light; estimating a second duration required for the first vehicle to cross the traffic light based on the first vehicle information and the second vehicle information; comparing the second duration with the green light duration; determining the probability of the first vehicle crossing the traffic light within the green light duration based on the comparison; and transmitting a notification to indicate the probability.

Claims

1. A first vehicle, comprising: a memory configured to store first vehicle information, the first vehicle information comprising a historical speed profile change rate associated with the first vehicle; A transceiver, the transceiver being configured to: receiving real-time traffic signal information of a traffic signal light, wherein the real-time traffic signal information includes a green light duration associated with a green light phase of the traffic signal light; as well as receiving second vehicle information associated with a second vehicle located ahead of the first vehicle, wherein the second vehicle information includes a first duration of time the second vehicle crosses the traffic light; a processor communicatively coupled to the transceiver, wherein the processor is configured to: obtaining the real-time traffic signal information associated with the green light duration, the second vehicle information, and the first vehicle information; estimating a second duration required for the first vehicle to cross the traffic light based on the first vehicle information and the second vehicle information; comparing the second duration with the green light duration; determining a probability that the first vehicle will pass through the traffic light within the green light duration based on the comparison; as well as A notification is transmitted to indicate the probability. 2 . The first vehicle of claim 1 , wherein the first vehicle information further comprises one or more of: a first vehicle weight, a first vehicle length, a first vehicle tire condition, or a first vehicle propulsion type.

3. The first vehicle of claim 1 , wherein the second vehicle information further includes one or more of: a first distance that the second vehicle must travel to cross the traffic light, a second vehicle propulsion type, or a second vehicle position in a vehicle queue near the traffic light.

4. The first vehicle of claim 3, wherein the processor is further configured to: determining a second distance between the first vehicle and the second vehicle; and A third distance that the first vehicle must travel to cross the traffic light is calculated based on the first vehicle information, the second vehicle information, and the second distance. 5 . The first vehicle of claim 4 , wherein the processor is further configured to broadcast the third distance to a third vehicle located behind the first vehicle. 6 . The first vehicle of claim 5 , wherein the processor is configured to estimate the second duration required to cross the traffic light further based on the third distance. 7 . The first vehicle of claim 6 , wherein the processor is further configured to broadcast the second duration to the third vehicle.

8. The first vehicle of claim 1, wherein the processor is further configured to: Obtain information related to road and weather conditions; and The second duration required to cross the traffic light is estimated based on the information associated with the road condition and the weather condition.

9. The first vehicle of claim 1, further comprising a detection unit configured to capture an image of a geographic area proximate a front portion of the first vehicle.

10. The first vehicle of claim 9, wherein the processor is further configured to: obtaining input from the detection unit; determining a first vehicle position in a vehicle train based on the input from the detection unit and the second vehicle information; and Another notification is transmitted to indicate the first vehicle location.

11. The first vehicle of claim 1 , wherein the processor is configured to determine that the probability of crossing the traffic light is greater than a predefined threshold when the second duration is less than the green light duration, and to determine that the probability of crossing the traffic light is less than a predefined threshold when the second duration is greater than the green light duration.

12. A method performed by a first vehicle, the method comprising: Information associated with a green light duration associated with a green light phase of a traffic light, first vehicle information associated with the first vehicle, and second vehicle information associated with a second vehicle located ahead of the first vehicle is obtained by a processor, wherein: The first vehicle information includes a historical speed profile rate associated with the first vehicle, and The second vehicle information includes a first duration of time during which the second vehicle passes through the traffic light; estimating, by the processor, a second duration required for the first vehicle to cross the traffic light based on the first vehicle information and the second vehicle information; comparing, by the processor, the second duration with the green light duration; determining, by the processor, a probability that the first vehicle will pass through the traffic light within the green light duration based on the comparison; and A notification is transmitted by the processor to indicate the probability.

13. The method of claim 12, wherein the first vehicle information further comprises one or more of: a first vehicle weight, a first vehicle length, a first vehicle tire condition, or a first vehicle propulsion type.

14. The method of claim 12, wherein the second vehicle information further includes one or more of: a first distance that the second vehicle must travel to cross the traffic light, a second vehicle propulsion type, or a second vehicle position in a vehicle queue near the traffic light.

15. The method of claim 14, further comprising: determining a second distance between the first vehicle and the second vehicle; as well as A third distance that the first vehicle must travel to cross the traffic light is calculated based on the first vehicle information, the second vehicle information, and the second distance.

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