System, method and vehicle for modifying the driving behavior of a vehicle driver

Abstract

To reduce undesirable situations caused by drive behavior that a driver considers to be correct or legal.SOLUTION: A systems and a method for correcting a drive behavior of a driver of a vehicle, and a vehicle are provided. The system includes a controller that is programed to identify a repetitive motion pattern of a driver of a vehicle at a predetermined location based on accumulated drive data of the vehicle, execute digital twin simulation of the vehicle using a repetitive motion pattern and the digital twin simulation of another object and identify potential collision between the vehicle and the other object to inform the driver of the vehicle of predetermined drive behavior in response to identification of the potential collision.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to a system, method, and vehicle for modifying the driving behavior of a driver of the vehicle. [Background technology]

[0002] Generally, drivers will perform driving behaviors (e.g., backing into parking spaces, making U-turns) that they consider to be correct or legal. Such driving behaviors are reinforced through repetition and become driving habits. However, when other objects (e.g., vehicles) are present, such driving behaviors, habits, or both (e.g., following speed limits, following maneuvering rules, or both) may cause undesirable situations.

[0003] Conventional systems and methods can correct these driving behaviors through driver guidance and abnormal driving detection research. For example, conventional systems and methods look for indicators that are a type of abnormal driving that exceeds a certain limit. However, in such cases, the driver may not have enough time to correct the driving behavior. Furthermore, in some cases, conventional systems and methods may not be able to detect abnormal driving. For example, if the driver is obeying the speed limit, conventional systems and methods may not be able to detect abnormal driving.

[0004] Thus, there is a need for systems, methods and vehicles that mitigate undesirable situations caused by driving behavior that a driver believes to be correct or legal. Summary of the Invention

[0005] The present disclosure provides a system, method, and vehicle for modifying a driving behavior of a driver of a vehicle, which mitigates undesirable situations caused by the driving behavior by observing and modifying the driving behavior of the driver before a potential collision between the vehicle and another object. The system, method, and vehicle identify a repetitive movement pattern of the driver of the vehicle at a given location, regardless of whether the driver is following a speed limit, a maneuvering rule, or both. Thus, the system, method, and vehicle may detect abnormal driving behavior even if the driver is following a speed limit, a maneuvering rule, or both. Thus, the system, method, and vehicle can notify the driver of correct or legal driving behavior before an abnormal event such as a vehicle collision occurs, thereby reducing a potential collision such as a vehicle collision.

[0006] In one embodiment, a system for modifying a driving behavior of a driver of a vehicle is provided, the system including a controller programmed to identify a repetitive movement pattern of a driver of the vehicle at a predetermined location based on accumulated driving data of the vehicle, perform a digital twin simulation of the vehicle using the repetitive movement pattern and a digital twin simulation of another object to identify a potential collision between the vehicle and the other object, and notify the driver of the vehicle of the predetermined driving behavior in response to identifying the potential collision.

[0007] In another embodiment, a method for modifying a driving behavior of a driver of a vehicle is provided, the method including accumulating a repetitive movement pattern of a driver of the vehicle at a predetermined location, conducting a digital twin simulation of the vehicle using the repetitive movement pattern and a digital twin simulation of another object to identify a potential collision between the vehicle and the other object, and in response to identifying the potential collision, notifying the driver of the vehicle of the predetermined driving behavior.

[0008] In yet another embodiment, a vehicle for modifying a driving behavior of a driver of the vehicle is provided. The vehicle includes an actuator, a controller, and an output device. The actuator is configured to move the vehicle. The controller is programmed to identify a repetitive movement pattern of a driver of the vehicle at a predetermined location based on accumulated driving data of the vehicle, and in response to identifying a vehicle proximate to the predetermined location, perform a digital twin simulation of the vehicle using the repetitive movement pattern and a digital twin simulation of another object to identify a potential collision between the vehicle and the other object, and in response to identifying the potential collision, transmit the predetermined driving behavior to the output device. The output device is configured to output the predetermined driving behavior.

[0009] These and other features provided by the embodiments of the present disclosure will be more fully understood by considering the following detailed description in conjunction with the drawings. [Brief description of the drawings]

[0010] [Figure 1A] FIG. 1A illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a reverse parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 1B] FIG. 1B illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a reverse parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 1C] FIG. 1C illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a reverse parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 1D] FIG. 1D illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a reverse parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 1E]FIG. 1E illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a reverse parking motion pattern, according to one or more embodiments illustrated and described herein. [Diagram 2] FIG. 2 shows a schematic diagram of a system for modifying the driving behavior of a driver of a vehicle according to one or more embodiments shown and described herein. [Diagram 3] FIG. 3 illustrates a flowchart of a method for modifying the driving behavior of a driver of a vehicle according to one or more embodiments illustrated and described herein. [Figure 4A] FIG. 4A illustrates generally an exemplary embodiment of modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a U-turn motion pattern, according to one or more embodiments illustrated and described herein. [Figure 4B] FIG. 4B illustrates generally an exemplary embodiment of modifying the driving behavior of a driver of a vehicle when the repetitive motion pattern is associated with a U-turn motion pattern, according to one or more embodiments illustrated and described herein. [Figure 4C] FIG. 4C illustrates generally an exemplary embodiment of modifying the driving behavior of a driver of a vehicle when the repetitive motion pattern is associated with a U-turn motion pattern, according to one or more embodiments illustrated and described herein. [Figure 4D] FIG. 4D illustrates generally an exemplary embodiment of modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a U-turn motion pattern, according to one or more embodiments illustrated and described herein. [Figure 4E] FIG. 4E illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a U-turn motion pattern, according to one or more embodiments illustrated and described herein. [Figure 4F] FIG. 4F illustrates generally an exemplary embodiment of modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a U-turn motion pattern, according to one or more embodiments illustrated and described herein. [Figure 5A]FIG. 5A illustrates generally an exemplary embodiment for modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 5B] FIG. 5B illustrates generally an exemplary embodiment of modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 5C] FIG. 5C illustrates generally an exemplary embodiment of modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a parking motion pattern, according to one or more embodiments illustrated and described herein. [Figure 5D] FIG. 5D illustrates generally an exemplary embodiment of modifying a driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a parking motion pattern, according to one or more embodiments illustrated and described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] The following detailed description of specific embodiments of the present disclosure can be best understood when read in conjunction with the following drawings, in which like structure is indicated with like reference numerals and in which:

[0012] Embodiments disclosed herein include methods, systems, and vehicles for modifying the driving behavior of a driver of a vehicle, by observing and modifying the driver's driving behavior prior to a potential collision between the vehicle and another object, thereby mitigating dangerous situations caused by the driving behavior.

[0013] 1A, 1B, 1C, 1D and 1E illustrate schematic diagrams of an exemplary embodiment for modifying the driving behavior of a driver of a vehicle when a repetitive motion pattern is associated with a reverse parking motion pattern, according to one or more embodiments illustrated and described herein.

[0014] 1A-1E, the vehicle 100 identifies a repetitive motion pattern 102 of a driver of the vehicle 100 at a predetermined location based on accumulated driving data of the vehicle 100. The vehicle 100 accumulates the repetitive motion pattern 102 to define a predetermined driving behavior. The vehicle 100 stores a driving trajectory using a sensor such as a GPS sensor, and identifies the multiple driving trajectories as a repetitive motion pattern when there are multiple driving trajectories that are similar to each other within a certain period of time. In some embodiments, the vehicle 100 transmits the identified repetitive motion pattern to the server 140. In some embodiments, the vehicle 100 transmits its trajectory to the server 140, and the server 140 identifies the repetitive motion pattern of the vehicle 100. In FIG. 1A-1E, the repetitive motion pattern 102 is associated with a parking motion pattern, and the predetermined location includes a parking spot 104. The vehicle 100 may be a vehicle including an automobile or any other passenger or non-passenger vehicle, such as a land vehicle, a water vehicle, and / or an air vehicle. Vehicle 100 may be an autonomous connected vehicle that navigates its environment with limited or no human input. Vehicle 100 is equipped with internet access to share data with other devices both inside and outside vehicle 100. Vehicle 100 communicates with and transmits its data to server 140. For example, vehicle 100 transmits information about its repetitive motion patterns, its current location and destination, information about the current driver, information about the task currently being performed, etc.

[0015] Vehicle 100 includes an actuator configured to move vehicle 100. Vehicle 100 includes an output device configured to output a predetermined driving behavior of a driver. In an embodiment, the output device of vehicle 100 includes a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof.

[0016] FIG. 1A illustrates the collection of repetitive motion patterns for reverse parking of a vehicle in the real world, according to one embodiment illustrated and described herein. Referring to FIG. 1A, a vehicle 100 reverses into a parking spot 104 using a repetitive motion pattern 102. The vehicle 100 turns on the left turn signal for reverse parking, and then veers right for lining up. The vehicle 100 reverses and reverses into a parking spot 104. In an embodiment, when the repetitive motion pattern 102 is associated with a reverse parking motion pattern, the repetitive motion pattern 102 includes turning on the left turn signal, veering right for lining up, and reverses into the parking spot 104. The driver of the vehicle 100 believes that the repetitive motion pattern 102 is correct or legal. In an embodiment, a server 140 collects, receives, or both performs the repetitive motion pattern 102 of the vehicle 100. The server 140 is communicatively coupled to the vehicle 100 via a network 170.

[0017] 1B illustrates a vehicle about to begin a repetitive motion pattern of reverse parking in the real world, according to one embodiment shown and described herein. With reference to FIG. 1B, another object 110 is behind the vehicle 100. In an embodiment, the another object 110 is another vehicle. The vehicle 100 detects the other object 110 within a predetermined distance from the vehicle 100. The predetermined distance from the vehicle 100 depends on the speed of the vehicle 100, the size of the vehicle 100, the speed of the other object 110, the size of the other object 110, or a combination thereof.

[0018] In this example, when the vehicle 100 repeats the repetitive motion pattern 102 for reverse parking, the repetitive motion may cause a potential collision with another object 110. Specifically, when the vehicle 100 turns on the left turn signal to reverse park, the other object 110 may veer to the right to pass the vehicle 100. At the same time, the vehicle 100 may veer to the right to line up for reverse parking. These motions may cause a potential collision between the vehicle 100 and the other object 110.

[0019] Therefore, before the vehicle 100 begins the repetitive reverse parking motion pattern, the vehicle 100 may perform a digital twin simulation, as shown in FIG. 1C, to identify potential collisions and take preventative measures, as shown in FIGS. 1D and 1E.

[0020] FIG. 1C illustrates a digital twin simulation of a vehicle and another vehicle according to one embodiment illustrated and described herein. Referring to FIG. 1C, the vehicle 100 performs a digital twin simulation of the vehicle 100 using a repetitive motion pattern 102 and a digital twin simulation of the other object 110 to identify this potential collision 112 between the vehicle 100 and the other object 110. In some embodiments, the server 140 may perform a digital twin simulation of the vehicle 100 using a repetitive motion pattern 102 and a digital twin simulation of the other object 110. A digital twin simulation is a simulated version of a particular real-world vehicle, object, or both that exists in a simulation. The structure, state, behavior, and responses of the digital twin are similar to the structure, state, behavior, and responses of the particular real-world vehicle, object, or both that the digital twin represents in the simulation. The digital environment included in the simulation is similar to the real-world road environment of the real-world vehicles and objects (e.g., the vehicle 100, the other object 110, other vehicles or objects in the road environment). In an embodiment, vehicle 100 or server 140 includes simulation software that performs a digital twin simulation. The simulation software includes code and routines operable to run a simulation based on a digital twin of a real-world vehicle, object, or both in a road environment.

[0021] 1B , when the vehicle 100 detects another object 110 within a predetermined distance from the vehicle 100, the vehicle 100 performs a digital twin simulation of the vehicle 100 using the repetitive motion pattern 102 and a digital twin simulation of the other vehicle 110 in response to identifying the vehicle 100 in proximity to a predetermined location and detecting the other object 110 within the predetermined distance from the vehicle 100 to identify a potential collision 112 between the vehicle 100 and the other object 110. Specifically, in the simulation, the vehicle 100 follows the repetitive motion pattern 102 and the other object 110 follows a path 114. Thereafter, a collision 112 occurs between the repetitive motion pattern 102 and the path 114.

[0022] 1D and 1E, after the simulation, the real-world vehicle 100, in response to identifying the potential collision 112, notifies the driver of the vehicle 100 of a predefined driving behavior that may prevent the potential collision. In an embodiment, the vehicle 100 displays the predefined driving behavior. For example, a screen of the vehicle 100 displays the predefined driving behavior. In some embodiments, the predefined driving behavior may be displayed on a device other than the device of the vehicle 100, for example, a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In some embodiments, the vehicle 100 displays the results of a digital twin simulation of the vehicle 100 and another object 110, for example, the digital twin simulation shown in FIG. 1C, indicating a potential collision 112.

[0023] In an embodiment, the vehicle 100 or the server 140 instructs a device of the other object 110 to output a warning. In an embodiment, the device of the other object 110 includes a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In an embodiment, the vehicle 100 or the server 140 instructs a device of the other object 110 to output a warning with the result that a digital twin simulation of the vehicle 100 and the other object 110 indicates a potential collision 112.

[0024] Referring to FIG. 1D, if there is a potential collision 112 between the vehicle 100 and another object 110, the vehicle 100 instructs the driver of the vehicle 100 to park the vehicle 100 forward. In an embodiment, an output device of the vehicle 100 may display a result that a digital twin simulation of the vehicle 100 and another object 110 indicates the potential collision 112 shown in FIG. 1C. In some embodiments, if there is a potential collision 112 between the vehicle 100 and another object 110, the controller may further explore and infer the correct driving behavior at a given location. For example, the controller checks a Department of Motor Vehicles (DMV) manual on parking and searches for the correct driving behavior. In some embodiments, if there is a potential collision 112 between the vehicle 100 and another object 110, the controller may obtain information on the correct driving behavior from other drivers. For example, the controller indicates the potential collision 112 between the vehicle 100 and another object 110 to other drivers, and the other drivers communicate information on the correct driving behavior to the driver of the vehicle 100.

[0025] 1E, if there is a potential collision 112 between the vehicle 100 and another object 110, the vehicle 100 instructs the driver of the vehicle 100 to reverse park the vehicle 100 without turning right as shown in FIG. 1A. If there is no other object in the nearest lane, the vehicle 100 may instruct the driver of the vehicle 100 to reverse park the vehicle 100 by reversing into the nearest lane. If there is no potential collision 112 between the vehicle 100 and another object, the vehicle 100 may not instruct the driver of the vehicle 100 to park the vehicle 100 forward, and the driver of the vehicle 100 may park the vehicle 100 backward by the driver's driving behavior described in FIG. 1A. In an embodiment, the display of the output device of the vehicle 100 displays the result that the digital twin simulation of the vehicle 100 and the another object 110 shows the potential collision 112 shown in FIG. 1C.

[0026] 2 shows a schematic diagram of a system for modifying the driving behavior of a driver of a vehicle according to one or more embodiments shown and described herein. The system 10 includes a vehicle system 200, a separate object system 220, and a server 240.

[0027] 2 illustrates vehicle system 200 communicating with one separate object system 220, it should be noted that vehicle system 200 may communicate with two or more separate object systems 220. In an embodiment, vehicle system 200 and each of separate object systems 220 are contained within a vehicle, which may be an automobile or any other passenger or non-passenger vehicle, such as, for example, a land vehicle, a water vehicle, and / or an air vehicle. In some embodiments, the vehicle may be an autonomous vehicle that navigates its environment with limited or no human input.

[0028] The vehicle system 200 includes one or more processors 202. Each of the one or more processors 202 is any device capable of executing machine-readable and executable instructions. Each of the one or more processors 202 is a controller, an integrated circuit, a microchip, a computer, or any other computing device. The one or more processors 202 are coupled to a communication path 204 that provides signal interconnectivity between various modules of the system. The communication path 204 communicatively couples any number of the processors 202 to each other, allowing the modules coupled to the communication path 204 to operate in a distributed computing environment. Each of the modules operates as a node that may transmit and / or receive data. As used herein, the term "communicatively coupled" means that the coupled components can exchange data signals with each other, such as electrical signals through conductive media, electromagnetic signals through air, and optical signals through optical waveguides.

[0029] The communication path 204 is formed from any medium capable of transmitting a signal, such as conductive wire, conductive trace, optical waveguide, etc. In some embodiments, the communication path 204 facilitates the transmission of wireless signals, such as WiFi, Bluetooth, Near Field Communication (NFC), etc. The communication path 204 may be formed from a combination of media capable of transmitting a signal. In one embodiment, the communication path 204 includes a combination of conductive traces, conductive wires, connectors, and buses that cooperate to enable the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices. The communication path 204 may include a vehicle bus, such as a LIN bus, a CAN bus, a VAN bus, etc. It is further noted that the term "signal" refers to a waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic), such as DC, AC, sine wave, triangle wave, square wave, vibration, etc., that can travel through a medium.

[0030] The vehicle system 200 includes one or more memory modules 206 coupled to the communication path 204. The one or more memory modules 206 may comprise a RAM, a ROM, a flash memory, a hard drive, or any device capable of storing machine-readable and executable instructions such that the machine-readable and executable instructions are accessible to the one or more processors 202. The machine-readable and executable instructions may include, for example, any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL), such as a machine language that may be executed directly by a processor, or logic or algorithms written in assembly language, object-oriented programming (OOP), scripting language, microcode, or the like, that may be compiled or assembled into machine-readable and executable instructions and stored in the one or more memory modules 206. The machine-readable and executable instructions may also be written in a hardware description language (HDL), such as logic implemented via a field programmable gate array (FPGA) configuration or an application specific integrated circuit (ASIC), or any of their equivalents. The methods described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements or as a combination of hardware and software components. The one or more processors 202, together with one or more memory modules 206, may operate as a controller for the vehicle system 200.

[0031] The one or more memory modules 206 include a repetitive action storage module 207 and a digital twin simulation module 209. The repetitive action storage module 207 and the digital twin simulation module 209 operate in cooperation or independently to modify driving behavior. Each of the repetitive action storage module 207 and the digital twin simulation module 209 may be a program module in the form of an operating system, application program modules, and other program modules stored in the one or more memory modules 206. In some embodiments, the program modules may be stored in a remote storage device that may communicate with the vehicle system 200, for example, in a cloud server or an edge server. Such program modules include, but are not limited to, routines, subroutines, programs, objects, components, data structures, and the like for performing certain tasks or executing certain data types as described below.

[0032] In an embodiment, the repetitive action accumulation module 207 identifies a repetitive action pattern of a driver of the vehicle at a given location based on the accumulated driving data of the vehicle. With reference to FIG. 1A, the repetitive action accumulation module 207 identifies a repetitive action pattern 102 including turning on the left turn signal, veering right to line up, and backing into a parking spot 104. For example, with reference to FIG. 4A, the repetitive action accumulation module 207 identifies a repetitive action pattern 402 including turning on the left turn signal, veering right to secure more space, and making a U-turn. For example, with reference to FIG. 5A, the repetitive action accumulation module 207 identifies a repetitive action pattern 502 including parking in a merging lane.

[0033] In an embodiment, the digital twin simulation module 209 performs a digital twin simulation of the vehicle using the repetitive motion patterns and a digital twin simulation of another object to identify a potential collision between the vehicle and the other object. Once the digital twin simulation module 209 performs the digital twin simulation of the vehicle using the repetitive motion patterns and the digital twin simulation of the other object to identify a potential collision between the vehicle and the other object, the digital twin simulation module 209 transmits instructions to inform a driver of the vehicle of a predetermined driving behavior in response to identifying the potential collision.

[0034] 2, the vehicle system 200 includes one or more sensors 208. The one or more sensors 208 are any device having an array of sensing devices capable of detecting radiation in the ultraviolet, visible, or infrared wavelength bands. The one or more sensors 208 can detect the presence of another object, such as another object 110 of FIGS. 1A-1C, the distance between the vehicle 100 and the another object 110, or both. The one or more sensors 208 may have any resolution. In some embodiments, one or more optical components, such as a mirror, a fisheye lens, or any other type of lens, may be optically coupled to the one or more sensors 208. In some embodiments, the one or more sensors 208 may provide image data to one or more processors 202 or another component communicatively coupled to the communication path 204. In some embodiments, the one or more sensors 208 may provide navigation support. In an embodiment, data captured by one or more sensors 208 may be used to navigate vehicle 100 autonomously or semi-autonomously.

[0035] In some embodiments, the one or more sensors 208 include one or more image sensors configured to operate in the visible and / or infrared spectrum to sense visible and / or infrared light. In some embodiments, the one or more sensors 208 include one or more LIDAR sensors, radar sensors, sonar sensors, or other types of sensors for collecting data that may be integrated with or supplement the data collection. A ranging sensor, such as a radar sensor, may be used to obtain rough depth and speed information regarding the field of view of the vehicle system 200.

[0036] Vehicle system 200 includes a satellite antenna 214 coupled to communication path 204 such that communication path 204 communicatively couples satellite antenna 214 to other modules of vehicle system 200. Satellite antenna 214 is configured to receive signals from Global Positioning System satellites. In one embodiment, satellite antenna 214 includes one or more conductive elements that interact with electromagnetic signals transmitted by Global Positioning System satellites. The received signals are converted by one or more processors 202 into data signals indicative of the location (e.g., latitude and longitude) of satellite antenna 214 or an object positioned near satellite antenna 214.

[0037] The vehicle system 200 includes one or more vehicle sensors 212. Each of the one or more vehicle sensors 212 is coupled to the communication path 204 and communicatively coupled to the one or more processors 202. The one or more vehicle sensors 212 include one or more motion sensors for detecting and measuring movement and changes in movement of the vehicle 100. The motion sensors may include an inertial measurement unit. Each of the one or more motion sensors may include one or more accelerometers and one or more gyroscopes. Each of the one or more motion sensors converts a sensed physical movement of the vehicle into a signal indicative of the vehicle's direction, rotation, speed, or acceleration. Based on the signal indicative of the vehicle's direction, rotation, speed, or acceleration, the repetitive motion storage module 207 can identify a repetitive motion pattern of the vehicle.

[0038] 2, the vehicle system 200 includes network interface hardware 216 for communicatively coupling the vehicle system 200 to another object system 220. The network interface hardware 216 is any device communicatively coupled to the communication path 204 and capable of transmitting and / or receiving data over a network. The network interface hardware 216 may include a communication transceiver for transmitting and / or receiving any wired or wireless communications. For example, the network interface hardware 216 includes an antenna, a modem, a LAN port, a WiFi card, a WiMAX card, mobile communication hardware, near field communication hardware, satellite communication hardware, and / or any wired or wireless hardware for communicating with other networks and / or devices. In one embodiment, the network interface hardware 216 includes hardware configured to operate according to the Bluetooth® wireless communication protocol. The network interface hardware 216 of the vehicle system 200 transmits its data to another object system 220. For example, the network interface hardware 216 of the vehicle system 200 may transmit vehicle data, position data, steering data, etc. to other objects, cloud servers, edge servers, etc.

[0039] The vehicle system 200 is connected with one or more external vehicle systems (e.g., another object system 220) and / or external processing devices (e.g., cloud servers or edge servers) via a direct connection. The direct connection may be vehicle-to-vehicle ("V2V connection"), vehicle-to-everything ("V2X connection"), or mmWave connection. The V2V, V2X, or mmWave connection is established using any suitable wireless communication protocol as discussed above. The vehicle-to-vehicle connection may utilize time-based and / or location-based sessions. In an embodiment, the connection between the vehicles or between the vehicles and infrastructure elements may utilize one or more networks for the connection. The network may be in lieu of or in addition to a direct connection between the vehicles or between the vehicles and infrastructure (e.g., V2V, V2X, mmWave, etc.). The vehicle system 200 may communicate with the external communication vehicle systems using wireless messages such as Basic Safety Messages (BSM), Steering Messages (MM), etc. BSMs are wireless messages transmitted between the vehicles. In vehicles, transmitters transmit vehicle position, speed, and other static and dynamic information. MM is a general class of radio messages exchanged between road users and infrastructure that contain the future trajectory (or possible future trajectory) of the sending road user. Examples of such messages are Maneuvering Coordination Messages (MCM) or Maneuvering Shared Coordination Messages (MSCM).

[0040] Vehicles can act as infrastructure nodes to form mesh networks and dynamically connect on an ad-hoc basis. In this manner, vehicles can freely enter and / or leave the network, causing the mesh network to self-organize and self-modify over time. The network may include vehicles forming peer-to-peer networks with other vehicles or utilizing centralized networks that rely on specific vehicles and / or infrastructure elements. The network may include networks that use centralized servers and other central computing devices to store and / or relay information between vehicles.

[0041] 2, the vehicle system 200 is communicatively coupled to another object system 220 or server 240 by a network 270. In one embodiment, the network 270 includes one or more computer networks (e.g., personal area networks, local area networks, or wide area networks), cellular networks, satellite networks, and / or global positioning systems, and combinations thereof. The vehicle system 200 is communicatively coupled to the network 270 via a wide area network, a local area network, a personal area network, a cellular network, a satellite network, and the like. Suitable local area networks include wired Ethernet and / or wireless technologies such as Wi-Fi. Suitable personal area networks include wireless technologies such as IrDA, Bluetooth, Wireless USB, Z-Wave, ZigBee, and / or other short-range wireless communication protocols. Suitable cellular networks include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM.

[0042] With further reference to FIG. 2 , another object system 220 includes one or more processors 222, one or more memory modules 226, one or more sensors 228, one or more vehicle sensors 232, a satellite antenna 234, network interface hardware 236, and a communication path 224 communicatively connected to other components of the another object system 220. The components of the another object system 220 are structurally similar and have similar functionality as the corresponding components of the vehicle system 200 (e.g., the one or more processors 222 correspond to the one or more processors 202, the one or more memory modules 226 correspond to the one or more memory modules 206, the one or more sensors 228 correspond to the one or more sensors 208, the one or more vehicle sensors 232 correspond to the one or more vehicle sensors 212, the satellite antenna 234 corresponds to the satellite antenna 214, the communication path 224 corresponds to the communication path 204, and the network interface hardware 236 corresponds to the network interface hardware 216). The one or more memory modules 226 store an iterative action storage module and a digital twin simulation module similar to the iterative action storage module 207 and the digital twin simulation module 209 of the vehicle system 200.

[0043] 2 , server 240 includes one or more processors 244, one or more memory modules 246, network interface hardware 248, one or more vehicle sensors 250, and a communication path 242 communicatively connected to other components of vehicle system 200 and / or other components of another object system 220. The components of server 240 are structurally similar and have similar functionality as corresponding components of another object system 220 (e.g., one or more processors 244 correspond to one or more processors 222, one or more memory modules 246 correspond to one or more memory modules 226, one or more vehicle sensors 250 correspond to one or more vehicle sensors 232, communication path 242 corresponds to communication path 224, and network interface hardware 248 corresponds to network interface hardware 236).

[0044] It should be understood that the components illustrated in Figure 2 are merely exemplary and are not intended to limit the scope of the present disclosure. More specifically, while the components in Figure 2 are illustrated as residing within vehicle system 200, this is a non-limiting example. In some embodiments, one or more of the components may be external to vehicle system 200, such as server 240.

[0045] Referring now to FIG. 3, there is shown a flow chart of a method 300 that may be implemented by the vehicle 100, another object 110, server 140, or a combination thereof of FIGS. 1A-1E.

[0046] In step 310, the controller identifies a repetitive motion pattern of the driver of the vehicle at a predetermined location. In some embodiments, the controller identifies a repetitive motion pattern of the autonomous driver of the vehicle at a predetermined location. The controller is a controller of the vehicle or a controller of a server. The controller accumulates driving data of the vehicle and identifies the repetitive motion pattern of the driver of the vehicle based on the accumulated driving data. With reference to FIG. 1A, the repetitive motion pattern 102 is associated with a parking motion pattern, and the predetermined location includes a parking spot 104 or a home address. The repetitive motion pattern 102 includes turning on a left turn signal, turning right to line up, and backing into the parking spot 104. For example, with reference to FIG. 4A, the repetitive motion pattern 402 is associated with a turning motion pattern, such as a U-turn motion pattern, and the predetermined location includes a turning spot, such as a U-turn spot. The repetitive motion pattern 402 includes turning on a left turn signal, turning right to secure more space, and making a U-turn. For example, referring to Figure 5A, the repetitive motion pattern 502 is associated with a parking motion pattern and the predetermined location includes a merging lane. The repetitive motion pattern 502 may include parking in a merging lane.

[0047] Referring again to FIG. 3, in step 320, in response to identifying a vehicle in proximity to a predetermined location, the controller performs a digital twin simulation of the vehicle using a repetitive motion pattern and a digital twin simulation of another object to identify a potential collision between the vehicle and the other object. The controller performs the digital twin simulation of the vehicle in real time or at the end of the day when the controller is not busy. The controller then infers a coaching guidance, such as a predetermined driving behavior, for the driver of the vehicle. For example, referring to FIG. 1B-FIG. 1C, when the vehicle 100 detects another object 110 within a predetermined distance from the vehicle 100, i.e., when the vehicle 100 is in proximity to the parking spot 104 and the other object 110 is detected nearby, the vehicle 100 performs a digital twin simulation of the vehicle 100 using a repetitive motion pattern 102 and a digital twin simulation of the other object 110 to identify this potential collision 112 between the vehicle 100 and the other object 110. As another example, when the vehicle 100 turns on its left turn signal and approaches a predetermined distance, the vehicle 100 may receive an indication that the vehicle's turn signal is on and then perform a digital twin simulation of the vehicle 100 using the repetitive motion pattern 102 and a digital twin simulation of another object 110 to identify this potential collision 112 between the vehicle 100 and the other object 110.

[0048] 4B-4C, in some embodiments, when the vehicle 400 detects another object 410 within a predetermined distance from the vehicle 400, i.e., when the vehicle 400 approaches a U-turn point and the other object 410 is detected nearby, the vehicle 400 performs a digital twin simulation of the vehicle 400 using the repetitive motion pattern 402 and a digital twin simulation of the other object 410 to identify this potential collision 412 between the vehicle 400 and the other object 410. As another example, when the vehicle 400 turns on the left turn signal for a U-turn, the vehicle 400 may receive an indication that the vehicle's turn signal is turned on, and then perform a digital twin simulation of the vehicle 400 using the repetitive motion pattern 402 and a digital twin simulation of the other object 410 to identify this potential collision 412 between the vehicle 400 and the other object 410.

[0049] In some embodiments, referring to Figures 5B-5C, when the vehicle 500 detects a first other object 510 within a predetermined distance from the vehicle 500, i.e., when the vehicle 500 approaches a merging lane for parking and the first other object 510 and a second other object 520 are detected nearby, the vehicle 500 performs a digital twin simulation of the vehicle 500 using a repetitive motion pattern 502 and a digital twin simulation of the first other object 510 and the second other object 520 to identify this potential collision 512 between the vehicle 500 and the first other object 510. As another example, when the vehicle 500 turns on its left turn signal, right turn signal, or emergency lights to park in a merging lane, the vehicle 500 may receive an indication that the vehicle's turn signals or emergency lights are on and then perform a digital twin simulation of the vehicle 500 using the repetitive motion pattern 502 and a digital twin simulation of the first separate object 510 and the second separate object 520 to identify this potential collision 512 between the vehicle 500 and the first separate object 510.

[0050] Referring again to FIG. 3, in step 330, the controller notifies the driver of the vehicle of a predetermined driving behavior in response to identifying a potential collision. The predetermined driving behavior refers to a driving behavior that may prevent a potential collision between the vehicle and another object. In an embodiment, the controller instructs a display of an output device of the vehicle, a display of a device of the driver, or both, to display the predetermined driving behavior. In an embodiment, the output device of the vehicle, the device of the driver, or both, includes a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In an embodiment, the controller may instruct a display of the output device of the vehicle to display a result that the digital twin simulation of the vehicle and another object indicates a potential collision. In an embodiment, the controller may instruct a device of the other object to output a warning. For example, referring to FIGS. 1C-1E, if there is a potential collision 112 between the vehicle 100 and another object 110, the controller instructs an output device of the vehicle 100 to guide the driver of the vehicle 100 to park the vehicle 100 facing forward. Specifically, the output device of the vehicle 100 may display a predetermined driving behavior, i.e., forward parking, as shown in FIG 1D. As another example, if there is a potential collision 112 between the vehicle 100 and another object 110, the controller instructs the output device of the vehicle 100 to guide the driver of the vehicle 100 to reverse park the vehicle 100 without veering right. Specifically, the output device of the vehicle 100 displays a predetermined driving behavior, i.e., reverse parking without veering right, as shown in FIG 1E.

[0051] 4C-4D, in some embodiments, when there is a potential collision 412 between the vehicle 400 and another object 410, the controller instructs the output device of the vehicle 400 to guide the driver of the vehicle 400 to keep the vehicle 400 straight, turn the vehicle 400 without crossing into an adjacent lane, and complete a U-turn without swerving, as shown in FIG. 4D. Specifically, the output device of the vehicle 400 displays a predetermined driving behavior, i.e., keep the vehicle 400 straight, turn the vehicle 400 without crossing into an adjacent lane, and complete a U-turn without swerving, as shown in FIG.

[0052] 5C-5D, for example, when there is a potential collision 512 between the vehicle 500 and a first other object 510, the controller instructs the output device of the vehicle 500 to guide the driver of the vehicle 500 not to park in the merging lane. Specifically, the output device of the vehicle 500 displays a predetermined driving behavior, i.e., not to park in the merging lane, as shown in FIG. 5D.

[0053] 4A, 4B, 4C, 4D, 4E and 4F, an exemplary embodiment is depicted for modifying the driving behavior of a vehicle driver when a repetitive motion pattern is associated with a U-turn motion pattern.

[0054] FIG. 4A illustrates collecting a repetitive motion pattern of a U-turn of a vehicle in the real world according to one embodiment illustrated and described herein. Referring to FIG. 4A, a vehicle 400 makes a U-turn using a repetitive motion pattern 402. The vehicle 400 turns on a left turn signal and then veers right to obtain more space to make a U-turn. The vehicle 400 then makes a U-turn. In an embodiment, if the repetitive motion pattern 402 is associated with a U-turn motion pattern, the repetitive motion pattern 402 includes turning on a left turn signal, veering right to obtain more space, and making a U-turn. The driver of the vehicle 400 believes that the repetitive motion pattern 402 is correct or legal. In an embodiment, a server 440 collects, receives, or both performs the repetitive motion pattern 402 of the vehicle 400. The server 440 is communicatively coupled to the vehicle 400 via a network 470.

[0055] 4B illustrates a vehicle about to initiate a repetitive U-turn motion pattern in the real world, according to one embodiment illustrated and described herein. With reference to FIG. 4B, another object 410 may pass in a lane next to the vehicle 400. In an embodiment, the another object 410 is another vehicle. The vehicle 400 detects the another object 410 within a predetermined distance from the vehicle 400. The predetermined distance from the vehicle 400 may depend on the speed of the vehicle 400, the size of the vehicle 400, the speed of the another object 410, the size of the another object 410, or a combination thereof.

[0056] In this example, when the vehicle 400 repeats the repetitive motion pattern of a U-turn, the repeated motions may cause a potential collision with another object 410. Specifically, when the vehicle 400 turns on the left turn signal to make a U-turn, the other object 410 may expect the vehicle 400 to turn left and pass through the adjacent lane. At the same time, the vehicle 400 may veer to the right to secure more space to make a U-turn. These motions may cause a potential collision 412 between the vehicle 400 and the other object 410.

[0057] Therefore, before the vehicle 400 starts the repetitive motion pattern 402 of the U-turn, the vehicle 400 performs a digital twin simulation to check for potential collisions, as shown in FIG. 4C, and takes preventive measures, as shown in FIG. 4D. In some embodiments, the controller infers where the driver of the vehicle 400 needs assistance. For example, if the vehicle 400 repeats the repetitive motion pattern of the U-turn, the controller analyzes data of the repetitive motion pattern of the U-turn and recognizes that the driver of the vehicle 400 may have some problems with the U-turn. When the vehicle 400 next approaches the U-turn point, the driver of the vehicle 400 is assisted by the controller.

[0058] 4C illustrates a digital twin simulation of the vehicle and another vehicle according to one embodiment depicted and described herein. Referring to FIG. 4C, the vehicle 400 performs a digital twin simulation of the vehicle 400 and a digital twin simulation of the other object 410 to identify this potential collision 412 between the vehicle 400 and the other object 410. In some embodiments, a server 440 may perform a digital twin simulation of the vehicle 400 using the repetitive motion pattern 402 and a digital twin simulation of the other object 410.

[0059] 4B , when the vehicle 400 detects another object 410 within a predetermined distance from the vehicle 400, the vehicle 400 performs a digital twin simulation of the vehicle 400 using the repetitive motion pattern 402 and a digital twin simulation of the other vehicle 410 in response to identifying the vehicle 400 in proximity to the predetermined location and detecting the other object 410 within the predetermined distance from the vehicle 400 to identify a potential collision 412 between the vehicle 400 and the other object 410. Specifically, in the simulation, the vehicle 400 follows the repetitive motion pattern 402 and the other object 410 follows a path 414. A collision 412 then occurs between the repetitive motion pattern 402 and the path 414.

[0060] 4D-4F, after the simulation, the real-world vehicle 400, in response to identifying the potential collision 412, notifies the driver of the vehicle 400 of a predetermined driving behavior that may prevent the potential collision 412. In one embodiment, the vehicle 400 displays the predetermined driving behavior. For example, a screen of the vehicle 400 displays the predetermined driving behavior. For example, the screen of the vehicle 400 may display the predetermined driving behavior. In some embodiments, the predetermined driving behavior may be displayed on a device other than the device of the vehicle 400, for example, a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In an embodiment, the vehicle 400 displays the results of a digital twin simulation of the vehicle 400 and another object 410, for example, the digital twin simulation shown in FIG. 4C, indicating the potential collision 412.

[0061] In an embodiment, the vehicle 400 or the server 240 instructs a device of the other object 410 to output a warning. In an embodiment, the device of the other object 410 includes a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In an embodiment, the vehicle 400 or the server 240 instructs a device of the other object 410 to output a warning with the result that a digital twin simulation of the vehicle 400 and the other object 410 indicates a potential collision 412.

[0062] 4D-4F, if there is a potential collision 412 between the vehicle 400 and another object 410, the vehicle 400 instructs the driver of the vehicle 400 to keep the vehicle 400 straight, steer the vehicle 400 without crossing into an adjacent lane, and complete a U-turn without swerving. In an embodiment, an output device of the vehicle 400 may display results that a digital twin simulation of the vehicle 400 and another object 410 indicates the potential collision 412 shown in FIG. 4C.

[0063] 4D , if there is no potential collision 412 between the vehicle 400 and another object, the vehicle 400 may not instruct the driver of the vehicle 400 to keep the vehicle 400 straight and steer the vehicle 400 without crossing into an adjacent lane to complete the U-turn without swerving, and the driver of the vehicle 400 may make the U-turn with the driver's driving behavior described in FIG 4A . In some embodiments, the display of an output device of the vehicle 400 displays the results of a digital twin simulation of the vehicle 400 and another object 410 indicating the potential collision 412 shown in FIG 4C .

[0064] 5A, 5B, 5C and 5D, an exemplary embodiment is depicted for modifying the driving behavior of a vehicle driver when a repetitive motion pattern is associated with a parking motion pattern.

[0065] FIG. 5A illustrates collecting a repetitive motion pattern of parking a vehicle in the real world, according to one embodiment illustrated and described herein. Referring to FIG. 5A, a vehicle 500 parks using a repetitive motion pattern 502. The vehicle 500 parks in a particular location. The particular location is a merging lane. In an embodiment, when the repetitive motion pattern 502 is related to a parking motion pattern, the repetitive motion pattern 502 includes parking in a merging lane. The driver of the vehicle 500 believes that the repetitive motion pattern 502 is correct or legal. In an embodiment, a server 540 collects, receives, or both performs the repetitive motion pattern 502 of the vehicle 500. The server 540 is communicatively coupled to the vehicle 500 via a network 570.

[0066] FIG. 5B illustrates a vehicle about to begin a repetitive parking pattern in the real world, according to one embodiment illustrated and described herein. Referring to FIG. 5B, a first other object 510 and a second other object 520 make a left turn into the lane of the vehicle 500 and into a lane next to the vehicle 500. In an embodiment, each of the first other object 510 and the second other object 520 is a different vehicle. The vehicle 500 detects at least one of the first other object 510 and the second other object 520 within a predetermined distance from the vehicle 500. The predetermined distance from the vehicle 500 may depend on the speed of the vehicle 500, the size of the vehicle 500, the speed of the first other object 510, the size of the first other object 510, the speed of the second other object 520, the size of the second other object 520, or a combination thereof.

[0067] In this example, when the vehicle 500 repeats a repetitive parking motion pattern, the repeated motions may cause a potential collision with the first other object 510. Specifically, when the vehicle 500 parks in a merging lane, the first other object 510 and the second other object 520 may simultaneously make a left turn. These motions may cause a potential collision 512 between the vehicle 500 and the first other object 510.

[0068] Therefore, before the vehicle 500 begins the parking repetitive motion pattern 502, the vehicle 500 performs a digital twin simulation to check for potential collisions, as shown in FIG. 5C, and takes preventative measures, as shown in FIG. 5D.

[0069] 5C illustrates a digital twin simulation of the vehicle, the first other vehicle, and the second other vehicle according to one embodiment illustrated and described herein. Referring to FIG. 5C, the vehicle 500 performs a digital twin simulation of the vehicle 500 and a digital twin simulation of the first other object 510 and the second other object 520 to identify this potential collision 512 between the vehicle 500 and the first other object 510. In some embodiments, a server 540 may perform a digital twin simulation of the vehicle 500 using a repetitive motion pattern 502 and a digital twin simulation of the first other object 510.

[0070] In an embodiment, as shown in FIG. 5B, when the vehicle 500 detects a first other object 510 and a second other object 520 within a predetermined distance from the vehicle 500, the vehicle 500 performs a digital twin simulation of the vehicle 500 using a repetitive motion pattern 502 and a digital twin simulation of the first other object 510 and the second other object 520 in response to identifying the vehicle 500 in the vicinity of the predetermined location and detecting the other object 510 within the predetermined distance from the vehicle 500 to identify a potential collision 512 between the vehicle 500 and the first other object 510. Specifically, in the simulation, the vehicle 500 follows the repetitive motion pattern 502 and the first other object 510 and the second other object 520 follow a path 514. Then, a collision 512 occurs between the repetitive motion pattern 502 and the path 514.

[0071] 5D, after the simulation, the real-world vehicle 500, in response to identifying the potential collision 512, notifies the driver of the vehicle 500 of a predetermined driving behavior that may prevent the potential collision 512. In an embodiment, the vehicle 500 displays the predetermined driving behavior. For example, a screen of the vehicle 500 may display the predetermined driving behavior. In some embodiments, the predetermined driving behavior may be displayed on a device other than the device of the vehicle 500, such as a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In an embodiment, the vehicle 500 may display a result of a digital twin simulation of the vehicle 500, the first other object 510, and the second other object 520, such as the digital twin simulation shown in FIG. 5C, indicating the potential collision 512.

[0072] In an embodiment, the vehicle 500 or the server 540 instructs a device of the first other object 510 to output a warning. In an embodiment, the device of the first other object 510 includes a smartphone, a smart watch, a laptop computer, a tablet computer, a personal computer, and a wearable device, or a combination thereof. In an embodiment, the vehicle 500 or the server 540 instructs a device of the first other object 510 to output a warning with the result that the digital twin simulation of the vehicle 500, the first other object 510, and the second other object 520 indicates a potential collision 512.

[0073] 5D, if there is a potential collision 512 between the vehicle 500 and the first other object 510, the vehicle 500 instructs the driver of the vehicle 500 not to park in the merging lane. In an embodiment, an output device of the vehicle 500 may display results that a digital twin simulation of the vehicle 500, the first other object 510, and the second other object 520 indicates the potential collision 512 shown in FIG.

[0074] 5D , if there is no potential collision 512 between the vehicle 500 and the other object, the vehicle 500 may not instruct the driver of the vehicle 500 not to park in the merging lane, and the driver of the vehicle 500 may park with the driver's driving behavior described in FIG 5A . In an embodiment, the display of the output device of the vehicle 500 displays the results that a digital twin simulation of the vehicle 500, the first other object 510, and the second other object 520 indicates the potential collision 512 shown in FIG 5C .

[0075] It should be noted that for purposes of describing and defining this disclosure, reference herein to a variable that is a "function" of a parameter or another variable is not intended to mean that the variable is solely a function of the listed parameter or variable. Rather, reference herein to a variable that is a "function" of listed parameters is intended to be open-ended, such that the variable may be a function of a single parameter or multiple parameters.

[0076] It should be noted that descriptions herein of components of the present disclosure being "configured" or "programmed" in a particular way to embody particular properties or to function in a particular manner are structural descriptions, as opposed to descriptions of intended use. More specifically, references herein to the manner in which a component is "configured" or "programmed" should be interpreted as indicating the existing physical state of the component and, as a result, as explicitly reciting the structural characteristics of the component.

[0077] It should be noted that terms such as "preferably," "generally," and "typically," when used herein, are not intended to limit the scope of the claimed invention or to imply that certain features are essential, essential, or even critical to the structure or function of the claimed invention. Rather, such terms are merely intended to identify particular aspects of embodiments of the present disclosure or to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present disclosure.

[0078] The order of execution or performance of operations in the examples of the disclosure illustrated and described herein is not essential, unless otherwise specified. That is, unless otherwise specified, operations may be performed in any order, and examples of the disclosure may include more or fewer operations than those disclosed herein. For example, performing or performing a particular operation before, contemporaneously with, or after another operation is considered to be within the scope of aspects of the disclosure.

[0079] Although the subject matter of the present disclosure has been described in detail with reference to certain embodiments thereof, it should be noted that various details disclosed herein should not be interpreted as meaning that such details relate to elements that are essential components of the various embodiments described herein, even if a particular element is shown in each drawing accompanying this description. Moreover, it will be apparent that modifications and variations are possible without departing from the scope of the present disclosure, including, but not limited to, the embodiments defined in the appended claims. More specifically, although certain aspects of the present disclosure are identified herein as preferred or particularly advantageous aspects, it is believed that the present disclosure is not necessarily limited to such aspects. The present disclosure includes the following aspects. (1) identifying repetitive movement patterns of a driver of the vehicle at a given location based on accumulated driving data of the vehicle; conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of another object to identify potential collisions between the vehicle and the other object; A system comprising a controller programmed to notify an operator of the vehicle of a predetermined driving behavior in response to identifying the potential crash. (2) The controller: The system described in (1) above, further programmed to, in response to identifying the vehicle in proximity to the predetermined location, perform a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object. (3) The controller: receiving an indication that a turn signal of the vehicle is illuminated; The system of (1) above, further programmed to, in response to receiving an indication that the vehicle's turn signal is on, perform a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object. (4) The system described in (1) above, wherein the repetitive motion pattern is associated with a parking motion pattern and the predetermined location includes a parking spot. (5) The system described in (1) above, wherein the repetitive motion pattern is associated with a turning motion pattern and the predetermined location includes a turning point. (6) The system described in (5) above, wherein the turning operation pattern includes a U-turn operation pattern and the predetermined location includes a U-turn point. (7) The controller: The system of claim 1, further programmed to instruct a display of the vehicle's output device or a display of the driver's device to display the predetermined driving behavior. (8) The controller: The system of claim 1, further programmed to instruct a display of an output device of the vehicle to display results indicating that a digital twin simulation of the vehicle and the other object is indicative of the potential collision. (9) The controller: The system described in (1) above, further programmed to instruct a device of the other object to output a warning. (10) The controller: Detecting the other object within a predetermined distance from the vehicle; The system described in (1) above, further programmed to, in response to identifying the vehicle in proximity to the predetermined location and detecting the other object within the predetermined distance from the vehicle, perform a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify the potential collision between the vehicle and the other object. (11) 1. A method for modifying a driving behavior of a driver of a vehicle, comprising: identifying repetitive movement patterns of a driver of the vehicle at a given location based on accumulated driving data of the vehicle; conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of another object to identify potential collisions between the vehicle and the other object; In response to identifying the potential crash, notifying an operator of the vehicle of a predetermined driving behavior. A method comprising: (12) The method of claim 11, further comprising, in response to identifying the vehicle in proximity to the predetermined location, conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object. (13) receiving an indication that a turn signal of the vehicle is on; The method of claim 11, further comprising: in response to receiving an indication that a turn signal of the vehicle is on, conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object. (14) The method according to claim 11, wherein the repetitive movement pattern is associated with a parking movement pattern and the predetermined location includes a parking spot. (15) The method according to claim 11, wherein the repetitive motion pattern is associated with a turning motion pattern and the predetermined location includes a turning point. (16) The method according to (15) above, wherein the turning operation pattern includes a U-turn operation pattern, and the predetermined location includes a U-turn point. (17) The method according to claim 11, further comprising the step of instructing a display of an output device of the vehicle or a display of the driver's device to display the predetermined driving behavior. (18) 12. The method of claim 11, further comprising the step of instructing a display of an output device of the vehicle to display a result that the digital twin simulation of the vehicle and the other object is indicative of the potential collision. (19) The method according to claim 11, further comprising the step of instructing a device of the other object to output an alarm. (20) A vehicle, an actuator configured to move the vehicle; identifying repetitive movement patterns of a driver of the vehicle at a given location based on accumulated driving data of the vehicle; responsive to identifying the vehicle proximate to the predetermined location, conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of another object to identify a potential collision between the vehicle and the other object; a controller programmed to transmit a predetermined driving behavior to an output device in response to identifying the potential collision; Equipped with The output device is configured to output the predetermined driving behavior.

Claims

1. identifying a repetitive motion pattern repeated by a driver of the vehicle at a given location based on accumulated driving data of the vehicle; Conducting a digital twin simulation of the vehicle and a digital twin simulation of another object using the repetitive motion pattern, respectively; identifying a potential collision between the vehicle and the other object based on a path of the repetitive motion pattern obtained from a digital twin simulation of the vehicle and a path of the other object obtained from a digital twin simulation of the other object; A system comprising a controller programmed to notify an operator of the vehicle of a predetermined driving behavior in response to identifying the potential crash.

2. The controller:

2. The system of claim 1, further programmed to, in response to identifying the vehicle in proximity to the predetermined location, perform a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object.

3. The controller: receiving an indication that a turn signal of the vehicle is illuminated; 2. The system of claim 1, further programmed to, in response to receiving an indication that a turn signal of the vehicle is on, perform a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object.

4. The system of claim 1 , wherein the repetitive motion pattern is associated with a parking motion pattern and the predetermined location includes a parking spot.

5. The system of claim 1 , wherein the repetitive motion pattern is associated with a turning motion pattern and the predetermined location includes a turning point.

6. The system of claim 5 , wherein the turning motion pattern comprises a U-turn motion pattern, and the predetermined location comprises a U-turn point.

7. The controller: The system of claim 1 , further programmed to: prompt a display of the vehicle output device or a display of the driver's device to display the predetermined driving behavior.

8. The controller:

10. The system of claim 1, further programmed to: direct a display on an output device of the vehicle to display results of a digital twin simulation of the vehicle and the other object that are indicative of the potential collision.

9. The controller: The system of claim 1 , further programmed to instruct a device of the other object to output a warning.

10. The controller: Detecting the other object within a predetermined distance from the vehicle; 2. The system of claim 1, further programmed to, in response to identifying the vehicle in proximity to the predetermined location and detecting the other object within the predetermined distance from the vehicle, perform a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify the potential collision between the vehicle and the other object.

11. 1. A processor-executed method for modifying a driving behavior of a driver of a vehicle, comprising: identifying repetitive motion patterns repeated by a driver of the vehicle at a given location based on accumulated driving data of the vehicle; Conducting a digital twin simulation of the vehicle and a digital twin simulation of another object using the repetitive motion pattern, respectively; identifying a potential collision between the vehicle and the other object based on a path of the repetitive motion pattern obtained from a digital twin simulation of the vehicle and a path of the other object obtained from a digital twin simulation of the other object; In response to identifying the potential crash, notifying an operator of the vehicle of a predetermined driving behavior. A method comprising:

12. 12. The method of claim 11, further comprising, in response to identifying the vehicle proximate to the predetermined location, conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object.

13. receiving an indication that a turn signal of the vehicle is on; 12. The method of claim 11, further comprising: in response to receiving an indication that a turn signal of the vehicle is on, conducting a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of the other object to identify a potential collision between the vehicle and the other object.

14. The method of claim 11 , wherein the repetitive motion pattern is associated with a parking motion pattern, and the predetermined location includes a parking spot.

15. The method of claim 11 , wherein the repetitive motion pattern is associated with a turning motion pattern, and the predetermined location includes a turning point.

16. The method of claim 15 , wherein the turning motion pattern comprises a U-turn motion pattern and the predetermined location comprises a U-turn point.

17. The method of claim 11 , further comprising the step of: prompting a display of the vehicle output device or a display of the driver device to display the predetermined driving behavior.

18. 12. The method of claim 11, further comprising: instructing a display of an output device of the vehicle to display a result that the digital twin simulation of the vehicle and the other object is indicative of the potential collision.

19. The method of claim 11 , further comprising the step of instructing a device on the other object to output an alarm.

20. A vehicle, an actuator configured to move the vehicle; identifying repetitive motion patterns repeated by a driver of the vehicle at a given location based on accumulated driving data of the vehicle; responsive to identifying the vehicle proximate to the predetermined location, performing a digital twin simulation of the vehicle using the repetitive motion pattern and a digital twin simulation of another object, respectively; identifying a potential collision between the vehicle and the other object based on a path of the repetitive motion pattern obtained from a digital twin simulation of the vehicle and a path of the other object obtained from a digital twin simulation of the other object; a controller programmed to transmit a predetermined driving behavior to an output device in response to identifying the potential collision; Equipped with The output device is configured to output the predetermined driving behavior.

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