Determining optimal departure time for vehicle

Abstract

A method for providing traffic information to an occupant of a vehicle may include identifying a node location in an environment around the vehicle. The node location is a location of an intersection between a first road and a second road on which the vehicle is traveling, the first road having a first road level and the second road having a second road level. The first road grade is lower than the second road grade. The method may also include determining traffic data regarding one or more remote vehicles traveling on a segment of a second road adjacent to the node location. The method may also include determining an estimated latency of the vehicle based at least in part on the traffic data and the distance between the vehicle and the node location. The method may also include performing a first action based at least in part on the estimated latency.

Description

Technical Field

[0001] This disclosure relates to advanced driver assistance systems and methods for vehicles, as well as automated driving systems and methods, and more specifically, to systems and methods for alleviating traffic congestion and increasing the comfort of vehicle occupants. Background Technology

[0002] To enhance occupant awareness and convenience, vehicles can be equipped with Advanced Driver Assistance Systems (ADAS) and / or Automated Driving Systems (ADS). ADAS systems use various sensors, such as cameras, radar, and lidar, to detect and identify objects around the vehicle, including other vehicles, pedestrians, road features, and traffic signs. ADAS systems can take actions based on environmental conditions, such as applying the brakes or warning vehicle occupants. However, current ADS systems may not account for other factors that could affect the occupant experience. For example, crossing an intersection between a lower-grade road (e.g., a local road) and a higher-grade road (e.g., a connecting road) may cause delays due to the higher traffic density on the higher-grade road. Furthermore, waiting at traffic lights can also lead to delays.

[0003] Therefore, while ADAS and ADS systems and methods have achieved their intended purpose, a new and improved system and method is still needed to provide traffic information to vehicle occupants. Summary of the Invention

[0004] According to several aspects, a method for providing traffic information to occupants of a vehicle is provided. The method may include identifying a node location in the environment surrounding the vehicle. The node location is the location of an intersection between a first road and a second road on which the vehicle is traveling. The first road has a first road class, and the second road has a second road class. The first road class is lower than the second road class. The method may also include determining traffic data regarding one or more remote vehicles traveling on a segment of the second road adjacent to the node location. The method may further include determining an estimated waiting time for the vehicle based at least in part on the traffic data and the distance between the vehicle and the node location. The method may further include performing a first action based at least in part on the estimated waiting time.

[0005] In another aspect of this disclosure, determining traffic data may further include receiving remote vehicle telemetry data from one or more remote vehicles. The remote vehicle telemetry data includes at least the location of each of the one or more remote vehicles. Determining traffic data may also include determining traffic data at least in part based on the remote vehicle telemetry data.

[0006] In another aspect of this disclosure, determining traffic data may further include determining the percentage of one or more remote vehicles traveling at speeds below the speed limit of the second road segment during the most recent historical time period. Determining traffic data may further include determining the percentage of one or more remote vehicles traveling at free-driving speeds below the speed limit of the second road segment during the most recent historical time period. Determining traffic data may further include determining the business classification level of the second road segment during the most recent historical time period. Determining traffic data may further include determining the road segment traffic profile based at least in part on at least one of the following: the percentage of one or more remote vehicles traveling at speeds below the speed limit of the second road segment, the percentage of one or more remote vehicles traveling at free-driving speeds below the speed limit of the second road segment, and the business classification level of the second road segment. The road segment traffic profile describes the perceived traffic level on the second road segment during the most recent historical time period.

[0007] In another aspect of this disclosure, determining traffic data may further include receiving signal phase and timing (SPaT) data from traffic signals at node locations within the most recent historical time period. Determining traffic data may also include determining road segment traffic profiles based at least in part on one of the following: the percentage of one or more remote vehicles traveling at speeds below the speed limit of the second road segment, the percentage of one or more remote vehicles traveling at speeds below the free-driving speed of the second road segment, the traffic classification level of the second road segment, and SpaT data. The road segment traffic profile describes the perceived traffic level on the second road segment within the most recent historical time period.

[0008] In another aspect of this disclosure, determining the estimated waiting time may further include identifying recurring time periods when traffic conditions on a road segment reach a minimum. Determining the estimated waiting time may also include determining the estimated waiting time based at least in part on recurring time periods when traffic conditions on a road segment approach a minimum.

[0009] In another aspect of this disclosure, identifying the recurring time period when the traffic situation of a road segment reaches its minimum may further include fitting the traffic situation of the road segment to a periodic curve. Identifying the recurring time period when the traffic situation of a road segment reaches its minimum may further include determining one or more parameters characterizing the periodic curve. The one or more parameters include at least a minimum flow rate and a period. Identifying the recurring time period when the traffic situation of a road segment reaches its minimum may further include identifying the recurring time period based at least in part on the minimum flow rate and the period.

[0010] In another aspect of this disclosure, determining the estimated waiting time based at least in part on a repetitive time period may further include determining the estimated delay time based at least in part on one or more parameters characterizing the periodic curve and the current perceived traffic level of the segment of the second road, wherein the estimated delay time is the time until the perceived traffic level on the estimated segment of the second road reaches a minimum flow value. Determining the estimated waiting time based at least in part on a repetitive time period may further include determining the estimated travel time of a vehicle to the node location based at least in part on the distance between the vehicle and the node location and the free-traffic speed of the first road. Determining the estimated waiting time based at least in part on a repetitive time period may further include determining the estimated waiting time based at least in part on the estimated delay time and the estimated travel time. The estimated waiting time is the difference between the estimated delay time and the estimated travel time.

[0011] In another aspect of this disclosure, performing the first action may also include providing notification to the occupants of the vehicle using a vehicle display, at least in part, based on an estimated waiting time.

[0012] In another aspect of this disclosure, providing notification may also include determining an optimal departure delay based at least in part on estimated waiting time. The optimal departure delay is the amount of time by which occupants should delay their departure so that the estimated waiting time is zero upon arrival at the node location. Providing notification may also include providing notification to the vehicle's occupants based at least in part on the optimal departure delay.

[0013] In another aspect of this disclosure, performing the first action may further include determining an optimal departure delay based at least in part on an estimated waiting time. The optimal departure delay is the amount of time the vehicle should delay its departure so that the estimated waiting time is zero upon arrival at the node location. Performing the first action may further include comparing the optimal departure delay time to zero. Performing the first action may further include initiating an autonomous driving route using the vehicle's autonomous driving system in response to determining that the optimal departure delay is within a predetermined range of zero.

[0014] According to several aspects, a system for providing traffic information to occupants of a vehicle is provided. The system may include a server system, which may include a server communication system and a server controller electrically communicating with the server communication system. The server controller is programmed to identify node locations in the environment surrounding the vehicle. A node location is the location of an intersection between a first road and a second road on which the vehicle is traveling. The first road has a first road class, and the second road has a second road class. The first road class is lower than the second road class. The server controller is also programmed to use the server communication system to determine traffic data regarding one or more remote vehicles traveling on a segment of the second road adjacent to the node location. The server controller is also programmed to determine an estimated waiting time for the vehicle, at least in part, based on the traffic data and the distance between the vehicle and the node location. The server controller is further programmed to transmit the estimated waiting time using the server communication system.

[0015] In another aspect of this disclosure, to determine traffic data, the server controller is also programmed to receive remote vehicle telemetry data from one or more remote vehicles using a server communication system. The remote vehicle telemetry data includes at least the location of each of the one or more remote vehicles. To determine traffic data, the server controller is also programmed to determine traffic data at least in part based on the remote vehicle telemetry data.

[0016] In another aspect of this disclosure, to determine traffic data, the server controller is further programmed to determine, at least in part, the percentage of one or more remote vehicles traveling at speed restrictions lower than those of the second road segment during a recent historical time period, based on remote vehicle telemetry data. To determine traffic data, the server controller is also programmed to determine, at least in part, the percentage of one or more remote vehicles traveling at free-roaming speeds lower than those of the second road segment during a recent historical time period, based on remote vehicle telemetry data. To determine traffic data, the server controller is also programmed to determine, at least in part, the traffic classification level of the second road segment during a recent historical time period, based on remote vehicle telemetry data. To determine traffic data, the server controller is also programmed to receive signal phase and timing (SPaT) data from traffic signals at node locations during a recent historical time period. To determine traffic data, the server controller is also programmed to determine the road segment traffic profile based at least in part on at least one of the following: the percentage of one or more remote vehicles traveling at speed restrictions lower than those of the second road segment, the percentage of one or more remote vehicles traveling at free-roaming speeds lower than those of the second road segment, the traffic classification level of the second road segment, and the SPaT data. The road segment traffic profile describes the perceived traffic level on a segment of the Second Road during the most recent historical period.

[0017] In another aspect of this disclosure, to determine the estimated waiting time, the server controller is also programmed to fit the road segment traffic profile to a periodic curve. To determine the estimated waiting time, the server controller is also programmed to determine one or more parameters characterizing the periodic curve. These one or more parameters include at least a minimum flow rate and a period. To determine the estimated waiting time, the server controller is also programmed to identify recurring time periods when the road segment traffic profile reaches its minimum, based at least in part on the minimum flow rate and the time period. To determine the estimated waiting time, the server controller is also programmed to determine the estimated waiting time based at least in part on recurring time periods when the road segment traffic profile approaches its minimum.

[0018] In another aspect of this disclosure, to determine the estimated waiting time, the server controller is further programmed to determine, at least in part, the estimated delay time until the perceived traffic level on the segment of the second road reaches a minimum flow value, based on one or more parameters characterizing the periodic curve and the current perceived traffic level of the segment of the second road. To determine the estimated waiting time, the server controller is also programmed to determine, at least in part, the estimated travel time of a vehicle to the node location, based on the distance between the vehicle and the node location and the free-traffic speed of the first road. To determine the estimated waiting time, the server controller is also programmed to determine the estimated waiting time based at least in part on the estimated delay time and the estimated travel time. The estimated waiting time is the difference between the estimated delay time and the estimated travel time.

[0019] In another aspect of this disclosure, the system also includes a vehicle system. The vehicle system may include a vehicle communication system, a vehicle display, and a vehicle controller in electrical communication with the vehicle communication system and the vehicle display. The vehicle controller is programmed to receive an estimated waiting time from a server system using the vehicle communication system. The vehicle controller is also programmed to provide notifications to the occupants of the vehicle, at least in part, based on the estimated waiting time, using the vehicle display.

[0020] In another aspect of this disclosure, the vehicle system also includes an autonomous driving system in electrical communication with the vehicle controller. The vehicle controller is further programmed to determine an optimal departure delay based at least in part on an estimated waiting time. The optimal departure delay is the amount of time the vehicle should delay its departure such that the estimated waiting time is zero upon arrival at the node location. The vehicle controller is also programmed to compare the optimal departure delay with zero. The vehicle controller is further programmed to initiate an autonomous driving route using the vehicle's autonomous driving system in response to determining that the optimal departure delay is within a predetermined range of zero.

[0021] According to several aspects, a method for providing traffic information to vehicle occupants is provided. The method may include identifying node locations in the environment surrounding the vehicle. A node location is the location of an intersection between a first road and a second road on which the vehicle is traveling. The first road has a first road class, and the second road has a second road class. The first road class is lower than the second road class. The method may also include receiving remote vehicle telemetry data from one or more remote vehicles traveling on a segment of the second road. The remote vehicle telemetry data includes at least the location of each of the one or more remote vehicles. The segment of the second road is adjacent to the node location. The method may also include receiving signal phase and timing (SPaT) data of a traffic signal at the node location. The method may also include determining a road segment traffic profile based at least in part on the remote vehicle telemetry data and the SPaT data. The road segment traffic profile describes the perceived traffic level on the segment of the second road over a recent historical time period. The method may also include determining an estimated waiting time for the vehicle based at least in part on the remote vehicle telemetry data, the SPaT data, and the distance between the vehicle and the node location. The method may also include providing notification to vehicle occupants using a vehicle display based at least in part on the estimated waiting time.

[0022] In another aspect of this disclosure, determining the estimated waiting time may further include fitting the traffic profile of a road segment to a periodic curve. Determining the estimated waiting time may further include determining one or more parameters characterizing the periodic curve. These one or more parameters include at least a minimum flow rate and a period. Determining the estimated waiting time may further include identifying recurring time periods when the traffic profile of a road segment reaches a minimum, based at least in part on the minimum flow rate and the period. Determining the estimated waiting time may further include determining, at least in part on the parameters characterizing the periodic curve and the current perceived traffic level of the segment of the second road, an estimated delay time until the perceived traffic level on the segment of the second road is estimated to have reached a minimum flow rate. Determining the estimated waiting time may further include determining the estimated travel time for a vehicle to reach a node location, based at least in part on the distance between the vehicle and the node location and the free-traffic speed of the first road. Determining the estimated waiting time may further include determining the estimated waiting time based at least in part on the estimated delay time and the estimated travel time. The estimated waiting time is the difference between the estimated delay time and the estimated travel time.

[0023] In another aspect of this disclosure, the method may further include determining an optimal departure delay based at least in part on an estimated waiting time. The optimal departure delay is the amount of time by which the vehicle should delay its departure so that the estimated waiting time is zero upon arrival at the node location. The method may further include comparing the optimal departure delay to zero. The method may further include initiating an autonomous driving route using the vehicle's autonomous driving system in response to determining that the optimal departure delay is within a predetermined range of zero.

[0024] Further areas of application will become apparent from the description provided herein. It should be understood that these descriptions and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0025] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way.

[0026] Figure 1 This is a schematic diagram of a system for providing traffic information to occupants of a vehicle, according to an exemplary embodiment.

[0027] Figure 2 This is a flowchart of a method for providing traffic information to occupants of a vehicle according to an exemplary embodiment; and

[0028] Figure 3 This is a method for providing traffic information to vehicle occupants according to an exemplary embodiment. Figure 2 Flowchart (continued). Detailed Implementation

[0029] The following description is merely exemplary in nature and is not intended to limit this disclosure, its application, or its uses.

[0030] When crossing an intersection between a lower-grade road (e.g., a local road) and a higher-grade road (e.g., a connecting road), delays may occur due to the higher traffic density on the higher-grade road. Furthermore, waiting at traffic signals can also cause delays. Therefore, this disclosure provides a new and improved system and method for providing traffic information to vehicle occupants, taking into account traffic density and traffic signal behavior to determine the optimal departure time for vehicles, thereby minimizing delays and traffic congestion.

[0031] refer to Figure 1 The figure illustrates a system for providing traffic information to vehicle occupants, generally indicated by reference numeral 10. System 10 generally comprises a vehicle system 10a and a server system 10b.

[0032] Vehicle system 10a is shown together with exemplary vehicle 12. Although a passenger vehicle is shown, it should be understood that vehicle 12 can be any type of vehicle without departing from the scope of this disclosure. Vehicle system 10a generally includes vehicle controller 14, multiple vehicle sensors 16, vehicle display 18, and autonomous driving system 20.

[0033] The vehicle controller 14 is used to implement method 100, which provides traffic information to the occupants of a vehicle, as described below. The vehicle controller 14 includes at least one processor and a non-transitory computer-readable storage device or medium. The processor may be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among a plurality of processors associated with the vehicle controller 14, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or generally a device for executing instructions.

[0034] Computer-readable storage devices or media may include volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is persistent or non-volatile memory used to store various operational variables when the processor is powered off. Computer-readable storage devices or media may be implemented using a variety of storage devices, such as programmable read-only memory (PROM), electrical PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represents executable instructions used by vehicle controller 14 to control various systems of vehicle 12.

[0035] The vehicle controller 14 may also consist of multiple controllers that are electrically communicating with each other. The vehicle controller 14 may interconnect with additional systems and / or controllers of the vehicle 12, thereby allowing the vehicle controller 14 to access data such as the speed, acceleration, braking and steering angle of the vehicle 12.

[0036] The vehicle controller 14 communicates electrically with multiple vehicle sensors 16, vehicle displays 18, and an autonomous driving system 20. In an exemplary embodiment, electrical communication is established using, for example, a CAN network, a FLEXRAY network, a local area network (e.g., WiFi, Ethernet, etc.), a Serial Peripheral Interface (SPI) network, etc. It should be understood that various additional wired and wireless technologies and communication protocols used for communicating with the vehicle controller 14 are within the scope of this disclosure. It should also be understood that, within the scope of this disclosure, electrical communication also includes the transfer of power and / or energy between electrical devices (e.g., using wired and / or wireless power transmission technologies).

[0037] Multiple vehicle sensors 16 are used to acquire information related to the vehicle 12. In an exemplary embodiment, the multiple vehicle sensors 16 include at least a telemetry sensor (not shown) and a vehicle communication system (not shown).

[0038] A telemetry sensor is used to collect telemetry data about vehicle 12. In an exemplary embodiment, the telemetry data includes at least the position of vehicle 12. In another exemplary embodiment, the telemetry data also includes the speed of vehicle 12. In another exemplary embodiment, the telemetry data also includes the heading of vehicle 12. In another exemplary embodiment, the telemetry data also includes the acceleration of vehicle 12. The telemetry sensor is in electrical communication with vehicle controller 14, as described above.

[0039] In a non-limiting example, to determine the location of vehicle 12, the telemetry sensor includes a Global Navigation Satellite System (GNSS). GNSS is used to determine the geographic location of vehicle 12. In an exemplary embodiment, GNSS is a Global Positioning System (GPS). In a non-limiting example, GPS includes a GPS receiver antenna (not shown) and a GPS controller (not shown) in electrical communication with the GPS receiver antenna. The GPS receiver antenna receives signals from multiple satellites, and the GPS controller calculates the geographic location of vehicle 12 based on the signals received by the GPS receiver antenna. It should be understood that various additional types of satellite-based radio navigation systems, such as Global Positioning System (GPS), Galileo, GLONASS, and BeiDou Navigation Satellite System (BDS), are all within the scope of this disclosure.

[0040] In a non-limiting example, to determine the velocity, heading, and acceleration of vehicle 12, the telemetry sensors also include an inertial measurement unit (IMU). The IMU is used to determine the direction, velocity, and gravity acting on vehicle 12. In an exemplary embodiment, the IMU includes multiple sensors, including an accelerometer, a gyroscope, and / or a magnetometer. In a non-limiting example, the IMU includes a three-axis accelerometer and a three-axis gyroscope integrated into a single unit. The accelerometer measures linear acceleration along each axis, while the gyroscope measures angular velocity about each axis. The IMU processes data from the sensors to calculate the current orientation, velocity, heading, yaw rate (i.e., the rate of change of heading), and acceleration of vehicle 12 in three-dimensional space.

[0041] Vehicle controller 14 uses a vehicle communication system to communicate with other systems outside vehicle 12 (e.g., server system 10b, as discussed below). For example, the vehicle communication system includes the ability to communicate with other vehicles (“V2V” communication), with infrastructure (“V2I” communication), with remote systems such as remote call centers (e.g., General Motors’ ON-STAR), and / or with personal devices. Generally, the term “vehicle-to-everything (“V2X” communication) refers to communication between vehicle 12 and any remote system (e.g., vehicles, infrastructure, and / or remote systems).

[0042] In some embodiments, the vehicle communication system is a wireless communication system configured to communicate using the IEEE 802.11 standard or via a wireless local area network (WLAN) using cellular data communication (e.g., using GSMA standards such as SGP.02, SGP.22, SGP.32, etc.). Therefore, the vehicle communication system may also include an embedded universal integrated circuit card (eUI CC), configured to store at least one cellular connectivity configuration profile, such as an embedded subscriber identity module (eSIM) profile.

[0043] The vehicle communication system is also configured to communicate via a personal area network (e.g., Bluetooth), near field communication (NFC), and / or any additional types of radio frequency communication. However, additional or alternative communication methods, such as Dedicated Short Range Communication (DSRC) channels and / or mobile telecommunications protocols based on 3GPP standards, are also considered within the scope of this disclosure. A DSRC channel refers to a unidirectional or bidirectional short- to medium-range wireless communication channel designed specifically for automotive use, along with a set of corresponding protocols and standards. 3GPP refers to a partnership among multiple standards organizations that develop mobile telecommunications protocols and standards. 3GPP standards are structured as "releases." Therefore, communication methods based on 3GPP releases 14, 15, 16, and / or future 3GPP releases are considered within the scope of this disclosure.

[0044] Therefore, the vehicle communication system may include one or more antennas and / or communication transceivers for receiving and / or transmitting signals, such as cooperative sensing messages (CSM). The vehicle communication system is configured to wirelessly transmit information between vehicle 12 and another vehicle. Furthermore, the vehicle communication system is configured to wirelessly transmit information between vehicle 12 and infrastructure or other vehicles. It should be understood that, without departing from the scope of this disclosure, the vehicle communication system may be integrated with vehicle controller 14 (e.g., on the same circuit board as vehicle controller 14 or otherwise as part of vehicle controller 14).

[0045] In another exemplary embodiment, the plurality of vehicle sensors 16 further include sensors for determining performance data about the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 also include at least one of a motor speed sensor, a motor torque sensor, an electric drive motor voltage and / or current sensor, an accelerator pedal position sensor, a brake position sensor, a coolant temperature sensor, a cooling fan speed sensor, and a transmission fluid temperature sensor.

[0046] In another exemplary embodiment, the plurality of vehicle sensors 16 further include sensors for determining information about the environment within the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 also include at least one of a seat occupancy sensor, a cabin air temperature sensor, a cabin motion detection sensor, a cabin camera, a cabin microphone, etc.

[0047] In another exemplary embodiment, the plurality of vehicle sensors 16 further include sensors for determining information about the environment surrounding the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 also include at least one of an ambient air temperature sensor, a barometric pressure sensor, a global navigation satellite system (GNSS), and / or a camera and / or video camera, the camera and / or video camera being positioned to observe the environment in front of and / or around the vehicle 12.

[0048] In another exemplary embodiment, at least one of the plurality of vehicle sensors 16 is a sensing sensor capable of sensing objects in the environment surrounding the vehicle 12 and / or measuring distances. In a non-limiting example, the plurality of vehicle sensors 16 includes a stereo camera with distance measurement capabilities. In one example, at least one of the plurality of vehicle sensors 16 is fixed inside the vehicle 12, for example, fixed in the roof lining of the vehicle 12, having a field of view through the windshield of the vehicle 12. In another example, at least one of the plurality of vehicle sensors 16 is fixed outside the vehicle 12, for example, fixed on the roof of the vehicle 12, having a field of view of the environment surrounding the vehicle 12. It should be understood that various additional types of sensing sensors, such as LiDAR sensors, ultrasonic ranging sensors, radar sensors, cameras, and / or time-of-flight sensors, are all within the scope of this disclosure. The plurality of vehicle sensors 16 are in electrical communication with the vehicle controller 14, as described above.

[0049] The vehicle display 18 is used to provide information to the occupants of the vehicle 12. Within the scope of this disclosure, occupants include the driver and / or passengers of the vehicle 12. In an exemplary embodiment, the vehicle display 18 is a human-machine interface (HMI) located within the occupants' field of vision and capable of displaying text, graphics, and / or images. It should be understood that HMI display systems including LCD displays, LED displays, etc., are within the scope of this disclosure. Other exemplary embodiments in which the vehicle display 18 is disposed in a rearview mirror are also within the scope of this disclosure.

[0050] In another exemplary embodiment, the vehicle display 18 includes a head-up display (HUD) configured to provide information to an occupant by projecting text, graphics, and / or images onto the windshield of the vehicle 12. The text, graphics, and / or images are reflected by the windshield of the vehicle 12 and can be seen by the occupant without taking their eyes off the road in front of the vehicle 12. In another exemplary embodiment, the vehicle display 18 includes an augmented reality head-up display (AR-HUD). An AR-HUD is a type of HUD configured to enhance the occupant's view of the road ahead of the vehicle 12 by overlaying text, graphics, and / or images onto physical objects in the environment surrounding the vehicle 12 within the occupant's field of vision. In an exemplary embodiment, the occupant can interact with the vehicle display 18 using a human-machine interface device (HID), including, for example, a touchscreen, electromechanical switch, capacitive switch, knob, etc. It should be understood that additional systems for displaying information to the occupants of the vehicle 12 are also within the scope of this disclosure. The vehicle display 18 is in electrical communication with the vehicle controller 14, as described above.

[0051] The automated driving system 20 is used to provide assistance to occupants to increase their awareness and / or control of the behavior of the vehicle 12. Within the scope of this disclosure, the automated driving system 20 encompasses systems that provide any level of assistance to occupants (e.g., blind spot warning, lane departure warning, etc.) and systems capable of autonomously driving the vehicle 12 under some or all conditions (e.g., automatic lane keeping, adaptive cruise control, fully autonomous driving, etc.). It should be understood that all levels of driving automation (i.e., SAE LEVEL 0, SAE LEVEL 1, SAE LEVEL 2, SAE LEVEL 3, SAE LEVEL 4, and SAE LEVEL 5) as defined by, for example, the Society of Automotive Engineers (SAE) J3016 are within the scope of this disclosure.

[0052] In an exemplary embodiment, the autonomous driving system 20 is configured to detect and / or receive information about the environment surrounding the vehicle 12 and process that information to provide assistance to the occupants. In some embodiments, the autonomous driving system 20 is a software module that executes on the vehicle controller 14. In other embodiments, the autonomous driving system 20 includes a separate autonomous driving system controller similar to the vehicle controller 14, which is capable of processing information about the environment surrounding the vehicle 12. In an exemplary embodiment, the autonomous driving system 20 can operate in a manual operation mode, a partially automatic operation mode, and a fully automatic operation mode.

[0053] Within the scope of this disclosure, manual operation mode refers to the autonomous driving system 20 providing warnings or notifications to the occupants without directly intervening in or controlling the vehicle 12. In a non-limiting example, the autonomous driving system 20 receives information from multiple vehicle sensors 16. Using, for example, computer vision technology, the autonomous driving system 20 learns about the environment surrounding the vehicle 12 and provides assistance to the occupants. For example, if the autonomous driving system 20 identifies, based on data from the multiple vehicle sensors 16, that the vehicle 12 may collide with a remote vehicle, the autonomous driving system 20 may use the vehicle display 18 to provide a warning to the occupants.

[0054] Within the scope of this disclosure, a partially automated operating mode refers to the autonomous driving system 20 providing warnings or notifications to the occupants and, in certain circumstances, directly intervening in or controlling the vehicle 12. In a non-limiting example, the autonomous driving system 20 also communicates electrically with components of the vehicle 12, such as the braking system, propulsion system, and / or steering system, enabling the autonomous driving system 20 to control the behavior of the vehicle 12. In a non-limiting example, the autonomous driving system 20 can control the behavior of the vehicle 12 by applying the brakes to avoid an impending collision. In another non-limiting example, the autonomous driving system 20 can control the steering system of the vehicle 12 to provide automatic lane-keeping features. In yet another non-limiting example, the autonomous driving system 20 can control the braking system, propulsion system, and steering system of the vehicle 12 to temporarily drive the vehicle 12 toward a predetermined destination. However, occupant intervention may be required at any time. In an exemplary embodiment, the autonomous driving system 20 may include additional components, such as an eye-tracking device, configured to monitor the occupant's level of attention and ensure the occupant is ready to take over control of the vehicle 12.

[0055] Within the scope of this disclosure, fully automated operation mode refers to the autonomous driving system 20 using data from multiple vehicle sensors 16 to learn about the environment and control the vehicle 12 to drive the vehicle 12 to a predetermined destination without the control or intervention of the occupants.

[0056] The autonomous driving system 20 operates using a path planning algorithm configured to generate a safe and efficient trajectory for the vehicle 12 to navigate in the environment surrounding the vehicle 12. In an exemplary embodiment, the path planning algorithm is a machine learning algorithm trained to output control signals for the vehicle 12 based on input data collected from multiple vehicle sensors 16. In another exemplary embodiment, the path planning algorithm is a deterministic algorithm programmed to output control signals for the vehicle 12 based on data collected from multiple vehicle sensors 16.

[0057] In a non-limiting example, the path planning algorithm generates a series of waypoints or a continuous path that vehicle 12 should follow to reach its destination, while adhering to rules, regulations, and safety constraints. The sequence of waypoints or the continuous path is generated based at least in part on a detailed map and the current state of vehicle 12 (i.e., the position, speed, and orientation of vehicle 12). The detailed map includes information, for example, about lane boundaries, road geometry, speed limits, traffic signs, and / or other relevant features. In an exemplary embodiment, the detailed map is stored in the medium of vehicle controller 14 and / or in a remote database or on a server. In another exemplary embodiment, the path planning algorithm performs perception and mapping tasks to interpret data collected from multiple vehicle sensors 16 and to create, update, and / or expand the detailed map.

[0058] It should be understood that the autonomous driving system 20 may include any software and / or hardware modules configured to operate in manual, partially automated, or fully automated modes as described above. The autonomous driving system 20 is in electrical communication with the vehicle controller 14, as described above.

[0059] Continue to refer to Figure 1 The server system 10b generally includes a server controller 30 that is in electrical communication with the server database 32 and the server communication system 34. In a non-limiting example, the server system 10b is located in a server farm, data center, etc., and is connected to the Internet.

[0060] Server controller 30 is used to implement method 100, which provides traffic information to occupants of a vehicle, as described below. Server controller 30 includes at least one server processor 36 and a server non-transitory computer-readable storage device or server medium 38. The description of the type and configuration given above for vehicle controller 14 also applies to server controller 30. In some examples, server controller 30 differs from vehicle controller 14 in that it can have higher processing speeds, include more memory, include more inputs / outputs, etc. In non-limiting examples, the server processor 36 and server medium 38 of server controller 30 are structurally and / or functionally similar to the processor and medium of vehicle controller 14, as described above.

[0061] Server database 32 is used to store detailed maps of roads, including information such as lane boundaries, road geometry, speed limits, traffic signs, and / or other relevant features. Server database 32 is also used to store telemetry data received from vehicles, as will be discussed in more detail below. In an exemplary embodiment, server database 32 includes one or more mass storage devices, such as hard disk drives, tape drives, magneto-optical drives, optical disks, solid-state drives, and / or other devices operable to store data in a persistent and machine-readable manner. In some examples, the one or more mass storage devices may be configured to provide redundancy in the event of hardware failure and / or data corruption, using, for example, a redundant array of independent disks (RAID). In a non-limiting example, server controller 30 may execute software such as a database management system (DBMS), thereby allowing the organization and access of data stored on the one or more mass storage devices.

[0062] Server communication system 34 is used to communicate with external systems (e.g., vehicle controller 14) via vehicle communication system. In a non-limiting example, server communication system 34 is structurally and / or functionally similar to vehicle communication system, as described above. In some examples, server communication system 34 differs from vehicle communication system in that server communication system 34 is capable of higher power signal transmission, more sensitive signal reception, higher bandwidth transmission, additional transmission / reception protocols, etc.

[0063] Continue to refer to Figure 1 The system 10 is shown in an environment including a first road 40a, a second road 40b, a traffic signal 42, and one or more remote vehicles 44.

[0064] First road 40a is the road on which vehicle 12 travels. In an exemplary embodiment, first road 40a has a first road class. Second road 40b is the road on which one or more remote vehicles 44 are traveling. In an exemplary embodiment, second road 40b has a second road class. Within the scope of this disclosure, "road class" indicates a classification of a particular road based on its function and traffic capacity. In a non-limiting example, a road may be classified as a local road, connecting road, arterial road, or highway, where a local road is the "lowest" road class (by traffic capacity) and a highway is the "highest" road class (by traffic capacity). In an exemplary embodiment, the first road class is lower than the second road class. In a non-limiting example, the first road class is a local road and the second road class is a connecting road.

[0065] like Figure 1As shown, in an exemplary embodiment, first road 40a and second road 40b intersect at node location 46. Within the scope of this disclosure, node location 46 defines the location of the intersection between the road on which vehicle 12 is traveling (e.g., first road 40a) and a higher-level road (e.g., second road 40b). In an exemplary embodiment, traffic signal 42 is used to control the intersection between first road 40a and second road 40b. In a non-limiting example, traffic signal 42 includes one or more lights that can be illuminated based on signal phase and timing (SPaT) data. Within the scope of this disclosure, SPaT data contains information about traffic signal 42, such as the current signal phase, the remaining time until the next phase (i.e., the remaining time until the next signal phase changes), the future signal phase timing (i.e., the timing and duration of the upcoming signal phase), pedestrian crossing signal phase, etc.

[0066] SPaT data is managed and communicated by traffic control infrastructure. For example, the traffic control infrastructure managing SPaT data may include a traffic management center (i.e., a centralized facility for monitoring and managing traffic flow), roadside units (i.e., devices installed near node location 46 and configured to manage SPaT data), traffic signal controllers (i.e., devices installed near node location 46, primarily configured to control the timing and sequencing of traffic signals 42), and / or traffic signals 42 themselves. In an exemplary embodiment, SPaT data is periodically sent by the traffic control infrastructure to server system 10b, as will be discussed in more detail below.

[0067] One or more remote vehicles 44 are traveling on a segment of second road 40b. Within the scope of this disclosure, a segment of second road 40b is a portion of second road 40b within a predetermined distance (e.g., one mile) from node location 46 and adjacent to node location 46 (i.e., including or directly bordering node location 46). In an exemplary embodiment, each of the one or more remote vehicles 44 includes a remote vehicle controller 50 and a plurality of remote vehicle sensors 52 electrically communicating with the remote vehicle controller 50. In an exemplary embodiment, the remote vehicle controller 50 is structurally and functionally similar to the vehicle controller 14 described above. The plurality of remote vehicle sensors 52 are structurally and functionally similar to the plurality of vehicle sensors 16 described above, and include at least a remote vehicle telemetry sensor and a remote vehicle communication system. In an exemplary embodiment, the remote vehicle controller 50 is programmed to repeatedly determine remote vehicle telemetry data (i.e., position, speed, heading, and / or acceleration) for each of the one or more remote vehicles 44 using the plurality of remote vehicle sensors 52, and to transmit the telemetry data to server system 10b using the remote vehicle communication system. It should be understood that although a passenger car is depicted, one or more remote vehicles 44 may include any type of vehicle traveling on the second road 40b.

[0068] refer to Figure 2 The diagram illustrates a flowchart of a method 100 for providing traffic information to vehicle occupants. The method 100 begins at block 102 and proceeds to block 104. At block 104, the vehicle controller 14 uses multiple vehicle sensors 16 to determine the position of the vehicle 12 and transmits the position of the vehicle 12 to the server system 10b using a vehicle communication system. After block 104, the method 100 proceeds to block 106.

[0069] At block 106, server controller 30 uses server communication system 34 to receive the location of vehicle 12 sent at block 104 and identify node location 46. In an exemplary embodiment, to identify node location 46, server controller 30 searches a detailed map to identify an intersection between the road on which vehicle 12 is traveling (e.g., determined based on the location of vehicle 12, i.e., first road 40a) and another road with a higher road class (i.e., second road 40b). In an exemplary embodiment, node location 46 is further identified based at least in part on the navigation destination of vehicle 12. In a non-limiting example, node location 46 is determined to be located at an intersection along the navigation path of vehicle 12. After block 106, method 100 proceeds to block 108.

[0070] At block 108, server controller 30 receives remote vehicle telemetry data from one or more remote vehicles 44 traveling on a segment of second road 40b. In an exemplary embodiment, server controller 30 uses server communication system 34 to receive the remote vehicle telemetry data. In a non-limiting example, the remote vehicle telemetry data includes at least the location of each of the one or more remote vehicles 44 within the segment of second road 40b. In another non-limiting example, the remote vehicle telemetry data includes at least the location of each of a subset of one or more remote vehicles 44 within a predetermined range (e.g., one mile) of node location 46. In an exemplary embodiment, remote vehicle telemetry data from each of the one or more remote vehicles 44 is stored in server database 32 and aggregated at least within the most recent historical time period (e.g., the previous ten minutes).

[0071] At block 108, server controller 30 also receives SPaT data from traffic signal 42. In an exemplary embodiment, server system 10b uses server communication system 34 to receive the SPaT data. In a non-limiting example, the SPaT data includes at least data regarding the operation of traffic signal 42 within a recent historical time period. In an exemplary embodiment, server controller 30 also receives SPaT data from other traffic signals within a predetermined radius (e.g., two miles) of node location 46. Following block 108, method 100 proceeds to blocks 110, 112, and 114.

[0072] At boxes 110, 112, and 114, server controller 30 determines traffic data regarding one or more remote vehicles 44. Within the scope of this disclosure, traffic data is data relating to the movement of one or more remote vehicles 44 and / or congestion on a segment of second road 40b. At box 110, server controller 30 analyzes remote vehicle telemetry data and / or SPAT data received at box 108 to determine the percentage of one or more remote vehicles 44 traveling on a segment of second road 40b at a speed below the segment speed limit (e.g., 50 mph) within the most recent historical time period. Within the scope of this disclosure, the speed limit is the legally mandated maximum permissible speed on a segment of second road 40b.

[0073] In an exemplary embodiment, the speed limit of a road segment is retrieved from a detailed map stored in server database 32. In a non-limiting example, server controller 30 compares the average speed of one or more remote vehicles 44 over a recent historical time period with the speed limit of the road segment of second road 40b to determine the percentage of one or more remote vehicles 44 traveling at a speed lower than the speed limit of the road segment of second road 40b (e.g., 50 miles per hour) during the recent historical time period. Following box 110, method 100 proceeds to box 116, as will be discussed in more detail below.

[0074] At block 112, server controller 30 analyzes the remote vehicle telemetry data and / or SPaT data received at block 108 to determine the percentage of one or more remote vehicles 44 traveling on a segment of second road 40b at a free-driving speed (e.g., 50 mph) lower than the free-driving speed of the segment of second road 40b in the most recent historical time period. Within the scope of this disclosure, free-driving speed is the average vehicle speed measured during a low-traffic period under favorable conditions, including good weather and no roadworks or traffic accidents. In an exemplary embodiment, the free-driving speed of a road segment is determined based on long-term historical telemetry data (e.g., on a timescale of weeks or months) stored in server database 32. In a non-limiting example, server controller 30 compares the average speed of each of the one or more remote vehicles 44 in the most recent historical time period with the free-driving speed of the segment of second road 40b to determine the percentage of one or more remote vehicles 44 traveling on a segment of second road 40b at a free-driving speed (e.g., 60 mph) lower than the free-driving speed of the segment of second road 40b in the most recent historical time period. Following box 112, method 100 proceeds to box 116, which will be discussed in more detail below.

[0075] At box 114, server controller 30 determines the Level of Business (LOS) classification of a segment of second road 40b within a recent historical time period. Within the scope of this disclosure, LOS is a quality measure describing the operational status within traffic flow. LOS defines the vehicle traffic flow conditions along a street or road (e.g., a segment of second road 40b). In a non-limiting example, the LOS of a segment of second road 40b is classified as one of the following: LOS A, LOS B, LOS C, LOS D, LOS E, or LOS F. LOS A corresponds to free-flowing, uninterrupted vehicle traffic. LOS B corresponds to stable vehicle traffic, but other vehicles are noteworthy. LOS C corresponds to stable vehicle traffic, but vehicle operation is affected by other vehicles. LOS D corresponds to high-density free-flowing traffic, where vehicle operation is affected by other vehicles. LOS E corresponds to high-density traffic flow approaching road capacity and with extremely poor vehicle operation. LOS F corresponds to interrupted traffic flow exceeding road capacity (e.g., stop-and-go).

[0076] In an exemplary embodiment, to determine the LOS classification of a segment of the second road 40b, server controller 30 analyzes remote vehicle telemetry data and / or SPAT data received at block 108. In a non-limiting example, server controller 30 compares the average speed and traffic density (e.g., number of vehicles per square meter) of each of one or more remote vehicles 44 within the most recent historical time period with one or more predetermined thresholds to determine the LOS classification of the segment of the second road 40b within the most recent historical time period. Following block 114, method 100 proceeds to block 116.

[0077] At box 116, server controller 30 determines the road segment traffic profile. Within the scope of this disclosure, the road segment traffic profile describes the perceived traffic level on a segment of second road 40b within a recent historical time period. Within the scope of this disclosure, the perceived traffic level corresponds to the difficulty level of passing through the intersection at node location 46. Within the scope of this disclosure, passing through an intersection includes, for example, proceeding straight through the intersection or turning at the intersection. Within the scope of this disclosure, "difficulty level" characterizes the amount of time spent waiting for an opportunity to pass through an intersection, where a longer waiting time corresponds to a higher difficulty level. In another non-limiting example, "difficulty level" characterizes the level of stress or cognitive exertion required by an occupant to pass through an intersection, where higher stress or cognitive exertion corresponds to a higher difficulty level.

[0078] In an exemplary embodiment, server controller 30 determines the road segment traffic profile based at least in part on the percentage of one or more remote vehicles 44 traveling below the speed limit of the second road 40b, as determined at block 110. In a non-limiting example, a relatively high percentage (e.g., more than 50 percent) of one or more remote vehicles 44 traveling below the speed limit represents a high value for the road segment traffic profile at any given time.

[0079] In an exemplary embodiment, server controller 30 determines the road segment traffic profile based at least in part on the percentage of one or more remote vehicles 44 traveling at a speed lower than the free-driving speed of the road segment of the second road 40b, as determined at block 112. In a non-limiting example, a relatively high percentage (e.g., more than 50 percent) of one or more remote vehicles 44 traveling at a speed lower than the free-driving speed represents a high value for the road segment traffic profile at any given time.

[0080] In an exemplary embodiment, server controller 30 determines the road segment traffic profile based at least in part on the LOS classification of the road segment of the second road 40b determined at block 114. In a non-limiting example, poor LOS (e.g., LOSD or LOS E) represents a high value of the road segment traffic profile at any given time.

[0081] In an exemplary embodiment, server controller 30 determines the segment traffic profile based at least in part on SPaT data received at block 108. In a non-limiting example, SPaT data indicating a long red light (i.e., no-passing) phase in the direction of travel of one or more remote vehicles 44 indicates a high value for the segment traffic profile at any given time.

[0082] In an exemplary embodiment, server controller 30 determines road segment conditions based at least in part on remote vehicle telemetry data received at block 108. In a non-limiting example, remote vehicle telemetry data indicating relatively high traffic density (e.g., number of vehicles per square meter) represents a high value for road segment traffic conditions at any given time. As described above, road segment traffic conditions describe the perceived traffic level over time. Therefore, road segment traffic conditions describe how the perceived traffic level changes over time. Following block 116, method 100 proceeds to block 118.

[0083] At block 118, server controller 30 fits the road segment traffic profile determined at block 116 to a periodic curve. In an exemplary embodiment, a regression or curve fitting algorithm (e.g., a linear, polynomial, exponential, or logarithmic curve fitting algorithm) is used to fit the road segment traffic profile, the algorithm generating one or more parameters characterizing the periodic curve based at least in part on the road segment traffic profile. In a non-limiting example, the curve fitting algorithm uses an iterative process to determine the optimal value of each of the one or more parameters. In a non-limiting example, the one or more parameters include a maximum flow value, a minimum flow value, a frequency, a period, and one or more coefficients used in a mathematical equation describing the curve fitting, etc. In a non-limiting example, the periodic curve is a sine wave, cosine wave, square wave, triangular wave, sawtooth wave, arbitrary periodic wave, etc. The one or more parameters and the periodic curve can be used to estimate or predict past or future values ​​of the road segment traffic profile. After block 118, method 100 proceeds to block 120.

[0084] At block 120, server controller 30 identifies a recurring time period when road segment traffic conditions reach a minimum. In an exemplary embodiment, server controller 30 identifies the recurring time period when road segment traffic conditions reach a minimum based at least in part on a periodic curve identified at block 118 and one or more parameters. In a non-limiting example, the recurring time period is defined by the period of the periodic curve relative to the minimum traffic value. After block 120, method 100 via... Figure 3 The cross-page connector proceeds to boxes 122 and 124.

[0085] refer to Figure 3 This illustrates a method 100 for providing traffic information to vehicle occupants. Figure 2 The flowchart is continued. At block 122, server controller 30 determines an estimated delay time. Within the scope of this disclosure, the estimated delay time is the time required for the perceived traffic level (i.e., the traffic profile of the road segment) on the second road 40b to reach a minimum flow value. In an exemplary embodiment, the estimated delay time is determined at least in part based on one or more parameters, a periodic curve, and the current perceived traffic level of the second road segment 40b. In a non-limiting example, server controller 30 determines the current perceived traffic level of the second road segment 40b at least in part based on remote vehicle telemetry data. In a non-limiting example, server controller 30 determines the estimated delay time by matching the current perceived traffic level with a point on the periodic curve determined at block 118 and subsequently measuring the time between the matching point on the periodic curve and the minimum flow value. After block 122, method 100 proceeds to block 126, as will be discussed in more detail below.

[0086] At block 124, server controller 30 determines the estimated travel time for vehicle 12 to reach node location 46. In an exemplary embodiment, the estimated travel time is determined at least in part based on the position of vehicle 12 and node location 46. In a non-limiting example, server controller 30 determines the distance between vehicle 12 and node location 46. Subsequently, server controller 30 determines the estimated travel time at least in part based on the free-traffic speed of the first road 40a. In another exemplary embodiment, server controller 30 uses a detailed map stored in server database 32 to determine a navigation path between the position of vehicle 12 and node location 46. Server controller 30 then determines the estimated travel time at least in part based on the navigation path. After block 124, method 100 proceeds to block 126.

[0087] At block 126, server controller 30 determines an estimated waiting time. Within the scope of this disclosure, the estimated waiting time is the estimated amount of time that vehicle 12 needs to wait before it can pass the intersection at node location 46. In an exemplary embodiment, the estimated waiting time is the difference between the estimated delay time determined at block 122 and the estimated travel time determined at block 124.

[0088] t w =t d -t t (1)

[0089] Among them, t w To estimate the waiting time, t d To estimate the delay time, t t To estimate the travel time, after box 126, method 100 proceeds to box 128.

[0090] At block 128, server controller 30 determines an optimal departure delay. Within the scope of this disclosure, the optimal departure delay is the amount of time by which the occupants and / or the autonomous driving system 20 should delay leaving the position of vehicle 12 such that the estimated waiting time is zero when vehicle 12 arrives at node position 46. In an exemplary embodiment, the optimal departure delay is equal to the estimated waiting time determined at block 126.

[0091] In another exemplary embodiment, the optimal departure delay is greater than or equal to the estimated waiting time because the optimal departure delay also takes into account the signal phase timing of traffic signal 42 (i.e., determined based on the SPAT data received at block 108), allowing vehicle 12 to avoid stopping upon arrival at traffic signal 42. In a non-limiting example, the optimal departure delay is further adjusted based on the signal phase timing of multiple traffic signals within a predetermined radius (e.g., two miles) of node location 46 and / or along the planned route of vehicle 12 (i.e., determined based on the SPAT data received at block 108), so that vehicle 12 experiences a “green wave.” Within the scope of this disclosure, the term “green wave” refers to the phenomenon where vehicle 12 experiences multiple green traffic signals consecutively due to the coordination of the vehicle’s motion and / or route (e.g., speed and / or position) with SPAT data from multiple traffic signals. Following block 128, method 100 proceeds to block 130.

[0092] At box 130, server controller 30 compares the optimal departure delay determined at box 128 with zero. If the optimal departure delay is equal to or close to zero, the current time is the optimal departure time, such that the estimated waiting time when vehicle 12 arrives at node position 46 is zero. If the optimal departure delay is within a predetermined range of zero (e.g., from zero plus or minus two seconds), method 100 proceeds to boxes 132 and 134, as will be discussed in more detail below. If the optimal departure delay is not within a predetermined range of zero, method 100 proceeds only to box 132.

[0093] At box 132, server controller 30 uses server communication system 34 to transmit the optimal departure delay and estimated waiting time to vehicle controller 14. Vehicle controller 14 receives the optimal departure delay and estimated waiting time using vehicle communication system. Subsequently, vehicle controller 14 uses vehicle display 18 to provide notification to the occupants of vehicle 12, at least in part, based on the optimal departure delay and / or estimated waiting time. In a non-limiting example, the notification includes a text and / or graphic message instructing the occupants to delay their departure by the optimal departure delay. In another non-limiting example, the notification includes a text and / or graphic message informing the occupants of the estimated waiting time. In yet another non-limiting example, the notification includes a text and / or graphic message informing the occupants of the optimal vehicle speed for reaching node position 46, such that the estimated waiting time is zero. After box 132, method 100 proceeds to the standby state at box 136.

[0094] At block 134, server controller 30 transmits the optimal departure delay and estimated waiting time to vehicle controller 14 using server communication system 34. Vehicle controller 14 receives the optimal departure delay and estimated waiting time using vehicle communication system. Subsequently, vehicle controller 14 initiates an autonomous driving route using autonomous driving system 20 in response to determining that the optimal departure delay is within a predetermined range of zero. Within the scope of this disclosure, initiating an autonomous driving route means that vehicle controller 14 controls autonomous driving system 20 to begin driving a predetermined and / or pre-planned autonomous driving route toward a predetermined and / or pre-planned destination. The predetermined and / or pre-planned autonomous driving route includes node location 46. In a non-limiting example, vehicle controller 14 controls autonomous driving system 20 to travel at an optimal vehicle speed to reach node location 46, such that the estimated waiting time is zero. After block 134, method 100 continues into a standby state at block 136.

[0095] In an exemplary embodiment, the vehicle controller 14 repeatedly exits standby state 136 and restarts method 100 at block 102. In a non-limiting example, the vehicle controller 14 exits standby state 136 and restarts method 100 according to a timer, for example, every three hundred milliseconds.

[0096] The system 10 and method 100 of this disclosure offer several advantages. Using system 10 and method 100, vehicle 12 can more easily pass through the intersection at node location 46, despite the difference in road class between the first road 40a and the second road 40b. Using system 10 and method 100 improves occupant comfort and convenience. Furthermore, by taking into account SPaT data from nearby traffic signals, system 10 and method 100 facilitate “green wave” traffic through multiple traffic signals, further enhancing occupant comfort and convenience. Additionally, using method 100, system 10 can initiate autonomous driving at the optimal time, reducing occupant waiting time and alleviating traffic and congestion on the first road 40a and the second road 40b. Moreover, the road segment traffic density profile can be used to determine estimated waiting times for additional road users (e.g., pedestrians, cyclists, etc.), and these estimated waiting times are provided to additional road users using node location 46 and / or using physical displays near personal devices (e.g., smartphones).

[0097] The descriptions in this disclosure are merely exemplary in nature, and variations thereof that do not depart from the spirit and scope of this disclosure are intended to fall within its scope. Such variations should not be considered as departing from the spirit and scope of this disclosure.

Claims

1. A method for providing traffic information to occupants of a vehicle, the method comprising: Identify node locations in the environment surrounding the vehicle, wherein the node location is the location of the intersection between a first road and a second road on which the vehicle is traveling, wherein the first road has a first road class and the second road has a second road class, and wherein the first road class is lower than the second road class; Determine traffic data for one or more remote vehicles traveling on a segment of the second road, wherein the segment of the second road is adjacent to the node location; The estimated waiting time of the vehicle is determined at least in part based on the traffic data and the distance between the vehicle and the node location; as well as The first action is performed at least in part based on the estimated waiting time.

2. The method according to claim 1, wherein, Determining the traffic data also includes: Receive remote vehicle telemetry data from the one or more remote vehicles, wherein the remote vehicle telemetry data includes at least the location of each of the one or more remote vehicles; and The traffic data is determined at least in part based on the remote vehicle telemetry data.

3. The method according to claim 2, wherein, Determining the traffic data also includes: Determine the percentage of the one or more remote vehicles traveling at speeds below the speed limit of the second road segment during the most recent historical time period; Determine the percentage of one or more remote vehicles traveling at a speed lower than the free-driving speed of the segment of the second road during the most recent historical time period; Determine the business classification level of the road segment of the second road within the most recent historical time period; and The road segment traffic profile is determined at least in part based on at least one of the following: the percentage of one or more remote vehicles traveling at a speed limit lower than that of the segment of the second road, the percentage of one or more remote vehicles traveling at a speed lower than that of the free-driving speed of the segment of the second road, and the business classification level of the segment of the second road, wherein the road segment traffic describes the perceived traffic level on the segment of the second road during the most recent historical time period.

4. The method according to claim 3, wherein, Determining the traffic data also includes: Receive signal phase and timing (SPaT) data from traffic signals at the node location within the most recent historical time period; and The road segment traffic profile is determined at least in part based on at least one of the following: the percentage of one or more remote vehicles traveling at a speed limit lower than that of the segment of the second road, the percentage of one or more remote vehicles traveling at a speed lower than that of the free-driving speed of the segment of the second road, the traffic classification level of the segment of the second road, and the SpaT data, wherein the road segment traffic profile describes the perceived traffic level on the segment of the second road during the most recent historical time period.

5. The method according to claim 3, wherein, Determining the estimated waiting time also includes: Identify the recurring time periods when the traffic conditions on the road segment reach their minimum; and The estimated waiting time is determined at least in part based on the repeated time period when the traffic conditions of the road segment are close to the minimum.

6. The method according to claim 5, wherein, Identifying the recurring time periods when the traffic conditions on the road segment reach their minimum also includes: Fit the traffic conditions of the road segment to a periodic curve; Determine one or more parameters characterizing the periodic curve, wherein the one or more parameters include at least a minimum flow rate value and a period; and The recurring time period is identified at least in part based on the minimum flow rate and the period.

7. The method according to claim 6, wherein, Determining the estimated waiting time based at least in part on the repetition time period also includes: The estimated delay time is determined at least in part based on one or more parameters characterizing the periodic curve and the current perceived traffic level of the segment of the second road, the estimated delay time being the time until the perceived traffic level on the segment of the second road is estimated to reach the minimum flow value; The estimated travel time for the vehicle to reach the node location is determined at least in part based on the distance between the vehicle and the node location and the free-travel speed of the first road; and The estimated waiting time is determined at least in part based on the estimated delay time and the estimated travel time, wherein the estimated waiting time is the difference between the estimated delay time and the estimated travel time.

8. The method according to claim 1, wherein, Performing the first action also includes: The vehicle's occupants are notified using a vehicle display, at least in part, based on the estimated waiting time.

9. The method according to claim 8, wherein, Providing the notification also includes: The optimal departure delay is determined at least in part based on the estimated waiting time, wherein the optimal departure delay is the amount of time by which the occupants should delay their departure so that the estimated waiting time is zero upon arrival at the node location; and The notification is provided to the occupants of the vehicle, at least in part, based on the optimal departure delay.

10. The method according to claim 1, wherein, Performing the first action also includes: The optimal departure delay is determined at least in part based on the estimated waiting time, wherein the optimal departure delay is the amount of time by which the vehicle should delay its departure so that the estimated waiting time is zero when it arrives at the node location; Compare the optimal departure delay time with zero; and In response to determining that the optimal departure delay is within a predetermined zero range, the vehicle's autonomous driving system initiates an autonomous driving route.

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