Situation awareness guided energy harvesting system

Abstract

A Situation Awareness Guided Energy Harvesting (SAGEH) system located in a vehicle includes a cloud computing server and sensors that capture information about primary and remote vehicles and their environment. The carrier and the server are each provided with a controller, and the controllers execute SAGEH application programs. Comprising the following control logic: triggering collection of main vehicle and remote vehicle information and environmental information, continuously observing traffic signals along a road segment and traffic situation along a main vehicle path, generating an estimated amount of time for the traffic signal to change state, and generating an estimated amount of time for the main vehicle to stop at the traffic signal, and generating a control output command in response to one or more of the estimated amounts of time. The control output command causes the vehicle to activate one or more of a stop / start system and a regenerative braking system to dynamically, automatically, adaptively harvest energy.

Description

Technical Field

[0001] The present disclosure relates to energy saving systems in motor vehicles and, more particularly, to energy harvesting in vehicles utilizing regenerative braking and / or internal combustion engine (ICE) auto stop - start systems. Background Art

[0002] Environmental and fuel or energy efficiency increasingly drive vehicle operation, including ICE auto stop / start systems and energy harvesting, such as regenerative braking in an electric vehicle powertrain. To effectively manage energy use and recovery, static rule sets are typically used to manage the performance of ICE auto stop / start systems and regenerative braking systems. Thus, in situations where a vehicle operator may require immediate acceleration, the ICE auto stop / start system typically results in the ICE being turned off. Similarly, the regenerative braking system has limited adjustability. When the ICE auto stop / start and / or regenerative braking systems rely on static rule sets, the energy harvesting process may transmit sudden accelerations and / or decelerations, introducing unnecessary vehicle vibration forces and discomfort to vehicle occupants while reducing overall system efficiency.

[0003] Accordingly, while current systems and methods for energy saving systems in motor vehicles achieve their intended purposes, there is a need for a new and improved system and method for situation - aware energy harvesting in vehicles using existing hardware, thereby maintaining or reducing system component complexity and computational complexity while improving vehicle operator comfort and satisfaction and reducing the energy consumption of vehicles equipped with the system. Summary of the Invention

[0004] According to several aspects of the present disclosure, a system for situation awareness guided energy harvesting in a vehicle includes a host vehicle and one or more remote vehicles. The system further includes one or more sensors that capture information of the host vehicle and the remote vehicles, and capture environmental information about the environment of the host vehicle and the one or more remote vehicles. The system further includes a cloud computing server that communicates with the host vehicle and the one or more remote vehicles. Each of the host vehicle, the one or more remote vehicles, and the cloud computing server has a controller. The controller includes a processor, a memory, and one or more input / output (I / O) ports. The I / O ports communicate with the one or more sensors. The memory stores program control logic. The processor executes the program control logic. The program control logic includes a situation awareness guided energy harvesting (SAGEH) application. The SAGEH application includes at least first, second, third, fourth, and fifth control logics. The first control logic triggers the collection of host vehicle information, remote vehicle information, and environmental information from the one or more sensors. The second control logic continuously observes traffic signals along a road segment and traffic situation along the navigation path of the host vehicle. The third control logic generates a first estimated time amount for a traffic signal to change state and a second estimated time amount for the host vehicle to stop at the traffic signal. The fourth control logic generates a control output command in response to one or more of the first and second estimated time amounts. The fifth control logic causes a vehicle operator to generate feedback regarding the control output command. The control output command causes the vehicle to dynamically, automatically, and adaptively enable one or more of an internal combustion engine (ICE) stop / start system and a regenerative braking system to dynamically, automatically, and adaptively harvest energy.

[0005] In another aspect of the present disclosure, the first control logic further includes detecting host vehicle and remote vehicle position information, host vehicle navigation information, and environmental information within data from the one or more sensors. The one or more sensors further include one or more of the following: a camera, a light detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor, a sound navigation and ranging (SONAR) sensor, an ultrasonic sensor, a motion sensor, an inertial measurement unit (IMU), a global positioning system (GPS) sensor, a communication tower sensor, and a traffic signal sensor. The first control logic further determines that a situation of the host vehicle changes based on the host vehicle and remote vehicle position information, the host vehicle navigation information, and the environmental information. The first control logic further implements road segment specific data collection, including collecting from the one or more sensors: camera data; time of day (ToD) information, season information, traffic information, and global positioning system (GPS) position and navigation information. The first control logic further includes control logic for deriving parameter information within the road segment specific data, updating the road segment specific data with the parameter information, and sending the updated road segment related information to the cloud computing server.

[0006] In yet another aspect of the present disclosure, the first control logic further includes control logic for accessing crowdsourced data stored in the memory of a cloud computing server and obtained from sensors of one or more remote vehicles and sensors disposed on infrastructure, where the infrastructure includes traffic signals, GPS satellites, and communication towers. The first control logic further includes control logic for normalizing data specific to a road segment to determine at least time-based traffic waiting times and traffic signal duration information along the road segment.

[0007] In yet another aspect of the present disclosure, the second control logic further includes control logic for enabling an energy harvesting feature of the host vehicle; and executing stop-and-go estimation control logic. The stop-and-go estimation control logic operates while the energy harvesting feature is enabled. The stop-and-go estimation control logic performs continuous situational information collection, including: monitoring the navigation route of the host vehicle, monitoring GPS data, monitoring camera data, monitoring traffic information, and monitoring traffic signal cycles.

[0008] In yet another aspect of the present disclosure, the second control logic further includes control logic for processing data collected by the stop-and-go estimation control logic to calculate and derive parameter information about a road segment, where the parameter information includes: highway information, urban road information, single-lane or multi-lane information, straight road information, left-turn information, right-turn information, roundabout information, lane information, yield sign information, stop sign information, specific lane traffic signal and status information, other traffic signal status information, crosswalk information, and determining the number of pedestrians. The second control logic further includes control logic for executing focus area current situation analysis control logic to define the current situation of the host vehicle along the road segment.

[0009] In yet another aspect of the present disclosure, the third control logic further includes control logic for calculating first and second estimated time amounts based on the current traffic signal state, the number of remote vehicles in front of the host vehicle, the time of day, a static rule set, and crowdsourced data, where the crowdsourced data includes: the GPS position of the host vehicle, traffic signal identifiers, traffic waiting times based on current and historical times, and current and historical traffic signal durations.

[0010] In yet another aspect of the present disclosure, the fourth control logic further includes control logic for dynamically, automatically, and adaptively generating control output commands to one or more of the regenerative braking system and the automatic stop / start system of the host vehicle using a static rule set, vehicle parameters including the current speed and current charging state of the host vehicle, and estimated traffic signal behavior.

[0011] In yet another aspect of the present disclosure, generating a control output command for the regenerative braking system of the host vehicle further includes dynamically, automatically, and adaptively adjusting the regenerative braking intensity based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic state on the road segment, and the estimated traffic signal behavior.

[0012] In yet another aspect of the present disclosure, generating a control output command for the automatic stop / start system of the host vehicle further includes: dynamically, automatically, and adaptively adjusting the ICE stop / start system based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic state on the road segment, and the estimated traffic signal behavior to selectively stop the ICE of the host vehicle.

[0013] In yet another aspect of the present disclosure, the fifth control logic further includes: control logic for collecting the actions of the vehicle operator in chronological order during a time window, and control logic for comparing the expected behavior and the actual behavior with a threshold. When it is determined that the actual behavior is greater than the threshold, the fifth control logic marks the current host vehicle position by the difference between the actual behavior and the expected behavior. The fifth control logic also triggers a change request for the crowdsourced data hosted in the cloud computing server memory. When the number of change requests reaches or exceeds the change request threshold, the control logic triggers a change in the expected behavior, and when the number of change requests is below the change request threshold, the control logic triggers additional data collection.

[0014] In other aspects of the present disclosure, a method for situation awareness-guided energy harvesting in a vehicle includes capturing information about a host vehicle and one or more remote vehicles via one or more sensors, and capturing environmental information about the environment of the host vehicle and one or more remote vehicles. The method further includes utilizing a cloud computing server in communication with the host vehicle and one or more remote vehicles, and utilizing one or more controllers disposed in each of the host vehicle, one or more remote vehicles, and the cloud computing server. Each of the controllers includes a processor, a memory, and one or more input / output (I / O) ports. The I / O ports communicate with one or more sensors. The memory stores program control logic. The processor executes the program control logic. The program control logic includes a situation awareness-guided energy harvesting (SAGEH) application. The SAGEH application includes: triggering the collection of host vehicle information, remote vehicle information, and environmental information from one or more sensors; continuously observing traffic signals along a road segment and the traffic situation along the navigation path of the host vehicle; generating a first estimated amount of time for a traffic signal to change state and a second estimated amount of time for the host vehicle to stop at the traffic signal; generating a control output command in response to one or more of the first and second estimated amounts of time; and causing a vehicle operator to generate feedback regarding the control output command. The control output command causes the vehicle to dynamically, automatically, and adaptively enable one or more of an internal combustion engine (ICE) stop / start system and a regenerative braking system to dynamically, automatically, and adaptively harvest energy.

[0015] In yet another aspect of the present disclosure, the method further includes detecting host vehicle and remote vehicle location information, host vehicle navigation information, and environmental information within data from one or more sensors, where the one or more sensors further include one or more of the following: a camera, a light detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor, a sound navigation and ranging (SONAR) sensor, an ultrasonic sensor, a motion sensor, an inertial measurement unit (IMU), a global positioning system (GPS) sensor, a communication tower sensor, and a traffic signal sensor. The method further includes determining that a situation of the host vehicle has changed based on the host vehicle and remote vehicle location information, the host vehicle navigation information, and the environmental information. The method further includes implementing segment-specific data collection, collecting from one or more sensors: camera data; time-of-day (ToD) information, season information, traffic information, and global positioning system (GPS) location and navigation information. The method further includes deriving parameter information in the segment-specific data, and updating the segment-specific data with the parameter information and sending the updated segment-related information to the cloud computing server.

[0016] In yet another aspect of the present disclosure, the method further includes accessing crowdsourced data stored in a memory of a cloud computing server and obtained from sensors of one or more remote vehicles and sensors disposed on infrastructure, the infrastructure including: traffic signals, GPS satellites, and communication towers; and normalizing data specific to a road segment to determine at least time-based traffic waiting times and traffic signal duration information along the road segment.

[0017] In yet another aspect of the present disclosure, the method further includes enabling an energy harvesting feature of the host vehicle and executing traffic jam estimation control logic. The traffic jam estimation control logic operates while the energy harvesting feature is enabled. The traffic jam estimation control logic performs continuous situational information collection, including: monitoring the navigation route of the host vehicle, monitoring GPS data, monitoring camera data, monitoring traffic information, and monitoring traffic signal cycles.

[0018] In yet another aspect of the present disclosure, the method further includes processing data collected by the traffic jam estimation control logic to calculate and derive parameter information about a road segment, the parameter information including: highway information, urban road information, single-lane information or multi-lane information, straight road information, left-turn information, right-turn information, roundabout information, lane information, yield sign information, stop sign information, specific lane traffic signal and status information, other traffic signal and status information, crosswalk information, and determining the number of pedestrians. The method further includes executing focus area current situation analysis control logic to define the current situation of the host vehicle along the road segment.

[0019] In yet another aspect of the present disclosure, the method further includes calculating first and second estimated time amounts based on the current traffic signal state, the number of remote vehicles in front of the host vehicle, the time of day, a static rule set, and crowdsourced data, the crowdsourced data including: the GPS location of the host vehicle, traffic signal identifiers, traffic waiting times based on current and historical times, and current and historical traffic signal durations.

[0020] In yet another aspect of the present disclosure, the method further includes dynamically, automatically, and adaptively generating control output commands to one or more of a regenerative braking system and an auto stop / start system of the host vehicle using a static rule set, vehicle parameters including the current speed and current charging state of the host vehicle, and estimated traffic signal behavior.

[0021] In yet another aspect of the present disclosure, generating a control output command for the regenerative braking system of the host vehicle further includes dynamically, automatically, and adaptively adjusting the regenerative braking intensity based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic state on the road segment, and the estimated traffic signal behavior, and dynamically, automatically, and adaptively adjusting the ICE stop / start system based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic state on the road segment, and the estimated traffic signal behavior to selectively stop the ICE of the host vehicle.

[0022] In yet another aspect of the present disclosure, the method further includes collecting the chronological actions of the vehicle operator during a time window and comparing the expected behavior and the actual behavior with a threshold. When it is determined that the actual behavior is greater than the threshold, the current host vehicle position is marked by the difference between the actual behavior and the expected behavior; and a change request for the crowdsourced data hosted in the cloud computing server memory is triggered. When the number of change requests reaches or exceeds the change request threshold, the method triggers a change in the expected behavior, and when the number of change requests is below the change request threshold, the method triggers additional data collection.

[0023] In several additional aspects of the present disclosure, a method for situation awareness-guided energy harvesting in a vehicle includes: capturing information about a host vehicle and one or more remote vehicles, and environmental information about the environment of the host vehicle and one or more remote vehicles via one or more sensors; and utilizing a cloud computing server in communication with the host vehicle and one or more remote vehicles. The method further includes utilizing one or more controllers disposed in each of the host vehicle, one or more remote vehicles, and the cloud computing server. Each of the controllers includes a processor, a memory, and one or more input / output (I / O) ports. The I / O ports communicate with one or more sensors. The memory stores program control logic. The processor executes the program control logic. The program control logic includes a Situation Awareness-Guided Energy Harvesting (SAGEH) application. The SAGEH application includes the following control logic: for triggering the collection of host vehicle information, remote vehicle information, and environmental information from one or more sensors, including: detecting host vehicle and remote vehicle location information, host vehicle navigation information, and environmental information within data from one or more sensors. The SAGEH application further includes the following control logic for determining a change in the situation of the host vehicle based on the host vehicle and remote vehicle location information, host vehicle navigation information, and environmental information, thereby enabling data collection specific to a road segment, including collecting from one or more sensors: camera data; time-of-day (ToD) information, season information, traffic information, and global positioning system (GPS) location and navigation information, and deriving parameter information in the data specific to the road segment. The SAGEH application further includes updating the data specific to the road segment using the parameter information, sending the updated road segment-related information to the cloud computing server, and accessing crowdsourced data stored in the memory of the cloud computing server and obtained from sensors of one or more remote vehicles and sensors installed on infrastructure, the infrastructure including: traffic signals, GPS satellites, and communication towers. The sensors further include one or more of the following: cameras, light detection and ranging (LiDAR) sensors, radio detection and ranging (RADAR) sensors, sound navigation and ranging (SONAR) sensors, ultrasonic sensors, motion sensors, inertial measurement units (IMU), global positioning system (GPS) sensors, communication tower sensors, and traffic signal sensors. The SAGEH application further includes control logic for normalizing the data specific to the road segment to determine at least time-based traffic waiting times and traffic signal duration information along the road segment. The SAGEH application further includes continuously observing traffic signals along the road segment and the traffic situation along the host vehicle navigation path, including: enabling the energy harvesting feature of the host vehicle, and executing traffic jam estimation control logic. The traffic jam estimation control logic runs while the energy harvesting feature is enabled.The traffic jam estimation control logic performs continuous situation information collection, including: monitoring the navigation route of the host vehicle, monitoring GPS data, monitoring camera data, monitoring traffic information, and monitoring traffic signal cycles. The SAGEH application further includes control logic for processing the data collected by the traffic jam estimation control logic to calculate and derive parameter information about road segments. The parameter information includes: highway information, urban road information, single-lane information or multi-lane information, straight road information, left-turn information, right-turn information, roundabout information, lane information, yield sign information, stop sign information, specific lane traffic signal and status information, other traffic signal and status information, crosswalk information, and determining the number of pedestrians. The SAGEH application also includes control logic for performing a current situation analysis control logic of the focus area to define the current situation of the host vehicle along the road segment, and for generating a first estimated time amount for the traffic signal to change state and a second estimated time amount for the host vehicle to stop at the traffic signal, including: calculating the first and second estimated time amounts based on the current traffic signal state, the number of remote vehicles in front of the host vehicle, the time of day, a static rule set, and crowdsourced data, where the crowdsourced data includes: the GPS position of the host vehicle, traffic signal identifiers, traffic waiting times based on current and historical times, and current and historical traffic signal durations. In response to one or more of the first estimated time amount and the second estimated time amount, the SAGEH application generates control output commands, including: dynamically, automatically, and adaptively generating control output commands to one or more of the regenerative braking system and the automatic stop / start system of the host vehicle using the static rule set, vehicle parameters including the current host vehicle speed and the current host vehicle charge state, and the estimated traffic signal behavior. Generating a control output command to the regenerative braking system of the host vehicle further includes dynamically, automatically, and adaptively adjusting the regenerative braking intensity based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic state on the road segment, and the estimated traffic signal behavior, and dynamically, automatically, and adaptively adjusting the ICE stop / start system based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic state on the road segment, and the estimated traffic signal behavior to selectively stop the ICE of the host vehicle. The method further includes causing the vehicle operator to generate feedback about the control output commands, including: performing the following control logic of the SAGEH application, collecting the actions of the vehicle operator in chronological order during a time window; control logic for comparing the expected behavior and the actual behavior with a threshold, where, when it is determined that the actual behavior is greater than the threshold, marking the current host vehicle position by the difference between the actual behavior and the expected behavior; control logic for triggering a change request for the crowdsourced data hosted in the cloud computing server memory. When the number of change requests reaches or exceeds the change request threshold, control logic for triggering a change in the expected behavior is triggered, and when the number of change requests is below the change request threshold, control logic for triggering additional data collection is triggered.The control output command causes the vehicle to dynamically, automatically, and adaptively enable one or more of an internal combustion engine (ICE) stop / start system and a regenerative braking system to dynamically, automatically, and adaptively collect energy.

[0024] Further application areas 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 the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0026] Figure 1 is a schematic diagram depicting a situation awareness energy harvesting system according to an exemplary embodiment;

[0027] Figure 2 is depicting according to an exemplary embodiment Figure 1 of the control logic of the situation awareness energy harvesting system in a flowchart of the logical flow;

[0028] Figure 3 is depicting according to an exemplary embodiment for Figure 1 of the situation awareness energy harvesting system in a flowchart of the control logic of the trigger data collection and templated data transmission section;

[0029] Figure 4 is depicting according to an exemplary embodiment for Figure 1 of the situation awareness energy harvesting system in a flowchart of the control logic of the continuous traffic observation section;

[0030] Figure 5 is depicting for calculating and estimating according to an exemplary embodiment Figure 1 of the situation awareness energy harvesting system in a flowchart of the control logic of traffic signal changes and traffic signal stop times;

[0031] Figure 6 is depicting according to an exemplary embodiment for Figure 1 of the situation awareness energy harvesting system in a flowchart of the control logic of control output calculation; and

[0032] Figure 7 is depicting according to an exemplary embodiment for Figure 1 of the situation awareness energy harvesting system in a flowchart of the control logic of the vehicle operator feedback section. DETAILED DESCRIPTION

[0033] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.

[0034] Reference Figure 1 and Figure 2 shows a situation awareness-guided energy harvesting system 10. The system 10 generally includes a main vehicle 12 and one or more remote vehicles 12', a remote cloud computing server 13, and may also include infrastructure such as one or more communication towers 14, Global Positioning System (GPS) satellites 16, traffic signal devices 18, etc. Although the illustrated vehicle 12' includes passenger vehicles and buses, it should be understood that without departing from the scope or intent of the present disclosure, the vehicle 12' can be any one of a variety of vehicles 12' including autonomous and manually driven vehicles, but not limited to: cars, trucks, sport utility vehicles (SUVs), buses, semi-tractors, tractors for agriculture or construction, ships, aircraft such as airplanes or helicopters.

[0035] Each of the vehicle 12' and the remote cloud computing server 13 includes one or more controllers 20. The controller 20 is a non-generic electronic control device having a pre-programmed digital computer or processor 22, a non-transitory computer-readable medium or memory 24 for storing data such as control logic, software applications, instructions, computer code, data, look-up tables, etc., and a transceiver or input / output (I / O) port 26. The computer-readable medium includes any type of medium that can be accessed by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, compact disc (CD), digital video disc (DVD), or any other type of memory. The "non-transitory" computer-readable memory 24 does not include wired, wireless, optical, or other communication links that transmit transient electrical signals or other signals. The non-transitory computer-readable memory 24 includes media in which data can be permanently stored and media in which data can be stored and subsequently rewritten, such as rewritable optical discs or erasable storage devices. The computer code includes any type of program code, including source code, object code, and executable code. The processor 22 is configured to execute the code or instructions. In the vehicle 12', the controller 20 can be a dedicated Wi-Fi controller or an engine control module, a transmission control module, a body control module, an infotainment control module, etc. The I / O port 26 is configured for wireless communication using Wi-Fi protocols under IEEE 802.11x, cellular protocols such as Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Wireless Local Loop (WLL), vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) systems, General Packet Radio Service (GPRS), 1G, 2G, 3G, 4G Long Term Evolution (LTE), 5G, etc.

[0036] The memory 24 may store one or more application programs 28. The application program 28 is a software program configured to execute a specific function or set of functions. The application program 28 may include one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, associated data, or portions thereof adapted to be implemented in suitable computer-readable program code. The application program 28 may be stored in the memory 24 of the vehicle controller 20 in the vehicle 12', or in an additional or separate memory, such as in the memory 24 of a cloud computing device (e.g., the cloud computing server 13). Examples of the application program 28 include audio or video streaming services, games, browsers, social media, and applications for situation-awareness guided energy harvesting (SAGEH) 30.

[0037] The system 10 utilizing the SAGEH 30 application obtains and / or generates operation information from various sources, including V2V and V2X, but not limited to: one or more sensors 32, which are disposed on the vehicles 12, 12' and capture vehicle 12, 12' information, including vehicle 12, 12' telematics and communication information, such as vehicle 12, 12' speed, vehicle 12, 12' position information, vehicle 12, 12' latitude, etc. In several aspects, the sensors 32 disposed on the host vehicle 12 may include any one of a variety of sensor types, including but not limited to sensors 32 for detecting optical or electromagnetic information regarding the vehicles 12, 12', the surrounding environment of the vehicles 12, 12', and so on. The sensors 32 may include but are not limited to: cameras 34, light detection and ranging (LiDAR) sensors, radio detection and ranging (RADAR) sensors, sound navigation and ranging (SONAR) sensors, ultrasonic sensors, or combinations thereof. The sensors 32 may also include motion sensors, such as an inertial measurement unit (IMU). The IMU uses some or all of the following combinations to measure and report the attitude or position, linear velocity, acceleration, and angular rate relative to a global reference frame: accelerometers, gyroscopes, and magnetometers. In some examples, the IMU may also utilize global positioning system (GPS) data to indirectly measure the attitude or position, speed, acceleration, and angular rate of one or more vehicles 12'.

[0038] In additional examples, the host vehicle 12, the remote vehicle 12', and / or infrastructure-based sensors 32 may obtain environmental data regarding the area surrounding the vehicle 12', such as traffic condition information, road condition and pavement information, weather information, information from remote sensor 32 sources, such as infrastructure sensors 32, including GPS satellites 16, communication towers 14, traffic signal devices 18, or roadside sensing devices (e.g., speed or traffic sensing cameras, etc.).

[0039] In several aspects, system 10 collects data from a variety of different sensor 32 sources, including cameras 34, traffic signal devices 18, GPS satellites 16, and the like. Sensor 32 data obtained from the various sensors can include optical data, time-of-day (ToD) information, traffic density, traffic volume, traffic speed, traffic signal 18 cycles, physical location information of vehicles 12, 12', one or more intersections 36 on a road segment 38, and the like. Without departing from the scope or intent of the present disclosure, the data of sensor 32 can be acquired by sensor 32 and sent continuously, or can be sent periodically to the cloud computing server 13 through the I / O ports of the respective controllers 20, or collected and / or sent only when a triggering event occurs. In the controller 20 of the cloud computing server 13, the data received from the various sensors 32 of vehicles, infrastructure, etc. is aggregated and analyzed to determine time-based traffic waiting times and traffic signal 18 duration information.

[0040] Now turning more specifically to Figure 2 and continuing to refer to Figure 1 , the SAGEH 30 application is shown in schematic form as a series of control logic steps. SAGEH 30 generally includes a data collection section 100 and a feature stream section 150, which communicate with each other via a series of control logic subroutines. The data collection section 100 includes several of its own subroutines, specifically a first control logic 102 that acquires sensor 32 data, including camera 34 data, time-of-day information, traffic information, traffic signal 18 cycles, GPS location information, lane information, and the like. The data collection section 100 then proceeds to block 104, where triggered data collection and templated data transfer from vehicle(s) 12 to the cloud computing server 13 occur. Camera 34 data, data time, and other estimated parameters are included in the triggered and templated data transfer.

[0041] Now turning more specifically to Figure 3 and continuing to refer to Figure 1 and Figure 2 , Figure 2At block 104, the process of triggering and templating data transmission is shown in more detail. Starting from block 200, one or more sensors 32 generate situation data regarding the host vehicle 12. The sensor 32 data may indicate that the host vehicle 12 is encountering a new traffic signal 18, a new situation on a road segment 38, or an environmental change has occurred, or that the data generated by the sensors 32 or received from the cloud computing server 13 is out of sync. At block 202, the system 10 triggers a new query. At block 204, the cloud computing server 13 generates a navigation-based data request that causes the host vehicle 12 to utilize an in-vehicle navigation system that can interact with one or more of the communication towers 14 and GPS satellites 16 to determine the location of the host vehicle 12 and / or one or more remote vehicles 12'. At block 206, the SAGEH 30 application enables the system 10 to collect data from a specific location or along a specific road segment 38. The data collected at block 206 is represented at block 208 and may include camera 34 data, time-of-day information, season information, traffic information, GPS location, and navigation information, etc. The SAGEH 30 application proceeds from block 206 to block 210, where the sensor 32 data is processed to calculate and derive parameter information and to time-synchronize the received data. The processed sensor 32 data is then transmitted to the cloud computing server 13. In several aspects, the data processing includes analyzing and extracting road segment 38 parameter data, including the data represented at block 212. In several aspects, the data at block 212 includes, but is not limited to: the number of lanes on the road segment 38, the lane on which the host vehicle 12 is currently traveling on the road segment 38, the time of day, the season status, the holiday status, whether the host vehicle 12 is a school bus or other such special vehicle 12, the duration of the traffic signal 18 lights, the traffic condition (from strong to weak), the time and distance to reach the traffic signal 18 from a given location, the average speed of the vehicle 12, the estimated number of vehicles 12' in front of the host vehicle 12 based on: the distance to the traffic signal 18, etc. The SAGEH 30 application proceeds from block 210 to block 106, where the templated sensor 32 data is compared with and / or added to the crowdsourced data hosted within the cloud computing server 13. In several aspects, the comparison at block 106 includes a function approximation and normalization process that allows information to be extracted from the crowdsourced data and the templated sensor 32 data, including time-based traffic information, traffic waiting times, and traffic signal 18 duration information. In one example, the normal peak traffic travel time from the current location to the traffic signal 18 may take approximately forty seconds to travel thirty-five (35) meters or less distance to the traffic signal 18. In contrast, when traveling more than thirty-five meters to the traffic signal 18 during the same normal peak traffic travel time of the day, the same vehicle 12 may take ninety (90) seconds or more to travel to the traffic signal 18.

[0042] Refer again toFigure 2 And continuing reference Figure 1 and Figure 3 Once the function approximation and normalization processes for information extraction occur at block 106, the SAGEH 30 application proceeds to the feature stream section 150. Specifically, the feature stream section 150 begins at block 152, where the energy harvesting feature is enabled. The automatic energy harvesting feature of each vehicle 12 can be different. However, it should be understood that the energy harvesting feature of vehicle 12' can include automatic regenerative braking and / or an internal combustion engine (ICE) automatic stop-start system, combinations thereof, or similar energy retention or harvesting features of vehicle 12'. At block 154, the SAGEH 30 application uses sensors 32, including camera 34, to continuously or segmentally observe the traffic signal 18 and the situational state relative to the navigation path of the host vehicle 12. The data of the sensors 32 used are schematically shown at block 156 and can include camera 34 data, LiDAR data, SONAR data, ultrasonic sensor 32 data, etc., as well as ToD information, traffic information, traffic signal 18 cycle, GPS position, and navigation path information, etc.

[0043] Now turning to Figure 4 And continuing reference Figures 1 to 3, more particularly shows the continuous observations performed at block 154. The continuous observations 154 generally include a traffic jam estimation subroutine 300 having several additional control logic subroutines. Specifically, when the energy harvesting feature 152 is enabled, the traffic jam estimation subroutine 300 is also activated. The sensor 32 including the camera 34 acquires camera 34 data, traffic information, traffic signal 18 cycle information, GPS information, and navigation path information, and at block 302, the traffic jam estimation subroutine 300 continuously monitors the sensor 32 data and collects information about the situation of the host vehicle 12, the road segment 38, the traffic signal 18, etc. In several aspects, the information collected and stored in the memory 24 may include navigation route selection, GPS data, camera 34 data, etc. Then the traffic jam estimation subroutine 300 proceeds to block 304, where a route-based focus area is determined. The route-based focus area may include various different types of data, but is generally understood to include map information based on the following: GPS information, current and / or historical navigation system route selections, etc. The traffic jam estimation subroutine 300 proceeds from block 304 to block 306, where the data from block 304 is processed to calculate and derive parameter information, which includes but is not limited to what is represented at block 308. Specifically, at block 308, the parameter information may include designations of different road segment 38 types, such as highways, urban roads, two-way, single-lane, multi-lane, etc. Additional parameter information may include road segment 38 characteristics, such as whether the road segment 38 is straight, whether it has left or right turns, roundabouts, and / or lane information. Similarly, the traffic signal 18 and / or signage may be included in the parameter information. In some examples, the traffic signal 18 and / or signage information may include yield signs, stop signs, specific lane traffic signal 18 and status information, other traffic signal 18 statuses, the presence or absence of pedestrian crossings, and the presence and / or quantity of pedestrians, physical obstacles, curbs, traffic cones, etc.

[0044] Starting from block 306, the traffic jam estimation subroutine 300 defines and analyzes the current situation of the focus area at block 310. In several aspects, the current situation information of the focus area is represented at block 312 and may include a series of different parameter sets, each parameter set defining a different situation.

[0045] In the first example 314, the first current situation focus area may include defining that the current traffic signal 18 is red; in the manner in which the host vehicle 12 is currently traveling, the host vehicle 12 will take approximately thirty seconds to reach the red traffic signal 18; there are approximately four other vehicles 12' in front of the host vehicle 12, accounting for approximately twenty meters between the host vehicle 12 and the red traffic signal 18; there is at least one reference vehicle, such as a black full-size SUV; the color of the traffic signal 18 for vehicles passing through the road section 38 at the intersection 36 is yellow; there are pedestrians passing through the intersection 36 or along the road section 38; there are three pedestrians on the path of the host vehicle 12; and / or there are six pedestrians on the opposite side of the path of the host vehicle 12.

[0046] In the second example 316, the second current situation focus area may include defining that the current traffic signal 18 is red; in the manner in which the host vehicle 12 is currently traveling, the host vehicle 12 will take approximately zero seconds to reach the red traffic signal 18 (i.e., the host vehicle 12 has already reached the traffic signal 18); there are approximately four other vehicles 12' in front of the host vehicle 12, accounting for approximately twenty meters between the host vehicle 12 and the red traffic signal 18; there is at least one reference vehicle, such as a black full-size SUV; the color of the traffic signal 18 for vehicles passing through the road section 38 at the intersection 36 is yellow; there are pedestrians passing through the intersection 36 or along the road section 38; there are three pedestrians on the path of the host vehicle 12; and / or there are six pedestrians on the opposite side of the path of the host vehicle 12.

[0047] In the third example 318, the third current situation focus area may include defining that the current traffic signal 18 is green at the current moment; in the manner in which the host vehicle 12 is currently traveling, the host vehicle 12 will take approximately sixty seconds to reach the green traffic signal 18; there are zero remote vehicles 12' in front of the host vehicle 12; there is at least one reference vehicle, such as a white sedan of a specific color; the color of the traffic signal 18 for vehicles passing through the road section 38 at the intersection 36 is red; there are pedestrians passing through the intersection 36 or along the road section 38; the number of pedestrians on the path of the host vehicle 12 is zero; and / or there are two pedestrians on the opposite side of the path of the host vehicle 12. However, it should be understood that although in the foregoing, the first, second, and third examples of the current situation focus area are intended to be non-limiting examples of the types of data that can be found or determined through the current situation analysis of the focus area at block 310 without departing from the scope or intent of the present disclosure.

[0048] Refer again to Figure 2, once the continuous observation 154 is completed, the SAGEH 30 application proceeds to block 158, where the SAGEH 30 calculates an estimate of the amount of time the traffic signal 18 changes and estimates the time the host vehicle 12 stops for the signal 18. In several aspects, the estimates from block 158 utilize both static rules 160 and dynamic rules to determine the time the traffic signal 18 changes and the time the traffic signal 18 stops. Specifically, Figure 5 The calculations and estimates at block 158 are shown in more detail in

[0049] To calculate and estimate the amount of time the traffic signal 18 changes from one color to another, the SAGEH 30 application utilizes data from several different sources. Specifically, the SAGEH 30 application obtains or generates an information parameterization template at block 400, including current and historical data from sensors 32 and from the cloud computing server 13, including but not limited to: the current GPS location of the host vehicle 12, the traffic signal 18 identifier. In several aspects, the traffic signal identifier 18 is a unique identifier that defines which traffic signal 18 is currently relevant to the host vehicle 12 given the current planned path of the vehicle 12. The information template 400 also includes traffic signal 18 pattern information associated with the relevant traffic signal 18. The traffic signal 18 pattern information may include the amount of time the traffic signal 18 is programmed to be green (e.g., 180 seconds), red (e.g., 120 seconds), yellow (e.g., 5 seconds), green arrow (e.g., 90 seconds), etc. In another example, the traffic signal 18 pattern information may include regular peak time information based on ToD data. In some examples, when in regular traffic peak ToD, when the vehicle 12 is within thirty-five meters of the traffic signal 18, the information template 400 may indicate that the typical travel time for the host vehicle 12 to the traffic signal 18 is approximately forty seconds, while when the host vehicle 12 is more than thirty-five meters away from the traffic signal 18, the time for the host vehicle 12 to reach the traffic signal 18 may be approximately ninety seconds. The information template 400 may be at least partially sourced from crowdsourcing 106, including traffic waiting times based on current and historical times and the traffic signal 18 durations obtained by other vehicles 12' at block 402. At block 160, the SAGEH 30 application obtains the static rule set 160.

[0050] The static rule set 160 can include any of a variety of different operating rules for the host vehicle 12. For example, many host vehicles 12' utilize static rules that define a baseline set of behaviors for energy harvesting based on regenerative braking, automatic stop / start operation of the ICE vehicle 12 engine, and the like. The static rules set at block 160 can include, for example, rules that cause the ICE of the vehicle 12 to shut off when the host vehicle 12 stops, the brake pedal is depressed, and the accelerator pedal position is at the zero throttle position, and the host vehicle 12 HVAC system is in the "off" state. The rules can also include determining whether the temperature of the ICE is at an optimal threshold temperature, and when the ICE is operating at a temperature below the threshold temperature, the ICE is not commanded to shut off, and when the threshold temperature is met or exceeded, the ICE is selectively commanded to shut off. Similarly, when it is determined that the host vehicle 12 operator is decelerating at a predetermined rate, equal to or below a predetermined speed, etc., the regenerative braking system in at least a partially hybrid or fully electric vehicle 12 can be enabled. At block 406, the SAGEH 30 application obtains focus area related parameters, such as the current state of the traffic signal 18, the estimated time to reach the traffic signal 18, the number of remote vehicles 12' in front of the host vehicle 12, whether there is a crosswalk, etc. The calculations and estimations at block 158 utilize the focus area related parameters from block 406, the static rule set from block 160, the crowdsourced data from block 402, and the information template information from block 400 as inputs to an artificial intelligence (AI), machine learning (ML), or rule-based engine at block 408.

[0051] The AI, ML, or rule-based engine can utilize a variety of different AI, ML, and / or rule-based applications 28 or control logic subroutines to define an estimate of the traffic signal 18 behavior along the planned path of the host vehicle 12. Without departing from the scope or intent of the present disclosure, the AI, ML, and / or rule-based methods can include, but are not limited to, linear regression models, deep neural networks, logistic regression, decision trees, linear discriminant analysis, training using crowdsourced data, and the like. In an example of a rule-based engine, the SAGEH 30 application can utilize control logic according to the following logical flow pattern:

[0052] If (traffic signal 18 == red) && (path == straight) && (# of vehicles 12' in front >= 3) && (time == medium traffic) && (estimated time to reach traffic signal 18 <= 40% of traffic signal 18 duration) -> wait time at traffic signal 18 < estimated 30 seconds at red traffic signal 18.

[0053] However, it should be understood that the above logical flow is only intended as a non-limiting example of a rule-based approach, and deviations therefrom are intended to be within the scope and intended coverage of the present disclosure.

[0054] The AI, ML, and / or rule-based engine at block 408 provides calculations and estimations at block 158 to generate an estimation of the behavior of traffic signal 18 at block 410. In several aspects, the estimation of the behavior of traffic signal 18 at block 410 varies significantly depending on the situation and application.

[0055] In a first example 412, assuming that the closest traffic signal 18 is currently "red" and there are four (4) vehicles 12' in front of the host vehicle 12 at the current time of day, and the host vehicle 12 will take approximately fifteen seconds to reach the traffic signal 18, the traffic signal 18 may turn "green" in approximately twenty (20) seconds. Therefore, given the situation defined in the first example, the host vehicle 12 can selectively enable the ICE and / or EV control systems to change the behavior of the ICE and / or EV control systems of the host vehicle 12.

[0056] In a second example 414, assuming that the closest traffic signal 18 is currently "green" and there are zero (0) vehicles 12' in front of the host vehicle 12 at the current time of day, and the host vehicle 12 will take approximately twenty seconds to reach the traffic signal 18, the traffic signal 18 may turn "red" in approximately twenty (20) seconds. Therefore, given the situation defined in the first example, the host vehicle 12 can selectively enable the ICE and / or EV control systems to change the behavior of the ICE and / or EV control systems of the host vehicle 12.

[0057] Referring again to Figure 2 And continuing to refer to Figure 1 And Figures 3 to 5 , once at block 158, the SAGEH 30 application has calculated and generated an estimated amount of time for the traffic signal 18 to change from one color to another and / or calculated an estimated stop time at the traffic signal 18, the SAGEH 30 application proceeds to block 162, where the system 10 generates an ICE and / or EV control system output.

[0058] The ICE and / or EV control system output calculation is at Figure 6is shown in greater detail in. In several respects, the ICE and / or control system output calculations at block 162 utilize inputs from various sources, including data obtained from various sensors 32. More specifically, at block 500, primary vehicle 12 parameters are obtained from sensors 32. The primary vehicle 12 parameters can include various information related to the movement of the primary vehicle 12, its position on the road segment 38, and its GPS position. In some non-limiting examples, the primary vehicle 12 parameters can include the speed of the primary vehicle 12 in the X direction, the speed of the primary vehicle 12 in the Y direction, the current state of charge (SoC) of the traction battery of the electric or electrified vehicle 12, and so on.

[0059] At block 502, the SAGEH 30 application obtains the current static rule set and calibration and obstacle interruption detection along the road segment 38. In some non-limiting examples 503, for a vehicle 12 equipped with an ICE, the static rule set and calibration can include rules to prevent the ICE from shutting down when the external ambient temperature is below a threshold temperature or above a second threshold temperature, when the fuel level of the primary vehicle 12 is less than or equal to a predetermined minimum capacity threshold, and so on. In some specific non-limiting examples, the predetermined minimum fuel capacity threshold can be a fuel level less than or equal to 1% of the fuel capacity of the vehicle 12, and the external ambient temperature threshold can be above 90 degrees Fahrenheit, or below 15 degrees Fahrenheit, and so on. Similarly, for an electric vehicle (EV), the static rule set and calibration can include rules to prevent regenerative braking from being activated when the EV is traveling in traffic moving at a speed below a threshold speed (e.g., below twenty miles per hour), when there are no stopping points or exits available, when the primary vehicle 12 operator indicates a desire to allow the EV to coast, and so on. In another example, when the sensors 32 detect a change in the environment or situation, the static rule set, calibration, and obstacle interruption detection are recalculated to accommodate the changing environment.

[0060] The estimated traffic signal 18 behavior from block 410 is retrieved and used as an input, along with the primary vehicle parameters from block 500 and the current static rule set from block 502, for the ICE and / or EV control output calculations at block 504. In several respects, the ICE and / or EV control output calculations at block 504 include calculations for generating a control signal at block 506 that manages the performance of the ICE and / or EV system in the vehicle 12'.

[0061] In a non-limiting example, at block 506 for the ICE system, the control output calculation generates a control output 508 that causes the engine in vehicle 12 equipped with an ICE to remain on or to automatically shut off when one or more threshold conditions are met. More specifically, in vehicle 12 equipped with an ICE, the control output calculation can determine that the ICE of the host vehicle 12 will be shut off when the amount of time the host vehicle 12 is stopped is greater than or equal to a threshold amount of time. Subsequently, based on the predicted traffic signal 18 stop time, i.e., the amount of time before the color change from red to green occurs, the control output calculation at block 504 will start the ICE approximately one second before the end of the predicted traffic signal 18 stop time. By anticipating the traffic signal 18 color change in this way, the amount of time that the host vehicle 12 is stationary with the ICE off while the traffic signal 18 is green is significantly reduced. By anticipating the change in the traffic signal 18 color in this way, the amount of time that the vehicle 12 operator has to wait before being able to use the ICE to accelerate is significantly reduced or even completely eliminated, resulting in smoother traffic flow and increased customer satisfaction and trust in the host vehicle 12.

[0062] In another non-limiting example involving an EV vehicle 12’, the control output calculation generates a control output 508 that causes the regenerative braking system of a fully electric or partially electric vehicle 12 to change its performance when one or more threshold conditions are met. More specifically, in such a fully electric or partially electric vehicle 12 or EV, the control output calculation can determine which of several different regenerative braking modes should be employed. For example, a first-level coasting mode can be employed and the amount of regeneration can be gradually, dynamically, automatically, adaptively increased based on factors such as, for a given traffic signal 18, the distance to travel, the presence of obstacles, and / or the distance to the traffic signal 18 and / or the current color and the estimated time until the color change. The intensity of the coasting mode regenerative braking can also be dynamically, automatically, adaptively increased until the EV comes to a complete stop at the traffic signal 18 when the traffic signal is red. In some specific but non-limiting examples, above a threshold distance of approximately fifty meters from a red or predicted-to-be-red traffic signal 18, the host vehicle 12 performs regenerative braking energy harvesting at approximately 25% capacity. When the host vehicle 12 subsequently reaches the fifty-meter threshold distance, the host vehicle 12 increases the regenerative braking to approximately 65%, and when the host vehicle 12 is only ten meters from the red traffic signal 18, the regenerative braking is increased to 90% intensity or 90% capacity or higher, so that the host vehicle 12 stops at an appropriate position relative to the red traffic signal 18, crosswalk, etc.

[0063] By predicting the color change of the traffic signal 18 in this manner, the smoothness of the regenerative braking process is improved, while reducing the intervention of the operator of the vehicle 12 in the results of the braking, coasting, and acceleration processes, as well as the overall improvement in smooth traffic flow, and increasing the satisfaction and trust of the customer in the host vehicle 12. It should be understood that the examples of control output calculations can vary significantly depending on the application and circumstances without departing from the scope or intent of the present disclosure.

[0064] Referring again to Figure 2 and continuing to refer to Figure 1 and Figures 3 to 6 , once the control output 508 has been sent to the vehicle 12 control system, including one or more of the ICE and regenerative braking systems, the SAGEH 30 application proceeds to block 164, where the operator of the vehicle 12 can provide feedback regarding the control output 508. More specifically, the operator of the host vehicle 12 can provide feedback regarding the control output 508 relative to the desired response of the vehicle 12 of the vehicle 12 operator. The SAGEH 30 application utilizes the host vehicle 12 operator feedback to adjust future control output calculations through a collaborative learning mechanism.

[0065] Turning now to Figure 7 and continuing to refer to Figures 1 to 6 , the host vehicle 12 operator feedback at block 164 is shown in more detail in flowchart form. At block 600, the operator feedback section 164 of the SAGEH 30 application begins. At block 602, once the SAGEH 30 application determines an energy harvesting strategy at block 508, the time-ordered actions of the operator of the host vehicle 12 are collected during a specified time window. For example, sensor 32 data regarding brake application, acceleration, lane changes, etc. is collected. At block 604, a trigger check is performed. The trigger check compares the expected vehicle 12 and traffic signal 18 behavior against the actual recorded vehicle 12 and traffic signal 18 behavior. If the difference between the expected behavior and the actual behavior is greater than a threshold difference value, the operator feedback section 164 is triggered and ready to generate a vehicle 12 operator feedback request. In several aspects, the trigger check at block 604 utilizes a trigger rule set, which can cause the system 10 to trigger periodically, continuously, etc. after a predetermined number of events. In some non-limiting examples, the trigger rule set can cause the trigger check at block 604 to occur once a month, upon reaching ten or more events, etc.

[0066] At block 606, the operator feedback section 164 creates a trigger for the operator feedback request of vehicle 12. The trigger at block 606 uses the trigger rule set from block 604 to specify the scale and bounds of the thresholds for the expected and actual vehicle 12 and traffic signal 18 behaviors. At block 608, the operator feedback section 164 prepares a time series of the expected actions and the executed actual actions as well as feedback information. In several respects, the data prepared at block 606 includes energy automatic collection system control output decision information and the like.

[0067] At block 610, the operator feedback section 164 determines when the actual action and the expected action are relevant within a predetermined amount of time. When it is determined that the actual action and the expected action are indeed relevant during the predetermined amount of time, the operator feedback section 164 proceeds to block 612, where the current GPS position of the host vehicle 12 is marked or otherwise labeled as "good" or otherwise processed in an accurate manner. However, when the actual action and the expected action are not relevant at block 610, the operator feedback section 164 of the SAGEH 30 application proceeds to block 614, and marks the current GPS position of the host vehicle 12 with the difference between the actual action and the expected action, and the resulting information is transmitted via the I / O port to the cloud computing server 13, where the database is appropriately updated based on the difference between the actual information and the expected information. Additionally, when the actual action and the expected action are not relevant, the operator feedback section 164 proceeds to block 616, where a consistency check is performed. The consistency check at block 616 causes the system 10 to monitor the number of change requests and determine whether the requested difference is greater than or equal to a threshold change value. When the change requests exceed the threshold, the consistency check at block 616 results in the collection of additional data. However, when the requested difference is less than the threshold change value, the operator feedback section 164 triggers an update and causes the difference to be implemented in the memory 24 of the cloud computing server 24. The operator feedback section 164 proceeds from block 612 or block 614 to block 618, where the operator feedback section ends. The operator feedback section 164 can work continuously, periodically, or when one or more events occur during the operation of vehicle 12 by returning to block 600 again and running again.

[0068] Referring again to Figure 2 , and continuing to refer to Figure 1 and Figures 3 to 7 , once the consistency check is performed at block 616, the SAGEH 30 application returns to the data collection section 100 again, and uses the result of the consistency check at block 616 to update the crowdsourced data 106 during the next iteration while the SAGEH 30 application is running.

[0069] The systems and methods for situation awareness-guided energy harvesting of the present disclosure provide multiple advantages. These include situation awareness energy harvesting while leveraging existing hardware and while maintaining or reducing the component complexity and computational complexity of system 10, while simultaneously increasing the comfort and satisfaction of the operator of the primary vehicle 12 and while simultaneously reducing the energy consumption of vehicles 12, 12' equipped with system 10.

[0070] The description of the present disclosure is merely exemplary in nature and variations that do not depart from the gist of the present disclosure are intended to fall within the scope of the present disclosure. These variations should not be regarded as departing from the spirit and scope of the present disclosure.

Claims

1. A system for situational awareness guided energy harvesting in a vehicle, comprising: A main vehicle and one or more remote vehicles; one or more sensors that capture host vehicle information and remote vehicle information and capture environmental information about the environment of the host vehicle and the one or more remote vehicles; and a cloud computing server in communication with the host vehicle and one or more of the remote vehicles; Each of the host vehicle, the one or more remote vehicles, and the cloud computing server has a controller, the controller includes a processor, a memory, and one or more input / output I / O ports, the I / O ports communicate with the one or more sensors; the memory stores program control logic; the processor executes the program control logic; the program control logic includes a situational awareness guided energy harvesting SAGEH application, the SAGEH application includes: a first control logic for triggering the collection of the host vehicle information, the remote vehicle information, and the environmental information from the one or more sensors; a second control logic for continuously observing traffic signals along the road segment and traffic situations along the navigation path of the primary vehicle; third control logic for generating a first estimated amount of time for the traffic signal to change state and for generating a second estimated amount of time for the host vehicle to stop at the traffic signal; fourth control logic for generating a control output command in response to one or more of the first estimated amount of time and the second estimated amount of time; and A fifth control logic enables a vehicle operator to generate feedback regarding the control output command, wherein the control output command causes the vehicle to dynamically, automatically, and adaptively enable one or more of an internal combustion engine (ICE) stop / start system and a regenerative braking system to dynamically, automatically, and adaptively harvest energy.

2. The system according to claim 1, wherein: The first control logic further includes: Detecting host vehicle position information and remote vehicle position information, host vehicle navigation information, and environmental information in data from the one or more sensors, wherein the one or more sensors further include one or more of the following: Cameras, light detection and ranging LiDAR sensors, radio detection and ranging RADAR sensors, sound navigation and ranging SONAR sensors, ultrasonic sensors, motion sensors, inertial measurement units IMU, global positioning system GPS sensors, communication tower sensors and traffic signal sensors; Determining that the status of the main vehicle has changed according to the main vehicle position information and the remote vehicle position information, the main vehicle navigation information and the environmental information; Implementing data collection specific to the road segment, including collecting from the one or more sensors: camera data; time of day ToD information, seasonal information, traffic information, and global positioning system GPS location and navigation information; deriving parameter information in data specific to the road segment; and The data specific to the road section is updated through the parameter information, and the updated road section related information is sent to the cloud computing server.

3. The system according to claim 2, wherein: The first control logic further includes: accessing crowd-sourced data stored in a memory of the cloud computing server and obtained from sensors of the one or more remote vehicles and sensors disposed on infrastructure, including: traffic signals, GPS satellites, and communication towers; and The data specific to the road segment is normalized to determine at least time-based traffic wait time and traffic signal duration information along the road segment.

4. The system according to claim 1, wherein: The second control logic further includes: enabling an energy harvesting feature of the host vehicle; and Executing traffic jam estimation control logic, wherein the traffic jam estimation control logic runs while the energy harvesting feature is enabled, and wherein the traffic jam estimation control logic performs continuous situational information collection, including: monitoring the navigation route of the main vehicle, monitoring GPS data, monitoring camera data, monitoring traffic information, and monitoring traffic signal cycles.

5. The system according to claim 4, wherein: The second control logic further includes: processing the data collected by the traffic jam estimation control logic to calculate and derive parameter information about the road segment, the parameter information including: highway information, urban road information, single lane information or multi-lane information, through road information, left turn information, right turn information, roundabout information, lane information, yield sign information, stop sign information, specific lane traffic signal and status information, other traffic signal status information, crosswalk information, and determining the number of pedestrians; and Focus area situation analysis control logic is executed to define the current situation of the host vehicle along the road segment.

6. The system according to claim 4, wherein: The third control logic further includes: The first estimated amount of time and the second estimated amount of time are calculated based on the current traffic signal status, the number of remote vehicles ahead of the host vehicle, the time of day, a static rule set and crowd-sourced data, wherein the crowd-sourced data includes: the GPS location of the host vehicle, a traffic signal identifier, traffic wait times based on current and historical times, and current and historical traffic signal durations.

7. The system according to claim 1, wherein: The fourth control logic further includes: Control output commands are dynamically, automatically, and adaptively generated to one or more of a regenerative braking system and an automatic stop / start system of the host vehicle using a static rule set, vehicle parameters including a current host vehicle speed and a current host vehicle state of charge, and estimated traffic signal behavior.

8. The system according to claim 7, wherein: Generating the control output command to the regenerative braking system of the host vehicle further comprises: The regenerative braking intensity is dynamically, automatically, and adaptively adjusted based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic status on the road segment, and the estimated traffic signal behavior.

9. The system according to claim 7, wherein: Generating the control output command to the automatic stop / start system of the host vehicle further comprises: The ICE stop / start system is dynamically, automatically, and adaptively adjusted based on the speed of the host vehicle, the distance between the host vehicle and the traffic signal, the traffic conditions on the road segment, and the estimated traffic signal behavior to selectively stop the ICE of the host vehicle.

10. The system according to claim 1, wherein: The fifth control logic further includes: collecting the control logic of the actions performed by the vehicle operator in chronological order during the time window; control logic to compare the expected behavior and the actual behavior to a threshold, wherein upon determining that the actual behavior is greater than the threshold, marking the current host vehicle position by a difference between the actual behavior and the expected behavior; and and control logic for triggering change requests for crowdsourced data hosted in the memory of the cloud computing server, wherein when the number of change requests reaches or exceeds a change request threshold, control logic for triggering a change in expected behavior, and when the number of change requests falls below the change request threshold, control logic for triggering additional data collection.

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