DETERMINING AN OPTIMAL DEPARTURE TIME FOR A VEHICLE

Abstract

A method for providing traffic information to a vehicle occupant may include identifying a junction location in the environment surrounding the vehicle. The junction location is the point of intersection between a first road of a first road class, on which the vehicle is traveling, and a second road of a second road class. The first road class is lower than the second road class. The method may further include determining traffic data about one or more long-distance vehicles traveling on a segment of the second road adjacent to the junction location. The method may further include determining an estimated waiting time for the vehicle, based at least in part on the traffic data and a distance between the vehicle and the junction location. The method may further include performing an initial action based at least in part on the estimated waiting time.

Description

INTRODUCTION

[0001] The present disclosure relates to advanced driver assistance systems and automated driving systems and methods for vehicles, and in particular to systems and methods for relieving traffic congestion and increasing passenger comfort for a vehicle.

[0002] To enhance occupant awareness and comfort, vehicles may be equipped with advanced driver assistance systems (ADAS) and / or automated driving systems (ADS). ADAS systems can use various sensors, such as cameras, radar, and LiDAR, to detect and identify objects in the vehicle's surroundings, including other vehicles, pedestrians, road configurations, and traffic signs. Based on the environmental conditions around the vehicle, ADAS systems can take action, such as applying the brakes or warning a vehicle occupant. However, current ADS systems may not consider additional factors that can influence the occupant experience. For example, passing through an intersection between a lower-class road (e.g., a local road) and a higher-class road (e.g., a highway) may not provide sufficient information for the occupant to perceive the road ahead.Delays can occur on roads with higher traffic volumes (e.g., a collector road) due to the higher traffic density on higher-class roads. Furthermore, waiting at traffic signals can also cause delays.

[0003] US 2021 / 0201669A1 describes a method and a congestion management system for reducing traffic congestion. Traffic data relating to a large number of vehicles is analyzed by a trained traffic model to predict the speed of each vehicle and the signal times for intersections. Subsequently, based on an analysis of previous values ​​and historical traffic data, the optimal speed for each vehicle and an optimal signal time for each intersection are determined. Finally, the determined optimal speed and signal time are provided to a vehicle control system assigned to each vehicle and a traffic controller assigned to each intersection.

[0004] DE 10 2023 002 887 A1 describes a method for assisting a vehicle in merging back into traffic, wherein said method comprises the following steps: detection of the vehicle's brief stop when briefly leaving the road; in response to said detection of the brief stop, acquisition of information on the status of the traffic light at the intersection to be crossed by the vehicle after merging back into traffic; at least based on said status information, generation and output of start-up guidance signals, wherein said start-up guidance signals assist the vehicle in crossing said intersection without stopping after merging back into traffic.

[0005] DE 10 2022 119 256 A1 describes a system for vehicle speed planning based on the timing of traffic lights. The system comprises one or more vehicle sensors, a transceiver, and a processor. The one or more sensors are configured to receive vehicle sensor data relating to the vehicle's operation. The transceiver is configured to receive traffic light data relating to a multitude of traffic lights along a path or roadway on which the vehicle is traveling.The processor is coupled with one or more sensors and the transceiver and is configured to at least enable: determining a desired motion control of the vehicle based on the vehicle sensor data, the traffic light data and one or more optimization criteria relating to the vehicle; and performing a vehicle action based on the desired motion control of the vehicle.

[0006] DE 10 2015 015 868 A1 describes a vehicle system with a speed sensor that measures the speed of a receiving vehicle and a processing device programmed to compare the speed with a speed threshold and determine the time period during which the speed is below the speed threshold. The processing device detects the presence of a traffic control device based on the time period during which the speed is below the speed threshold.

[0007] While ADAS and ADS systems and procedures fulfill their purpose, there is a need for a new and improved system and procedure for providing traffic information to the occupants of a vehicle. DESCRIPTION

[0008] According to the invention, a method for providing traffic information to a vehicle occupant is provided. The method may include identifying an intersection location in the environment surrounding the vehicle. The intersection location is the location of an junction between a first road on which the vehicle is traveling and a second road. 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 further include determining traffic data about one or more long-distance vehicles traveling on a segment of the second road. The segment of the second road is adjacent to the intersection location. The method may further include determining an estimated waiting time for the vehicle, which is based at least partially on the traffic data and a distance between the vehicle and the intersection location.The procedure may further include performing an initial action that is at least partially based on the estimated waiting time. Determining the traffic data further includes determining a percentage of the one or more long-distance vehicles traveling below a speed limit on the second road segment over a recent time period. Determining the traffic data may further include determining a percentage of the one or more long-distance vehicles traveling below a free-flow speed on the second road segment during the recent time period. Determining the traffic data may further include determining a service level categorization of the second road segment over the recent time period.Determining the traffic data may further include determining a road segment traffic profile, based at least in part on at least one of the following: the percentage of one or more long-distance vehicles traveling below a speed limit on the second road segment, the percentage of one or more long-distance vehicles traveling below a free-flow speed on the second road segment, and the service-level categorization of the second road segment. The road segment traffic profile describes the total traffic volume on the second road segment over the recent past period.

[0009] In another aspect of the present disclosure, determining the traffic data may further include receiving long-range vehicle telemetry data from the one or more long-range vehicles. The long-range vehicle telemetry data contains at least one location of each of the one or more long-range vehicles. Determining the traffic data may further include determining the traffic data, at least partially, based on the long-range vehicle telemetry data.

[0010] In another aspect of this disclosure, determining the traffic data may further include receiving signal phase and timing (SPaT) data from a traffic signal at the intersection location over the recently elapsed time period. Determining the traffic data may further include determining the road segment traffic profile, which is based at least partially on at least one of the following factors: the percentage of one or more long-distance vehicles traveling below a speed limit on the second road segment, the percentage of one or more long-distance vehicles traveling below a free-flow speed on the second road segment, the service level categorization of the second road segment, and the SPaT data. The road segment traffic profile describes the total traffic volume on the second road segment over the recently elapsed time period.

[0011] In another aspect of the present disclosure, determining the estimated waiting time may further include identifying recurring time periods when the road segment traffic profile reaches a minimum value. Determining the estimated waiting time may also include determining the estimated waiting time based at least partially on the recurring time periods as the road segment traffic profile approaches the minimum value.

[0012] In another aspect of the present disclosure, identifying the recurring time periods when the road segment traffic profile reaches a minimum value may further include fitting the road segment traffic profile to a periodic curve. Identifying the recurring time periods when the road segment traffic profile reaches a minimum value may further include determining one or more parameters that characterize the periodic curve. The one or more parameters include at least a minimum traffic value and a period. Identifying the recurring time periods when the road segment traffic profile reaches a minimum value may further include identifying the recurring time periods based at least partially on the minimum traffic value and the period.

[0013] In another aspect of the present disclosure, determining the estimated waiting time based at least partially on the recurring time intervals may further include determining an estimated delay time until the estimated traffic volume on the segment of the second road reaches the minimum traffic value, based at least partially on one or more parameters that characterize the periodic curve and a current estimated traffic volume of the segment of the second road. Determining the estimated waiting time based at least partially on the recurring time intervals may further include determining an estimated travel time for the vehicle to reach the intersection location based at least partially on the distance between the vehicle and the intersection location and a free-flow velocity of the first road.Determining the estimated waiting time based at least partially on recurring time periods may further include determining the estimated waiting time based at least partially 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.

[0014] In another aspect of the present disclosure, performing the first action may further include providing a notification to the vehicle occupant based at least partially on the estimated waiting time using a vehicle display.

[0015] In another aspect of the present disclosure, providing the notification may further include determining an optimal departure delay, which is based at least partially on the estimated waiting time. The optimal departure delay is a period of time by which the occupant should delay departure so that the estimated waiting time upon reaching the intersection location is zero. Providing the notification may further include providing the notification to the vehicle occupant, which is based at least partially on the optimal departure delay.

[0016] In another aspect of the present disclosure, performing the first action may further include determining an optimal departure delay based at least partially on the estimated waiting time. The optimal departure delay is a time interval by which the vehicle should delay departure so that the estimated waiting time upon reaching the intersection location is zero. Performing the first action may further include comparing the optimal departure delay with zero. Performing the first action may also include initiating an automated route using the vehicle's automated driving system in response to determining that the optimal departure delay lies within a predetermined range of zero.

[0017] A system for providing traffic information to a vehicle occupant is provided according to several aspects. The system may include a server system, a server communication system, and a server control unit in electrical communication with the server communication system. The server control unit is programmed to identify a junction location in the environment surrounding the vehicle. The junction location is the point of intersection between a first road, on which the vehicle is traveling, and a second road. 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 control unit is further programmed to determine, with the help of the server communication system, traffic data about one or more remote vehicles traveling on a segment of the second road. The segment of the second road is adjacent to the junction location.The server control unit is further programmed to determine an estimated waiting time for the vehicle, at least partially, based on traffic data and the distance between the vehicle and the intersection location. The server control unit is also programmed to transmit the estimated waiting time using the server communication system.

[0018] In another aspect of the present disclosure, the server control unit for determining traffic data is further programmed to receive remote vehicle telemetry data from one or more remote vehicles via the server communication system. The remote vehicle telemetry data includes at least the location of each of the one or more remote vehicles. To determine the traffic data, the server control unit is further programmed to determine the traffic data, at least partially, based on the remote vehicle telemetry data.

[0019] In another aspect of the present disclosure, the server control unit for determining traffic data is further programmed to determine a percentage of the one or more long-distance vehicles that, in a recently elapsed time period, traveled below a speed limit on the segment of the second road, based at least partially on the long-distance vehicle telemetry data. To determine the traffic data, the server control unit is further programmed to determine a percentage of the one or more long-distance vehicles that traveled below a free-flow speed on the segment of the second road over the recently elapsed time period, based at least partially on the long-distance vehicle telemetry data.To determine the traffic data, the server control unit is further programmed to determine a service level categorization of the second road segment over the recently elapsed time period, at least partially based on the remote vehicle telemetry data. To determine the traffic data, the server control unit is also programmed to receive signal phase and timing (SPaT) data from a traffic signal at the intersection location over the recently elapsed time period.To determine the traffic data, the server control unit is further programmed to determine a road segment traffic profile, which is based at least partially on at least one of the following factors: the percentage of one or more long-distance vehicles traveling below a speed limit on the second road segment, the percentage of one or more long-distance vehicles traveling below a free-flow speed on the second road segment, the service level categorization of the second road segment, and the SPaT data. The road segment traffic profile describes the year-round traffic volume on the second road segment over the recently elapsed period.

[0020] In another aspect of the present disclosure, the server control unit is further programmed to fit the road segment traffic profile to a periodic curve in order to determine the estimated waiting time. To determine the estimated waiting time, the server control unit is further programmed to determine one or more parameters that characterize the periodic curve. The one or more parameters include at least a minimum traffic value and a period. To determine the estimated waiting time, the server control unit is further programmed to identify recurring time intervals when the road segment traffic profile reaches a minimum value, which is based at least partially on the minimum traffic value and the period.To determine the estimated waiting time, the server control unit is further programmed to determine the estimated waiting time, at least partially, based on recurring time periods when the road segment traffic profile approaches the minimum value.

[0021] In another aspect of the present disclosure, the server control unit is further programmed to determine an estimated waiting time until the recurring traffic volume on the segment of the second road reaches an estimated minimum traffic value, based at least partially on one or more parameters that characterize the periodic curve and a current recurring traffic volume of the segment of the second road. To determine the estimated waiting time, the server control unit is further programmed to determine an estimated travel time for the vehicle to reach the intersection location, based at least partially on the distance between the vehicle and the intersection location and a free-flow velocity of the first road.To determine the estimated waiting time, the server control unit is further programmed to calculate the estimated waiting time, at least partially, based on the estimated delay time and the estimated travel time. The estimated waiting time is the difference between the estimated waiting time and the estimated travel time.

[0022] In another aspect of the present disclosure, the system further comprises a vehicle system. The vehicle system may include a vehicle communication system, a vehicle display, and a vehicle control unit electrically connected to the vehicle communication system and the vehicle display. The vehicle control unit is programmed to receive the estimated waiting time from the server system via the vehicle communication system. The vehicle control unit is further programmed to provide the vehicle occupant with a notification via the vehicle display, based at least in part on the estimated waiting time.

[0023] In another aspect of the present disclosure, the vehicle system further comprises an automated driving system that is electrically connected to the vehicle control unit. The vehicle control unit is further programmed to determine an optimal departure deceleration, which is based at least partially on the estimated waiting time. The optimal departure deceleration is a time interval by which the vehicle should delay departure so that the estimated waiting time upon reaching the intersection location is zero. The vehicle control unit is further programmed to compare the optimal departure deceleration with zero. The vehicle control unit is further programmed to initiate an automated driving route using the automated driving system when it determines that the optimal departure deceleration is within a predetermined range of zero.

[0024] According to several aspects, a method for providing traffic information to a vehicle occupant is provided. The method may include identifying a junction location in the environment surrounding the vehicle. The junction location is the point of intersection between a first road on which the vehicle is traveling and a second road. 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 further include receiving long-range vehicle telemetry data from one or more long-range vehicles traveling on a segment of the second road. The long-range vehicle telemetry data includes at least one location of each of the one or more long-range vehicles. The segment of the second road is adjacent to the junction location.The method may further include receiving signal phase and timing (SPaT) data from a traffic signal at the intersection location. The method may further include determining a road segment traffic profile based at least partially on the long-range vehicle telemetry data and the SPaT data. The road segment traffic profile describes the year-round traffic volume on the second road segment over a recently elapsed period. The method may further include determining an estimated waiting time for the vehicle based at least partially on the long-range vehicle telemetry data, the SPaT data, and a distance between the vehicle and the intersection location. The method may further include providing a message to the vehicle occupant based at least partially on the estimated waiting time using a vehicle display.

[0025] In another aspect of the present disclosure, determining the estimated waiting time may further include fitting the road segment traffic profile to a periodic curve. Determining the estimated waiting time may further include determining one or more parameters that characterize the periodic curve. The one or more parameters include at least a minimum traffic value and a period. Determining the estimated waiting time may further include identifying recurring time intervals when the road segment traffic profile reaches a minimum value that is based at least partially on the minimum traffic value and the period.Determining the estimated waiting time may further include determining an estimated waiting time until the recurring traffic volume on the second road segment reaches the minimum traffic value, based at least in part on the one or more parameters that characterize the periodic pattern and a current recurring traffic volume on the second road segment. Determining the estimated waiting time may further include determining an estimated travel time for the vehicle to reach the intersection location based at least in part on the distance between the vehicle and the intersection location and a free-flow velocity 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 waiting time and the estimated travel time.

[0026] In another aspect of the present disclosure, the method may further include determining an optimal departure delay, which is based at least partially on the estimated waiting time. The optimal departure delay is a period of time by which the vehicle should delay departure so that the estimated waiting time upon reaching the intersection is zero. The method may further include comparing the optimal departure delay with zero. The method may also include initiating an automated route using the vehicle's automated driving system in response to determining that the optimal departure delay lies within a predetermined range of zero.

[0027] Further areas of application will become apparent from the present description. It should be understood that the description and specific examples serve only for illustration and are not intended to limit the scope of this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings described here are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. Fig. Figure 1 is a schematic representation of a system for providing traffic information to an occupant of a vehicle, according to an exemplary embodiment; Fig. Figure 2 is a flowchart of a method for providing traffic information to the vehicle occupants, according to an exemplary embodiment; and Fig. 3 is a continuation of the flowchart from Fig. 2 of the method for providing traffic information to the occupants of the vehicle, according to an exemplary embodiment. DETAILED DESCRIPTION

[0029] The following description is merely exemplary and is not intended to limit the present disclosure, application or use.

[0030] Passing through an intersection between a lower-class road (e.g., a local road) and a higher-class road (e.g., a collector road) can lead to delays due to the higher traffic density on the higher-class road. Furthermore, waiting at traffic signals can also cause delays. Accordingly, the present disclosure provides a new and improved system and method for supplying traffic information to a vehicle occupant, which takes into account traffic density and the behavior of traffic signals to determine an optimal departure time for the vehicle in order to minimize delays and traffic congestion.

[0031] In Fig. System 1 is a system for providing traffic information to a vehicle occupant and is generally referred to by the reference number 10. System 10 generally comprises a vehicle system 10a and a server system 10b.

[0032] The vehicle system 10a is illustrated with an exemplary vehicle 12. Although a passenger vehicle is shown, the vehicle 12 can be any type of vehicle without this deviating from the scope of this disclosure. The vehicle system 10a generally comprises a vehicle control unit 14, a plurality of vehicle sensors 16, a vehicle display 18, and an automated driving system 20.

[0033] The vehicle control unit 14 serves to implement a method 100 for providing traffic information to a vehicle occupant, as described below. The vehicle control unit 14 comprises at least one processor and a non-transferable, computer-readable storage device or media. The processor may be a customer-specific or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors connected to the vehicle control unit 14, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or, more generally, a device for executing instructions.

[0034] Computer-readable storage devices or media can include volatile and non-volatile memory, such as read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor is off. The computer-readable storage device(s) can be implemented using a variety of storage devices, such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represents executable instructions used by the vehicle control unit 14 to control various systems of the vehicle 12.

[0035] The vehicle control unit 14 can also consist of several control units that are electrically interconnected. The vehicle control unit 14 can be connected to other systems and / or control units of the vehicle 12, so that the vehicle control unit 14 can access data such as speed, acceleration, braking, and steering angle of the vehicle 12.

[0036] The vehicle control unit 14 is electrically connected to the majority of vehicle sensors 16, the vehicle display 18, and the automated driving system 20. In an exemplary embodiment, the electrical communication is established, for example, via a CAN network, a FLEXRAY network, a local area network (e.g., WiFi, Ethernet, and the like), a serial peripheral interface network (SPI), or the like. It is understood that various additional wired and wireless techniques and communication protocols for communication with the vehicle control unit 14 fall within the scope of this disclosure. It should further 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 conductive wires and / or wireless power transfer techniques).

[0037] The multiple vehicle sensors 16 are used to acquire information relevant to the vehicle 12. In an exemplary embodiment, the majority of the vehicle sensors 16 comprise at least one telemetry sensor (not shown) and a vehicle communication system (not shown).

[0038] The telemetry sensor is used to collect telemetry data about the vehicle 12. In one exemplary embodiment, the telemetry data includes at least the location of the vehicle 12. In another exemplary embodiment, the telemetry data also includes the speed of the vehicle 12. In yet another exemplary embodiment, the telemetry data further includes the course of the vehicle 12. In a further exemplary embodiment, the telemetry data also includes the acceleration of the vehicle 12. The telemetry sensor is electrically connected to the vehicle control unit 14, as described above.

[0039] In a non-limiting example, the telemetry sensor for determining the location of the vehicle 12 includes a global navigation satellite system (GNSS). The GNSS is used to determine the geographic location of the vehicle 12. In an exemplary embodiment, the GNSS is a global positioning system (GPS). In a non-limiting example, the GPS includes a GPS receiving antenna (not shown) and a GPS controller (not shown) electrically connected to the GPS receiving antenna. The GPS receiving antenna receives signals from multiple satellites, and the GPS controller calculates the geographic location of the vehicle 12 based on the signals received by the GPS receiving antenna. It is understood that various additional types of satellite-based radio navigation systems, such as…The Global Positioning System (GPS), Galileo, GLONASS and the BeiDou Navigation Satellite System (BDS) fall within the scope of this disclosure.

[0040] In a non-restrictive example, the telemetry sensor for determining the speed, heading, and acceleration of the vehicle 12 also includes an inertial measurement unit (IMU). The IMU is used to determine the orientation, speed, and gravitational forces acting on the vehicle 12. In an exemplary embodiment, the IMU includes multiple sensors, including accelerometers, gyroscopes, and / or magnetometers. In a non-restrictive example, the IMU includes tri-axis accelerometers and tri-axis gyroscopes integrated into a single unit. The accelerometers measure the linear acceleration along each axis, while the gyroscopes measure the angular velocity about each axis. The IMU processes the sensor data to determine the current orientation, speed, heading, and yaw rate (i.e.,to calculate the rate of course change) and the acceleration of vehicle 12 in three-dimensional space.

[0041] The vehicle's communication system is used by the vehicle control unit 14 to communicate with other systems outside the vehicle 12 (e.g., the server system 10b, as explained below). For example, the vehicle communication system includes capabilities for communicating with other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems in a remote call center (e.g., GENERAL MOTORS' ON-STAR), and / or personal devices. In general, the term vehicle-to-everything communication (“V2X” communication) refers to communication between the vehicle 12 and any remote system (e.g., vehicles, infrastructure, and / or remote systems).

[0042] In certain embodiments, the vehicle communication system is a wireless communication system configured to communicate over a wireless local area network (WLAN) using IEEE 802.11 standards or using cellular data communication (e.g., using GSMA standards such as SGP.02, SGP.22, SGP.32, and the like). Accordingly, the vehicle communication system may further include an embedded universal integrated circuit (eUICC) configured to store at least one configuration profile for cellular connectivity, such as a profile for an embedded subscriber identity module (eSIM).

[0043] The vehicle communication system is further configured to communicate via a personal network (e.g., Bluetooth), near-field communication (NFC), and / or any other type of radio frequency communication. However, additional or alternative communication methods, such as a dedicated short-range communication channel (DSRC) and / or mobile telecommunications protocols based on the standards of the 3rd Generation Partnership Project (3GPP), are also considered within the scope of this disclosure. DSRC channels refer to one-way or two-way short- to medium-range wireless communication channels specifically designed for use in motor vehicles, as well as a range of protocols and standards. The 3GPP is a partnership of several standards organizations that develop protocols and standards for mobile telecommunications. 3GPP standards are structured as releases.Therefore, communication methods based on 3GPP versions 14, 15, 16 and / or future 3GPP versions fall within the scope of this disclosure.

[0044] Accordingly, 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 (CSMs). The vehicle communication system is configured to wirelessly transmit information between the vehicle 12 and another vehicle. Furthermore, the vehicle communication system is configured to wirelessly transmit information between the vehicle 12 and the infrastructure or other vehicles. It is understood that the vehicle communication system may be integrated into the vehicle control unit 14 (e.g., on the same circuit board as the vehicle control unit 14 or otherwise as part of the vehicle control unit 14) without this deviating from the scope of this disclosure.

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

[0046] In another exemplary embodiment, the plurality of vehicle sensors 16 further comprises sensors for determining information about an environment inside the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 further comprises at least a seat occupancy sensor, a cabin air temperature sensor, a cabin motion detection sensor, a cabin camera, a cabin microphone and / or the like.

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

[0048] In another exemplary embodiment, at least one of the multiple vehicle sensors 16 is a perception sensor capable of perceiving objects and / or measuring distances in the vicinity of the vehicle 12. In a non-limiting example, the majority of the vehicle sensors 16 comprise a stereoscopic camera with distance-measuring capabilities. In one example, at least one of the multiple vehicle sensors 16 is located inside the vehicle 12, for example, in the headliner of the vehicle 12, with a view through a windshield of the vehicle 12. In another example, at least one of the multiple vehicle sensors 16 is located outside the vehicle 12, for example, on the roof of the vehicle 12, with a view of the environment surrounding the vehicle 12. It is understood that various additional types of perception sensors, such as...LiDAR sensors, ultrasonic sensors, radar sensors, cameras and / or time-of-flight sensors fall within the scope of this disclosure. The multiple vehicle sensors 16 are electrically connected to the vehicle control unit 14, as described above.

[0049] The vehicle display 18 is used to provide information to an occupant of the vehicle 12. For the purposes of this disclosure, the occupant includes a driver and / or a passenger of the vehicle 12. In one exemplary embodiment, the vehicle display 18 is a human-machine interface (HMI) located within the occupant's field of vision and capable of displaying text, graphics, and / or images. It is understood that HMI display systems, including LCD displays, LED displays, and the like, fall within the scope of this disclosure. Further exemplary embodiments in which the vehicle display 18 is arranged in a rearview mirror also fall within the scope of this disclosure.

[0050] In another exemplary embodiment, the vehicle display 18 comprises a head-up display (HUD) configured to provide information to the occupant by projecting text, graphics, and / or images onto the windshield of the vehicle 12. The text, graphics, and / or images are reflected off the windshield of the vehicle 12 and are visible to the occupant without requiring them to take their eyes off the road ahead. In yet another exemplary embodiment, the vehicle display 18 comprises an augmented reality head-up display (AR-HUD). The AR-HUD is a type of HUD configured to enhance the occupant's view of the road ahead by overlaying text, graphics, and / or images onto physical objects in the vicinity of the vehicle 12 within the occupant's field of vision.In one exemplary embodiment, the occupant can interact with the vehicle display 18 via a human interface device (HID), e.g., a touchscreen, an electromechanical switch, a capacitive switch, a rotary knob, and the like. It is understood that additional systems for displaying information to the occupant of the vehicle 12 also fall within the scope of this disclosure. The vehicle display 18 is electrically connected to the vehicle control unit 14, as described above.

[0051] The automated driving system 20 serves to assist the occupant, to increase the occupant's awareness, and / or to control the behavior of the vehicle 12. Within the scope of this disclosure, the automated driving system 20 includes systems that provide the occupant with any level of assistance (e.g., blind spot warning, lane keeping assist, and / or similar), as well as systems capable of autonomously driving the vehicle 12 under some or all conditions (e.g., automatic lane keeping, adaptive cruise control, fully autonomous driving, and / or similar). It is understood that all levels of driving automation, as defined, for example, by the Society of Automotive Engineers (SAE) J3016 (i.e., SAE Level 0, SAE Level 1, SAE Level 2, SAE Level 3, SAE Level 4, and SAE Level 5), fall within the scope of this disclosure.

[0052] In one exemplary embodiment, the automated driving system 20 is configured to detect and / or receive information about the environment surrounding the vehicle 12 and to process this information to provide assistance to the occupant. In some embodiments, the automated driving system 20 is a software module running on the vehicle controller 14. In other embodiments, the automated driving system 20 includes a separate controller for the automated driving system, similar to the vehicle controller 14, which is capable of processing information about the vehicle 12's environment. In one exemplary embodiment, the automated driving system 20 can operate in a manual operating mode, a semi-automated operating mode, and a fully automated operating mode.

[0053] Within the scope of this disclosure, manual operating mode means that the automated driving system 20 provides warnings or notifications to the occupant but does not directly intervene in or control the vehicle 12. In a non-restrictive example, the automated driving system 20 receives information from the majority of the vehicle's sensors 16. Using techniques such as computer vision, the automated driving system 20 understands the vehicle's 12 surroundings and assists the occupant. For example, if the automated driving system 20 detects, based on data from the multiple vehicle sensors 16, that the vehicle 12 is likely to collide with a distant vehicle, the automated driving system 20 can use the vehicle's display 18 to provide a warning to the occupant.

[0054] Within the scope of this disclosure, the semi-automated operating mode means that the automated driving system 20 issues warnings or notifications to the occupant and, in certain situations, can directly intervene in or control the vehicle 12. In a non-limiting example, the automated driving system 20 is additionally in electrical communication with components of the vehicle 12, such as a braking system, a drive system, and / or a steering system of the vehicle 12, so that the automated driving system 20 can control the behavior of the vehicle 12. In a non-limiting example, the automated driving system 20 can control the behavior of the vehicle 12 by applying the vehicle 12's brakes to avoid an imminent collision. In another non-limiting example, the automated driving system 20 can control the vehicle 12's steering system to provide an automatic lane-keeping function.In another, non-limited example, the automated driving system 20 can control the braking system, the drive system, and the steering system of the vehicle 12 to temporarily drive the vehicle 12 to a predetermined destination. However, occupant intervention may be required at any time. In an exemplary embodiment, the automated driving system 20 can include additional components, such as an eye-monitoring device, configured to monitor the occupant's level of attention and ensure that the occupant is ready to take control of the vehicle 12.

[0055] Within the scope of the present disclosure, the fully automatic operating mode means that the automated driving system 20 uses data from the majority of vehicle sensors 16 to understand the environment and to control the vehicle 12 in order to drive the vehicle 12 to a predetermined destination without requiring any control or intervention by the occupant.

[0056] The automated driving system 20 operates with a path planning algorithm configured to generate a safe and efficient trajectory for the vehicle 12 as it navigates its surroundings. In one 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 by the 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 acquired by the majority of vehicle sensors 16.

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

[0058] It is understood that the automated driving system 20 can contain any software and / or hardware module configured to operate in manual mode, semi-automated mode, or fully automated mode as described above. The automated driving system 20 is electrically connected to the vehicle control unit 14 as described above.

[0059] With further reference to Fig. 1. The server system 10b generally comprises a server control unit 30, which is electrically connected to a server database 32 and a server communication system 34. In a non-restrictive example, the server system 10b is located in a server farm, a data center, or the like, and is connected to the internet.

[0060] The server control unit 30 is used to implement the procedure 100 for providing traffic information to a vehicle occupant, as described below. The server control unit 30 comprises at least one server processor 36 and a non-transferable, computer-readable server storage device or server medium 38. The description of type and configuration given above for the vehicle control unit 14 also applies to the server control unit 30. In some examples, the server control unit 30 may differ from the vehicle control unit 14 in that the server control unit 30 is capable of a higher processing speed, includes more memory, more inputs / outputs, and / or similar features. In a non-restrictive example, the server processor 36 and the server media 38 of the server control unit 30 are similar in structure and / or function to the processor and media of the vehicle control unit 14, as described above.

[0061] The server database 32 is used to store detailed maps of roads, including, for example, information about lane boundaries, road geometry, speed limits, traffic signs, and / or other relevant features. The server database 32 is also used to store telemetry data received from vehicles, as explained in more detail below. In an exemplary embodiment, the server database 32 comprises one or more mass storage devices, such as hard disk drives, magnetic tape drives, magneto-optical disk drives, optical disks, solid-state drives, and / or additional devices capable of storing data permanently and in a machine-readable format. In some examples, the one or more mass storage devices may be configured to provide redundancy in the event of a hardware failure and / or data corruption, for example, by using a redundant array of independent disks (RAID).In a non-restrictive example, the server control unit can run 30 software applications, such as a database management system (DBMS), which enables the organization of and access to data stored on one or more mass storage devices.

[0062] The server communication system 34 is used for communication with external systems, such as the vehicle control unit 14, via the vehicle communication system. In a non-restrictive example, the server communication system 34 is similar in structure and / or function to the vehicle communication system, as described above. In some examples, the server communication system 34 may differ from the vehicle communication system in that the server communication system 34 is capable of transmitting signals with higher power, receiving signals more sensitively, transmitting with higher bandwidth, using additional transmission / reception protocols, and / or similar features.

[0063] With further reference to Fig. Figure 1 shows the system 10 in an environment comprising a first road 40a, a second road 40b, a traffic signal 42 and one or more long-distance vehicles 44.

[0064] The first road 40a is a road on which the vehicle 12 travels. In an exemplary embodiment, the first road 40a has a first road class. The second road 40b is a road on which the one or more long-distance vehicles 44 travel. In an exemplary embodiment, the second road 40b has a second road class. For the purposes of this disclosure, "road class" means a classification of a particular road based on the function and traffic capacity of the respective road. In a non-limiting example, roads can be classified as local roads, collector roads, main roads, or highways, with local roads being the "lowest" road class (by traffic capacity) and highways being 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-restrictive example, the first road class is a local class and the second road class is a collector road.

[0065] As in Fig. As shown in Figure 1, in an exemplary embodiment, the first road 40a intersects the second road 40b at a junction location 46. Within the scope of this disclosure, the junction location 46 defines a point of intersection between a road on which the vehicle 12 is traveling (e.g., the first road 40a) and a road of a higher class (e.g., the second road 40b). In an exemplary embodiment, the traffic signal 42 is used to control the intersection between the first road 40a and the second road 40b. In a non-limiting example, the traffic signal 42 comprises one or more lamps that can be switched on depending on signal phase and timing data (SPaT). Within the scope of this disclosure, SPaT data includes information about the traffic signal 42, such as the current signal phase, the time remaining until the next phase (i.e., the time remaining until the next phase).the remaining time until the next signal phase change), a future signal phase timing (i.e., timing and duration of upcoming signal phases), pedestrian crossing signal phases and / or similar.

[0066] The SPaT data is managed and transmitted by the traffic control infrastructure. The traffic control infrastructure managing the SPaT data may include, for example, a traffic management center (i.e., a central facility that monitors and manages traffic flow), a roadside unit (i.e., a device installed near intersection location 46 and configured to manage the SPaT data), a traffic signal controller (i.e., a device installed near intersection location 46 primarily configured to control the timing and sequencing of traffic signal 42), and / or the traffic signal 42 itself. In one exemplary embodiment, the SPaT data is periodically transmitted from the traffic control infrastructure to server system 10b, as further explained below.

[0067] The one or more remote vehicles 44 travel on a segment of the second road 40b. For the purposes of this disclosure, the segment of the second road 40b is a segment of the second road 40b within a predetermined distance from the junction 46 (e.g., one mile) and adjacent to the junction 46 (i.e., including or directly adjacent to the junction 46). In an exemplary embodiment, the one or more remote vehicles 44 each comprise a remote vehicle control unit 50 and a plurality of remote vehicle sensors 52 electrically connected to the remote vehicle control unit 50. In an exemplary embodiment, the remote vehicle control unit 50 is similar in structure and function to the vehicle control unit 14 described above.The multiple remote vehicle sensors 52 are similarly constructed and function similarly to the multiple vehicle sensors 16 described above, including at least one 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., location, speed, heading, and / or acceleration) of each of the one or more remote vehicles 44 using the multiple remote vehicle sensors 52 and transmit the telemetry data to the server system 10b using the remote vehicle communication system. It should be understood that, while passenger vehicles are depicted, the one or more remote vehicles 44 can include any type of vehicle traveling on the second road 40b.

[0068] In Fig. Figure 2 shows a flowchart of procedure 100 for providing traffic information to a vehicle occupant. Procedure 100 begins at block 102 and continues to block 104. In block 104, the vehicle control unit 14 determines the location of the vehicle 12 using the multiple vehicle sensors 16 and transmits the location of the vehicle 12 to the server system 10b using the vehicle communication system. After block 104, procedure 100 proceeds to block 106.

[0069] In block 106, the server control unit 30 receives the location of the vehicle 12 transmitted in block 104 via the server communication system 34 and identifies the junction location 46. In an exemplary embodiment, to identify the junction 46, the server control unit 30 searches the detailed map to identify an intersection between the road on which the vehicle 12 is traveling (as determined from the location of the vehicle 12, i.e., the first road 40a) and another road of a higher road class (i.e., the second road 40b). In an exemplary embodiment, the junction location 46 is further determined at least partially based on a navigation destination of the vehicle 12. In a non-restrictive example, it is determined that the junction location 46 is at an intersection along a navigation path of the vehicle 12. After block 106, the procedure 100 proceeds to block 108.

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

[0071] In block 108, the server control unit 30 also receives the SPaT data from the traffic signal 42. In an exemplary embodiment, the server system 10b uses the server communication system 34 to receive the SPaT data. In a non-restrictive example, the SPaT data includes at least data about the operation of the traffic signal 42 over the recently elapsed time period. In an exemplary embodiment, the server control unit 30 also receives SPaT data from other traffic signals within a predetermined radius (e.g., two miles) around the intersection location 46. After block 108, the method 100 continues with blocks 110, 112, and 114.

[0072] In blocks 110, 112, and 114, the server control unit 30 determines traffic data about the one or more long-distance vehicles 44. For the purposes of this disclosure, traffic data is data relating to the movement of the one or more long-distance vehicles 44 and / or congestion of the second road segment 40b. In block 110, the server control unit 30 analyzes the long-distance vehicle telemetry data and / or the SPaT data received in block 108 to determine a percentage of the one or more long-distance vehicles 44 within the second road segment 40b that are traveling below a speed limit for the second road segment 40b (e.g., fifty miles per hour) over the recently elapsed time period. For the purposes of this disclosure, the speed limit is a legally mandated maximum speed for the second road segment 40b.

[0073] In an exemplary embodiment, the speed limit of the road segment is retrieved from the detailed map stored in the server database 32. In a non-restrictive example, the server controller 30 compares an average speed of each of the one or more long-distance vehicles 44 over the recently elapsed time period with the speed limit of the segment of the second road 40b to determine the percentage of the one or more long-distance vehicles 44 within the segment of the second road 40b that are traveling below the speed limit of the segment of the second road 40b (e.g., fifty miles per hour) over the recently elapsed time period. After Block 110, the procedure 100 transitions to Block 116, as further explained below.

[0074] In block 112, the server control unit 30 analyzes the long-distance vehicle telemetry data and / or the SPaT data received in block 108 to determine a percentage of the one or more long-distance vehicles 44 within the segment of the second road 40b that are traveling below a free-flow speed of the segment of the second road 40b (e.g., fifty miles per hour) over the recently elapsed time period. For the purposes of this disclosure, the free-flow speed is an average vehicle speed measured during periods of low traffic under favorable conditions, including good weather and without roadworks or traffic disruptions. In an exemplary embodiment, the free-flow speed of the road segment is determined based on long-term historical telemetry data (e.g., over a period of weeks or months) stored in the server database 32.In a non-restrictive example, the server control unit 30 compares an average speed of each of the one or more long-distance vehicles 44 over the recently elapsed time period with the free-flow speed of the segment of the second road 40b to determine the percentage of the one or more long-distance vehicles 44 within the segment of the second road 40b that are traveling below the free-flow speed of the segment of the second road 40b (e.g., sixty miles per hour) over the recently elapsed time period. Following Block 112, Procedure 100 proceeds to Block 116, as further explained below.

[0075] In Block 114, the server control unit 30 determines a Level of Service (LOS) categorization of the segment of Second Street 40b over the recently elapsed time period. For the purposes of this disclosure, LOS is a quality measure that describes the operating conditions within a traffic flow. LOS defines how well vehicle traffic flows along a road (e.g., the segment of Second Street 40b). In a non-restrictive example, the LOS of the segment of Second Street 40b is categorized as follows: 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 steady vehicle traffic, but other vehicles are perceptible. LOS C corresponds to steady vehicle traffic, but vehicle operation is affected by other vehicles.LOS D corresponds to a dense, free-flowing traffic pattern where the operation of vehicles is hampered by other vehicles. LOS E corresponds to a dense traffic pattern approaching the road's capacity, with extremely poor operating conditions for vehicles. LOS F corresponds to an interrupted traffic pattern (e.g., stop-and-go) exceeding the road's capacity.

[0076] In an exemplary embodiment, the server control unit 30 analyzes the long-distance vehicle telemetry data and / or SPaT data received in block 108 to determine the LOS categorization of the segment of the second road 40b. In a non-restrictive example, the server control unit 30 compares an average speed of each of the one or more long-distance vehicles 44 and a traffic density (e.g., number of vehicles per square meter) over the recently elapsed time period with one or more predetermined thresholds to determine the LOS categorization of the segment of the second road 40b over the recently elapsed time period. After block 114, the procedure 100 proceeds to block 116.

[0077] In block 116, the server control unit 30 determines a road segment traffic profile. Within the scope of this disclosure, the road segment traffic profile describes the total traffic volume on the segment of the second road 40b over the recently elapsed time period. Within the scope of this disclosure, the perceived traffic volume corresponds to a difficulty level when crossing the intersection at the junction location 46. Within the scope of this disclosure, crossing the intersection includes, for example, driving straight through the intersection or making a turn at the intersection. Within the scope of this disclosure, the term "difficulty level" refers to the time spent waiting for an opportunity to pass through the intersection, with a longer waiting time corresponding to a higher difficulty level.In another, non-restrictive example, “difficulty level” refers to the degree of stress or cognitive effort required by the occupant to pass the intersection, with higher stress or cognitive effort corresponding to a higher difficulty level.

[0078] In an exemplary embodiment, the server control unit 30 determines the road segment traffic profile at least partially based on the percentage, determined in block 110, of the one or more long-distance vehicles 44 traveling below the speed limits of the segment of the second road 40b. In a non-restrictive example, a relatively high percentage (e.g., over fifty percent) of the one or more long-distance vehicles 44 traveling below the speed limit is an indication of a high value for the road segment traffic profile at a given time.

[0079] In one exemplary embodiment, the server control unit 30 determines the road segment traffic profile at least partially based on the percentage of one or more long-distance vehicles 44 traveling below the free-flow speed of the second road segment 40b, as determined in block 112. In a non-restrictive example, a relatively high percentage (e.g., over fifty percent) of one or more long-distance vehicles 44 traveling below the free-flow speed indicates a high value for the road segment traffic profile at a given time.

[0080] In an exemplary embodiment, the server control unit 30 determines the road segment traffic profile at least partially based on the LOS categorization of the segment of the second road 40b determined in block 114. In a non-restrictive example, a poor LOS (e.g., LOS D or LOS E) indicates a high value for the road segment traffic profile at a given time.

[0081] In an exemplary embodiment, the server control unit 30 determines the road segment traffic profile at least partially based on the SPaT data received in block 108. In a non-restrictive example, SPaT data indicating long red light phases (i.e., stop phases) in the direction of travel of one or more long-distance vehicles 44 are an indication of a high value of the road segment traffic profile at a given time.

[0082] In an exemplary embodiment, the server control unit 30 determines the road segment profile at least partially based on the remote vehicle telemetry data received in block 108. In a non-restrictive example, remote vehicle telemetry data indicating a relatively high traffic density (e.g., the number of vehicles per square meter) suggests a high value for the road segment traffic profile at a given time. As mentioned earlier, the road segment traffic profile describes the total traffic volume over time. Therefore, the road segment traffic profile describes how the total traffic volume changes over time. After block 116, the procedure 100 transitions to block 118.

[0083] In block 118, the server control unit 30 fits the road segment traffic profile determined in block 116 to a periodic curve. In an exemplary embodiment, the road segment traffic profile is fitted using a regression or curve-fitting algorithm (e.g., a linear, polynomial, exponential, or logarithmic curve-fitting algorithm) that generates one or more parameters that at least partially characterize the periodic curve based on the road segment traffic profile. In a non-restrictive example, the curve-fitting algorithm uses an iterative process to determine optimal values ​​for each of the one or more parameters.In a non-restrictive example, the one or more parameters include a maximum traffic value, a minimum traffic value, a frequency, a period, one or more coefficients for a mathematical equation describing a curve fit, and / or the like. In a non-restrictive example, the periodic curve is a sine wave, a cosine wave, a square wave, a triangular wave, a sawtooth wave, any periodic wave, and / or the like. 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, Procedure 100 proceeds to Block 120.

[0084] In block 120, the server controller 30 identifies recurring time periods when the road segment traffic profile reaches a minimum value. In an exemplary embodiment, the server controller 30 identifies recurring time periods when the road segment traffic profile reaches the minimum value, which is based at least partially on the periodic curve and the one or more parameters identified in block 118. In a non-restrictive example, the recurring time periods are defined by the period of the periodic curve relative to the minimum traffic value. After block 120, method 100 transitions to blocks 122 and 124 via the off-page connection to Fig. 3.

[0085] In Fig. 3 is a continuation of the flowchart from Fig.Section 2 of method 100 for providing traffic information to a vehicle occupant is described. In block 122, the server control unit 30 determines an estimated delay time. Within the scope of this disclosure, the estimated delay time is the period until the total traffic volume on the segment of the second road 40b (i.e., the road segment traffic profile) is estimated to reach the minimum traffic value. In an exemplary embodiment, the estimated delay time is determined at least partially based on one or more parameters, the periodic curve, and a current perceived traffic volume of the segment of the second road 40b. In a non-limiting example, the server control unit 30 determines the current expected traffic volume of the segment of the second road 40b at least partially based on the remote vehicle telemetry data.In a non-restrictive example, the server control unit 30 determines the estimated delay time by comparing the current perceived traffic volume with a point on the periodic curve determined in block 118 and then measuring the time between the comparison point on the periodic curve and the minimum traffic value. After block 122, procedure 100 proceeds to block 126, as explained in more detail below.

[0086] In block 124, the server control unit 30 determines an estimated travel time for the vehicle 12 to reach the junction location 46. In an exemplary embodiment, the estimated travel time is determined at least partially based on the location of the vehicle 12 and the junction location 46. In a non-restrictive example, the server control unit 30 determines a distance between the vehicle 12 and the junction location 46. Subsequently, the server control unit 30 determines the estimated travel time at least partially based on a free-flow velocity of the first road 40a. In another exemplary embodiment, the server control unit 30 uses the detailed map stored in the server database 32 to determine a navigation path between the location of the vehicle 12 and the junction location 46. Subsequently, the server control unit 30 determines the estimated travel time at least partially based on the navigation path.After block 124, procedure 100 moves on to block 126.

[0087] In block 126, the server control unit 30 determines an estimated waiting time. Within the scope of the present disclosure, the estimated waiting time is an estimated period of time that the vehicle 12 must wait before it can pass through the intersection at junction location 46. In an exemplary embodiment, the estimated waiting time is a difference between the estimated waiting time determined in block 122 and the estimated travel time determined in block 124. tw=td−tt where t w the estimated waiting time, t d the estimated delay time and t t The estimated travel time is [date / time missing]. After block 126, procedure 100 continues to block 128.

[0088] In block 128, the server control unit 30 determines an optimal departure delay. Within the scope of this disclosure, the optimal departure delay is a time interval by which the occupant and / or the automated driving system 20 should delay departure from the vehicle 12's location such that the estimated waiting time is zero when the vehicle 12 reaches the intersection location 46. In an exemplary embodiment, the optimal departure delay is equal to the estimated waiting time determined in block 126.

[0089] In another exemplary embodiment, the optimal departure delay is greater than or equal to the estimated waiting time because the optimal departure delay additionally takes into account the signal phase timing of traffic signal 42 (i.e., determined from the SPaT data received in block 108), so that vehicle 12 can avoid stopping upon reaching traffic signal 42. In a non-restrictive example, the optimal departure delay is further adjusted based on the signal phase timing of several traffic signals (i.e., determined from the SPaT data received in block 108) within the predetermined radius (e.g., two miles) of junction 46 and / or along a planned route of vehicle 12, so that vehicle 12 experiences a "green wave".Within the context of this disclosure, the term "green wave" refers to a phenomenon in which the vehicle experiences several consecutive green traffic signals because the vehicle's movement and / or route (e.g., speed and / or location) is coordinated with the SPaT data from multiple traffic signals. Following Block 128, the procedure transitions to Block 130.

[0090] In block 130, the server control unit 30 compares the optimal departure delay determined in block 128 with zero. If the optimal departure delay is equal to or nearly equal to zero, the present time is an optimal departure time, so the estimated waiting time is zero when vehicle 12 reaches node 46. If the optimal departure delay is within a predetermined range of zero (e.g., plus or minus two seconds from zero), procedure 100 proceeds to blocks 132 and 134, as further explained below. If the optimal departure delay is not within the predetermined range of zero, procedure 100 proceeds only to block 132.

[0091] In block 132, the server control unit 30 uses the server communication system 34 to transmit the optimal departure delay and the estimated waiting time to the vehicle control unit 14. The vehicle control unit 14 receives the optimal departure time and the estimated waiting time via the vehicle communication system. Subsequently, the vehicle control unit 14 issues a message to the occupant of the vehicle 12 via the vehicle display 18, which is based at least partially on the optimal departure time and / or the estimated waiting time. In one non-restrictive example, the notification includes a text and / or graphic message instructing the occupant to delay departure by the optimal departure time. In another non-restrictive example, the message includes a text and / or graphic message informing the occupant of the estimated waiting time.In another non-restrictive example, the notification contains a text and / or graphic message informing the occupants of the optimal vehicle speed to reach junction 46, resulting in an estimated waiting time of zero. Following block 132, procedure 100 enters a standby state in block 136.

[0092] In block 134, the server control unit 30 transmits the optimal departure delay and the estimated waiting time to the vehicle control unit 14 via the server communication system 34. The vehicle control unit 14 receives the optimal departure time and the estimated waiting time via the vehicle communication system. Subsequently, the vehicle control unit 14 initiates an automated route with the aid of the automated driving system 20 if it determines that the optimal waiting time is within the specified range of zero. In the context of this disclosure, initiating the automated route means that the vehicle control unit 14 commands the automated driving system 20 to drive a predetermined and / or pre-planned automated route to a predetermined and / or pre-planned destination. The predetermined and / or pre-planned automated route includes the intersection location 46.In a non-restrictive example, the vehicle control unit 14 instructs the automated driving system 20 to drive at an optimal vehicle speed to reach junction 46, such that the estimated waiting time is zero. After block 134, the procedure 100 enters standby mode in block 136.

[0093] In one exemplary embodiment, the vehicle control unit 14 repeatedly exits the standby state 136 and restarts the procedure 100 in block 102. In a non-restrictive example, the vehicle controller 14 exits the standby state 136 and restarts the procedure 100 after a timer, for example, every three hundred milliseconds.

[0094] System 10 and Method 100 of the present disclosure offer several advantages. With System 10 and Method 100, the vehicle 12 can more easily cross the intersection at junction 46 despite the difference in road classification between the first road 40a and the second road 40b. The use of System 10 and Method 100 leads to an increase in the comfort and convenience of the occupants.

[0095] Furthermore, when using System 10 and Procedure 100, SPaT data from nearby traffic signals is taken into account, facilitating a "green wave" when passing through multiple traffic signals and increasing comfort and convenience for the occupants. Additionally, System 10, using Procedure 100, can initiate automated driving at an optimal time, thereby reducing occupant waiting times and alleviating traffic congestion on the first road 40a and the second road 40b. Moreover, the traffic density profile of the road segment can be used to determine estimated waiting times for additional road users (e.g., pedestrians, cyclists, and / or similar) and to display these estimated waiting times to these additional road users via physical displays near junction 46 and / or via personal devices (e.g., smartphones).

[0096] The description of the present revelation is merely exemplary, and variations that do not deviate from the core of the present revelation are to fall within its scope. Such variations are not to be considered a departure from the spirit and scope of the present revelation.

Claims

[1] A method for providing traffic information to an occupant of a vehicle (12), the method comprising: Identifying a junction location (46) in an environment surrounding the vehicle (12), wherein the junction location (46) is a location of an intersection between a first road (40a) on which the vehicle (12) travels and a second road (40b), wherein the first road (40a) has a first road class and the second road (40b) has a second road class, and wherein the first road class is lower than the second road class; Determining traffic data about one or more long-distance vehicles (44) traveling on a segment of the second road (40b), wherein the segment of the second road (40b) is adjacent to the junction location (46); Determining an estimated waiting time for the vehicle (12) based at least partially on traffic data and a distance between the vehicle (12) and the intersection location (46); and Performing an initial action based at least partially on the estimated waiting time; which still includes determining the traffic data: Determining a percentage of the one or more long-distance vehicles (44) that travel below a speed limit of the second road segment (40b) over a recently elapsed period of time; Determining a percentage of the one or more long-distance vehicles (44) that travel below a free-flow velocity of the second road segment (40b) over the recently elapsed time period; Determining a service level categorization of the second street segment (40b) over the recent past period; and Determining a road segment traffic profile based at least partially on at least one of the following: the percentage of one or more long-distance vehicles (44) traveling below a speed limit of the second road segment (40b), the percentage of one or more long-distance vehicles (44) traveling below a free-flow speed of the second road segment (40b), and the service level categorization of the second road segment (40b), wherein the road segment traffic profile describes a perceived traffic volume on the second road segment (40b) over the recently elapsed time period. [2] The method according to claim 1, wherein determining the traffic data further comprises: Receiving long-range vehicle telemetry data from the one or more long-range vehicles (44), wherein the long-range vehicle telemetry data includes at least one location of each of the one or more long-range vehicles (44); and Determining traffic data based at least partially on long-distance vehicle telemetry data. [3] The method according to claim 1, wherein determining the traffic data further comprises: Receiving signal phase and timing (SPaT) data from a traffic signal at the intersection location (46) over the recently elapsed time interval; and Determining the road segment traffic profile based at least partially on at least one of: the percentage of one or more long-distance vehicles (44) traveling below a speed limit of the second road segment (40b), the percentage of one or more long-distance vehicles (44) traveling below a free-flow speed of the second road segment (40b), the service level categorization of the second road segment, and the SPaT data, wherein the road segment traffic profile describes a perceived traffic level on the second road segment (40b) over the most recent historical period. [4] The method according to claim 1, wherein determining the estimated waiting time further comprises: Identifying recurring time periods when the road segment traffic profile reaches a minimum value; and Determining the estimated waiting time based at least partially on recurring time periods as the road segment traffic profile approaches its minimum value. [5] The method of claim 4, wherein identifying the recurring time periods when the road segment traffic profile reaches a minimum value further comprises: Adapting the road segment traffic profile to a periodic curve; Determine one or more parameters that characterize the periodic curve, wherein the one or more parameters include at least a minimum traffic value and a period; and Identifying recurring time periods based at least partially on the minimum market value and the period. [6] The method according to claim 5, wherein determining the estimated waiting time based at least partially on the recurring time intervals further comprises: Determining an estimated waiting time until the estimated minimum traffic volume is reached on the segment of the second road (40b) based at least partially on one or more parameters characterizing the periodic curve and a current estimated traffic volume on the segment of the second road (40b); Determining an estimated travel time for the vehicle (12) to reach the junction location (46), based at least partially on the distance between the vehicle (12) and the junction location (46) and a free-flow velocity of the first road (40a); and Determining the estimated waiting time based at least partially on the estimated delay time and the estimated travel time, where the estimated waiting time is a difference between the estimated delay time and the estimated travel time. [7] The method according to claim 1, wherein performing the first action further comprises: Providing a notification to the vehicle occupant (12) based at least partially on the estimated waiting time using a vehicle display. [8] The method according to claim 7, wherein providing the notification further comprises: Determining an optimal departure delay based at least partially on the estimated waiting time, wherein the optimal departure delay is a time interval by which the occupant should delay departure such that the estimated waiting time upon reaching the node location (46) is zero; and Providing the notification to the occupants of the vehicle (12) which is based at least partially on the optimal departure time. [9] The method according to claim 1, wherein performing the first action further comprises: Determining an optimal departure delay based at least partially on the estimated waiting time, wherein the optimal departure delay is a period of time by which the vehicle (12) should delay departure such that the estimated waiting time upon reaching the junction location (46) is zero; Comparing the optimal departure deceleration with zero; and Initiating an automated driving route using an automated driving system of the vehicle (12) in response to determining that the optimal departure deceleration is within a predetermined range of zero.

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