Detecting compromised vehicle and responding to detection of compromised vehicle

Abstract

To provide a method for detecting a compromised vehicle and responding to the detection of the compromised vehicle.SOLUTION: A method for detecting a compromised vehicle and responding to the detection of the compromised vehicle comprises: receiving one or more monitored inputs of a vehicle; predicting, using at least the one of the monitored inputs, a predicted vehicle trajectory; detecting a detected vehicle trajectory; comparing the predicted vehicle trajectory to the detected vehicle trajectory; determining a trajectory deviation value relating to the predicted vehicle trajectory and the detected vehicle trajectory; in response to determining that the trajectory deviation value exceeds a pre-determined trajectory deviation threshold, generating a response action; and implementing the response action.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure generally relates to the detection of a compromised vehicle and responses to the detection of a compromised vehicle, and more specifically, to a method, a system, and a non-transitory computer-readable medium that implement response measures for detecting a compromised vehicle and dealing with the compromised vehicle.

Background Art

[0002] Recent vehicles are increasingly connected to wireless networks and rely on software and electronic components with respect to the operation of the vehicle. Further, vehicles are increasingly incorporating autonomous driving functions and digitization of the vehicle driving process (for example, the acceleration of the vehicle is implemented by an electronic signal generated by pressing an accelerator pedal rather than a precise manual process of accelerating the engine). However, such connections and uses of software and electronic components pose a risk that the vehicle may become compromised (e.g., by a hacker or malicious hardware or software), and as a result, partial or complete control of the vehicle's system may be taken over by a malicious third party (e.g., "compromised"). In such a situation, the operation of the compromised vehicle may be partially or completely controlled by a third party (rather than, for example, the vehicle driver or the autonomous driving system). Further, the third party may conceal the compromised nature of the vehicle, and as a result, the vehicle driver and / or the vehicle security system may not notice or detect the change in control regarding the vehicle's system.

[0003] Accordingly, there is a need for a system, a method, and a non-transitory computer-readable medium that analyze a driver's behavior and a vehicle's vehicle trajectory to detect whether the vehicle is compromised and whether it is being controlled by a third party.

Summary of the Invention

[0004] According to the first aspect A1, a method for detecting an endangered vehicle and responding to the detection of the endangered vehicle may include receiving, by a computing device, one or more monitoring inputs of the vehicle; predicting, by the computing device, a predicted vehicle trajectory using at least one of the monitoring inputs; detecting, by a vehicle detection system, a detected vehicle trajectory; comparing, by the computing device, the predicted vehicle trajectory with the detected vehicle trajectory; determining, by the computing device, a trajectory deviation value regarding the predicted vehicle trajectory and the detected vehicle trajectory; generating, by the computing device, a response measure in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold; and implementing, by the computing device, the vehicle, or both, the response measure.

[0005] The second aspect A2 includes the method according to the first aspect A1, and the vehicle may include a motor vehicle.

[0006] The third aspect A3 includes the method according to the first aspect A1 or the second aspect A2, and the one or more monitoring inputs may be received from a vehicle detection system, a driver of the vehicle, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

[0007] The fourth aspect A4 includes the method according to any one of aspects A1 to A3, and the one or more monitoring inputs may include at least one of a pedal throttle pattern input, a brake pattern input, a linear acceleration input, a lateral acceleration input, a rotational smoothness input, a turning radius input, an operation mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or a combination thereof.

[0008] Aspect A5 includes the method according to any one of Aspects A1 to A4, the method further comprising at least one of the following: namely, comparing by a computing device at least one of the monitoring inputs, or the detected vehicle trajectory, or both, with a driver profile; determining by the computing device a driver profile deviation value for the driver profile and the detected vehicle trajectory or at least one of the monitoring inputs or both; and generating a response measure in response to determining by the computing device that the driver profile deviation value exceeds a profile deviation threshold.

[0009] Aspect A6 includes the method according to Aspect A5, wherein the driver profile can be generated from past inputs by the driver of the vehicle, the vehicle's autonomous driving system, or a combination thereof.

[0010] Aspect A7 includes the method according to any one of Aspects A1 to A6, wherein the response measure can comprise at least one of a security sweep, a connection cut-off, a warning to the driver of the vehicle, a warning to a third party, a vehicle cut-off, a cut-off of a vehicle component, a cut-off of a vehicle system, a null command, a command check, or a combination thereof.

[0011] According to Aspect B1, a system for detecting an endangered vehicle and responding to the detection of the endangered vehicle can comprise a vehicle detection system of the vehicle and a computing device having a memory component, the memory component storing logic which, when executed by the computing device, causes the system to at least: receive one or more monitoring inputs of the vehicle; predict a predicted vehicle trajectory using at least one of the monitoring inputs; detect a detected vehicle trajectory by the vehicle detection system; compare the predicted vehicle trajectory with the detected vehicle trajectory; determine a trajectory deviation value for the predicted vehicle trajectory and the detected vehicle trajectory; generate a response measure in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold; and implement the response measure using the computing device or the vehicle or both, the vehicle, or both.

[0012] Aspect B2 of the ninth aspect includes the system according to Aspect B1 of the eighth aspect, and the vehicle may include a motor vehicle.

[0013] Aspect B3 of the tenth aspect includes the system according to Aspect B1 of the eighth aspect or Aspect B2 of the ninth aspect, and one or more monitoring inputs may be received from a vehicle detection system, a driver of the vehicle, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

[0014] Aspect B4 of the eleventh aspect includes the system according to any one of Aspects B1 to B3, and one or more monitoring inputs may include at least one of a pedal throttle pattern input, a brake pattern input, a linear acceleration input, a lateral acceleration input, a rotational smoothness input, a turning radius input, an operation mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or a combination thereof.

[0015] Aspect B5 of the twelfth aspect includes the system according to any one of Aspects B1 to B4, and the system may further perform at least one of the following: comparing at least one of the detected vehicle trajectory or one or more monitoring inputs with a driver profile; determining a driver profile deviation value for the driver profile and at least one of the detected vehicle trajectory or one or more monitoring inputs; and generating a response measure when the driver profile deviation value exceeds a profile deviation threshold.

[0016] Aspect B6 of the thirteenth aspect includes the system according to Aspect B5, and the driver profile may be generated from past inputs by the driver of the vehicle, an autonomous driving system of the vehicle, or a combination thereof.

[0017] Aspect B7 of the 14th aspect includes the system according to any one of Aspects B1 to B6, and the response measures may include at least one of security sweep, connection interruption, warning to the vehicle driver, warning to a third party, vehicle interruption, interruption of vehicle components, interruption of vehicle systems, invalidation command, command check, or a combination thereof.

[0018] According to the 15th aspect C1, a non - transient computer - readable medium that detects an endangered vehicle and responds to the detection of the endangered vehicle may store logic, and when the logic is executed by a computing device, the computing device is caused to perform at least one of the following: receiving one or more monitoring inputs of the vehicle; predicting a predicted vehicle trajectory using at least one of the monitoring inputs; detecting a detected vehicle trajectory by a vehicle detection system; comparing the predicted vehicle trajectory with the detected vehicle trajectory; determining a trajectory deviation value regarding the predicted vehicle trajectory and the detected vehicle trajectory; generating a response measure in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold; and implementing the response measure using the computing device, the vehicle, or both.

[0019] Aspect C2 of the 16th aspect includes the non - transient computer - readable medium according to the 15th aspect C1, and the one or more monitoring inputs may be received from a vehicle detection system, a vehicle driver, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

[0020] Aspect C3 of the 17th aspect includes the non - transient computer - readable medium according to the 15th aspect C1 or the 16th aspect C2, and the one or more monitoring inputs may include at least one of a pedal throttle pattern input, a brake pattern input, a linear acceleration input, a lateral acceleration input, a rotational smoothness input, a turning radius input, an operation mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or a combination thereof.

[0021] Aspect C4 of the 18th aspect includes a non-transitory computer-readable medium of any one of aspects C1 to C3, and the system further performs at least one of the following: comparing at least one of the detected vehicle trajectory or the monitoring input, or both, with the driver profile; determining a driver profile deviation value regarding the driver profile and at least one of the detected vehicle trajectory or the monitoring input, or both; and generating a response measure in response to determining that the driver profile deviation value exceeds a profile deviation threshold.

[0022] Aspect C5 of the 19th aspect includes the non-transitory computer-readable medium of aspect C4 of the 18th aspect, and the driver profile can be generated from past inputs by the driver of the vehicle, the vehicle's autonomous driving system, or a combination thereof.

[0023] Aspect C6 of the 20th aspect includes a non-transitory computer-readable medium of any one of aspects C1 to C5, and the response measure can include at least one of a vehicle security sweep, interruption of the vehicle connection, warning to the vehicle driver, warning to a third party, interruption of the vehicle, interruption of vehicle components, interruption of the vehicle system, invalid command, command check, or a combination thereof.

[0024] Aspect C7 of the 21st aspect includes a non-transitory computer-readable medium of any one of aspects C1 to C6, and the vehicle can be a motor vehicle.

[0025] Additional features and advantages of the aspects described in this specification are described in the following detailed description, and will be partly readily apparent to those skilled in the art from the description, or will be recognized by implementing the aspects described in this specification, including the following detailed description, claims, and accompanying drawings.

[0026] It should be understood that both the foregoing general description and the following detailed description describe various aspects and are intended to provide an overview or framework for understanding the nature and characteristics of the claimed subject matter. The accompanying drawings, which are incorporated herein and constitute a part of this specification, are included to provide a further understanding of the various aspects. The drawings illustrate the various aspects described herein and, together with the description, serve to explain the principles and operations of the claimed subject matter.

Brief Description of the Drawings

[0027] The embodiments described in the drawings are essentially exemplary and illustrative and are not intended to limit the subject matter defined by the claims. The following detailed description of the exemplary embodiments can be understood when read in conjunction with the following drawings, and similar structures are indicated using the same reference numerals.

[0028]

Figure 1

Figure 2

Figure 3A

Figure 3B

Figure 4

Figure 5A

Figure 5B

Figure 5C

Figure 5D

Figure 6

Figure 7

[0029] For example, due to a vehicle's connection to a wireless network, reliance on software and electronic components, and the introduction of an autonomous driving system in a vehicle system, a vehicle (and / or a system and / or their components) can be endangered, for example, by malicious third parties or software. When a vehicle is endangered, nevertheless, the vehicle can still operate normally or appear to be operating normally, even if an intervening malicious third party or software dominates the vehicle (and / or the system and / or their components). A vehicle may be equipped with a security system to prevent the endangerment of the vehicle. Nevertheless, since the security system may have vulnerabilities that allow a malicious third party or software to endanger the vehicle (and / or the system and / or their components), a mechanism for actually determining whether the vehicle is endangered and generating response measures accordingly can provide a safety mechanism against the risk of the vehicle being in an endangered state.

[0030] It should be understood that the "components" of the vehicle described in this specification are physical devices, sensors, apparatuses, and / or any other functional parts related to the vehicle. For example, in an embodiment, the components of the vehicle may include the vehicle's accelerator pedal, brake pedal, brakes, engine, steering wheel, wheels, air conditioning and / or heating unit, front windshield wiper, interior and / or exterior lighting, manual controls for other components (e.g., knobs, buttons, and / or sticks), vehicle user displays, and / or any other functional physical parts related to the vehicle.

[0031] It should be understood that the "systems" of the vehicle described in this specification are digital infrastructure, physical infrastructure, and / or combinations thereof that provide one or more functions to the vehicle 200. For example, in an embodiment, the systems of the vehicle may include a vehicle security system, a vehicle autonomous driving system, a digital user interface, vehicle software infrastructure, and / or combinations of components that provide specified functions (e.g., an air conditioning system that includes manual controls or interfaces, an air conditioning unit, and / or other software and / or hardware that provides an air conditioning function).

[0032] It should be understood that the term "compromised vehicle" as used herein refers to a vehicle that is partially or fully controlled by a third party (e.g., a hacker), rather than by the vehicle's driver or the vehicle's autonomous driving system. That is, a vehicle that is partially or fully controlled by a third party without any, some, or all of the requests or intentions of the vehicle's driver and / or the vehicle's autonomous driving system.

[0033] When a vehicle is in a critical state, the critical nature of the vehicle may not be immediately apparent to the vehicle's driver or security system. For example, a malicious third party or software may nevertheless enable the vehicle to function normally or reproduce the normal functions of the vehicle (e.g., by reproducing electrical signals generated by components of the vehicle, such as signals generated by an accelerator pedal when pressed by a driver), providing the illusion that the vehicle is not in a critical state. Further, with the increasing development and use of autonomous driving systems, the critical nature of the vehicle may be even more difficult for the driver to discern, as it may not be apparent to the driver that a malicious third party or software, rather than the vehicle's autonomous driving system, is controlling the operation of the vehicle.

[0034] It should be understood that the term "autonomous driving system" as used herein refers to software, hardware, or any combination thereof that enables a vehicle to be driven without the complete or partial control of a human driver. That is, an autonomous driving system is a collection of software, hardware, or any combination thereof that enables the partial or complete control of the operation of a vehicle to be managed by a system rather than a driver, such as to assist the driver in driving the vehicle or to take over the complete or partial driving of the vehicle.

[0035] Accordingly, the embodiments described herein may provide a method, system, and non-transitory computer-readable medium that enable the identification of the critical nature of a vehicle even when the vehicle's security system is unable to prevent the vehicle from becoming critical and / or unable to identify that the vehicle has become critical.

[0036] Reference is now made in detail to various embodiments of a method, a system, and a non-transitory computer-readable medium for detecting an endangered vehicle and implementing response measures for responding to the detection of the endangered vehicle, in particular for detecting the endangered vehicle by a monitoring input and / or a detection trajectory of the endangered vehicle and for dealing with the endangered vehicle. Specifically, in an embodiment, the method described herein includes receiving one or more monitoring inputs of a vehicle, predicting a predicted vehicle trajectory using at least one of the monitoring inputs, detecting a detected vehicle trajectory, comparing the predicted vehicle trajectory with the detected vehicle trajectory, determining a trajectory deviation value for the predicted vehicle trajectory and the detected vehicle trajectory, generating a response measure in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold, and implementing the response measure.

[0037] Ranges may be expressed herein as from about a particular value and / or to about another particular value. When such a range is expressed, another embodiment includes from the particular value and / or to the other particular value. Similarly, when values are expressed as approximations by use of the antecedent "about", it will be understood that the particular value forms another embodiment. It will further be understood that each of the endpoints of a range is significant both in relation to the other endpoint and independently of the other endpoint.

[0038] As used herein, directional terms such as above, below, right, left, front, rear, top, bottom are made only with reference to the figures as drawn and are not intended to imply absolute orientation.

[0039] Unless otherwise expressly stated, it is not intended that any method described herein be construed as requiring that the steps of the method be performed in a particular order, nor that any apparatus be construed as requiring a particular orientation. Thus, where a method claim does not actually recite the order in which its steps are to be followed, or an apparatus claim does not actually recite an order or orientation of individual components, and where the steps are not particularly specifically stated in the claims or specification as being limited to a particular order, or where no particular order or orientation of the components of the apparatus is recited, it is not intended in any way that an order or orientation be inferred. This applies to any possible implicit basis for interpretation, including logical matters regarding the arrangement of steps, the flow of operations, the order of components, or the orientation of components, the plain meaning derived from grammatical construction or punctuation, and the number or type of embodiments described in the specification.

[0040] As used herein, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a" component includes aspects having two or more such components unless the context clearly indicates otherwise.

[0041] Here, referring to FIGS. 1-2, a method 100 for responding to the detection of an endangered vehicle includes a block 110 that receives one or more monitoring inputs of a vehicle 200. In the embodiment of FIG. 2, the vehicle 200 is being driven in a lane 213 of a road 210. In the embodiment of FIG. 2, the vehicle 200 is driven by a driver 202, although in other embodiments, the vehicle 200 may be driven by an autonomous driving system of the vehicle 200 (e.g., an autonomous driving system 330G of the vehicle 200 as described in further detail below with reference to FIG. 6). In an embodiment, one or more monitoring inputs of the vehicle 200 may include inputs from the driver 202, an autonomous driving system of the vehicle 200 (e.g., an autonomous driving system 330G of the vehicle 200 as described in further detail below with reference to FIG. 6), a vehicle computing device of the vehicle 200 (e.g., a vehicle computing device 310 as described in further detail below with reference to FIG. 3A), a remote computing device (e.g., a remote computing device 350 as described in further detail below with reference to FIG. 3B), and / or any combination thereof. In an embodiment, one or more monitoring inputs of the vehicle 200 may include inputs from any system and / or component of the vehicle 200 that controls or otherwise affects the operation of the vehicle 200 or the operation of a component or system of the vehicle 200. In an embodiment, the vehicle 200 may be an automobile. In other embodiments, the vehicle 200 may instead be, for example, a truck, a machine, a boat or other watercraft, an airplane, a helicopter, and / or any other type of vehicle.

[0042] In an embodiment, any, some, or all of the at least one monitoring input may include a single input (e.g., a change in the gear mode of the vehicle 200) or multiple inputs (e.g., some instances of braking by the driver 202 regarding a braking pattern) by a user of the vehicle 200 (e.g., the driver 202 of the vehicle 200 and / or the autonomous driving system) that command a change related to the nature, operation, and / or other aspects of the systems, components, and / or functions of the vehicle 200, or otherwise affect and / or determine it. In an embodiment, the monitoring input may include commands (e.g., from the driver 202 and / or the autonomous driving system of the vehicle 200), as well as and / or data including the observed behavior of the vehicle 200 regarding one or more of a pedal throttle pattern, a braking pattern, linear acceleration, lateral acceleration, rotational smoothness, turning radius, operation mode, engine mode, gear mode, user interface vehicle system, any other nature, operation, and / or other aspects of the systems, components, and / or functions of the vehicle 200, or combinations thereof. Thus, in an embodiment, the one or more monitoring inputs may comprise at least one of a pedal throttle pattern input, a braking pattern input, a linear acceleration input, a lateral acceleration input, a rotational smoothness input, a turning radius input, an operation mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or combinations thereof.

[0043] In an embodiment, the pedal throttle pattern may include the speed, degree, and / or frequency at which a pedal (e.g., a brake pedal, an accelerator pedal, or a gear shift pedal) is pushed and / or released, and / or other data related to the instantaneous and / or time-dependent (e.g., by the driver 202 of the vehicle 200 or an autonomous driving system) use of the pedal. In an embodiment, the brake pattern may be one or more of the frequency of braking, the magnitude of braking, the speed, degree, and / or frequency at which the brake pedal of the vehicle 200 is pushed and / or released, and / or other data related to the instantaneous and / or time-dependent braking of the vehicle 200 (e.g., by the driver 202 or an autonomous driving system). In an embodiment, linear acceleration may be one or more of the frequency of acceleration, the magnitude of acceleration, the rate at which the accelerator pedal of the vehicle 200 is pushed and / or released, and / or other data related to the instantaneous and / or time-dependent acceleration of the vehicle 200 (e.g., by the driver 202 or an autonomous driving system). In an embodiment, rotational smoothness may be one or more of the degree of rotation, the rate of rotation (e.g., related to the vehicle or the rate of change of speed and the rate of change of the rate of change of speed in rotation, as measured by, for example, the steering), the operation of the steering wheel of the vehicle 200 (e.g., by the driver 202), and / or other data related to the instantaneous and / or time-dependent smoothness of rotation of the vehicle 200 (e.g., by the driver 202 or an autonomous driving system). In an embodiment, the turning radius may be the radius of a turn made by the vehicle 200 (e.g., under the control of the driver 202 of the vehicle 200 or an autonomous driving system). In an embodiment, the operating mode of the vehicle 200 may be the state or manner of operation of the vehicle 200 (e.g., as requested and / or set by the driver 202 of the vehicle 200 or an autonomous driving system), such as, for example, a charging mode (e.g., where an electric vehicle is connected to a charger and receives energy from the charger), a "sports" mode (e.g., a mode that increases the amount of acceleration at a given level of depression of the accelerator pedal), a "towing" mode (e.g., where the vehicle is configured to function in a device being towed or in the (e.g., electrical) system of the vehicle), or any other mode related to the operation of the vehicle 200.In an embodiment, the engine mode can be the state of the engine of vehicle 200, for example, a "Drive" mode configured such that vehicle 200 moves forward, a "Reverse" mode configured such that vehicle 200 moves backward, a "Park" mode configured such that the parking brake of vehicle 200 is actuated and vehicle 200 does not move and / or resists movement, a "Neutral" mode in which the operation of the engine does not move vehicle 200, and / or any other mode regarding the engine of vehicle 200. In an embodiment, the gear mode can be the gear shift state of the manual or automatic transmission of vehicle 200, for example, first gear, second gear, and / or third gear. In an embodiment, the user interface vehicle system can include components and / or systems of vehicle 200 that can be controlled by a user (e.g., driver 202 or passenger) of vehicle 200 via a control mechanism (e.g., a manual knob, button, and / or switch, a touch screen digital interface, and / or voice and / or other non-contact controls), for example, an air conditioning system, a heating system, a front windshield wiper, a radio system, an audio system, the speed and frequency of the front windshield wiper and the associated front windshield wiper, and / or any other system, component, or aspect or characteristic thereof that can be controlled by the user of the vehicle.

[0044] Referring to FIG. 3A, in an embodiment, a system 300 that detects an endangered vehicle and responds to the detection of the endangered vehicle may include hardware (e.g., electrical components and / or manual controls) and / or software that controls and / or operates the vehicle 200, components and / or systems of the vehicle 200, and / or the vehicle 200 or any of its components or systems, and / or performs any, some, or all of data or signal processing, data communication, and / or data or signal output related to the vehicle. In an embodiment, the system 300 may include or be connected to a vehicle computing device 310, a vehicle detection system 320, and a vehicle control system 330. In an embodiment, the system 300 may provide or receive input data and generate and implement the results of the detection of the endangered vehicle and the generation of response measures.

[0045] In an embodiment, the vehicle computing device 310 may include or be connected to a transceiver 312 (which may be configured as a transmitter, receiver, and / or transceiver) and / or a vehicle detection system 320 through which the vehicle computing device 310 may receive one or more monitoring inputs of the vehicle 200. Depending on the embodiment, the remote computing device 350 may receive sensor data from dozens, hundreds, or even thousands of different hardware components located throughout the vehicle 200, or in other cases, detection data regarding the operation or movement of the vehicle 200. In an embodiment, the vehicle computing device 310 may include a vehicle data memory component 311 (represented as δ in FIG. 3A) that stores data corresponding to the monitoring inputs received by the vehicle 200 (in an embodiment, from the vehicle detection system 320 and / or in an embodiment, via the transceiver 312). Depending on a particular embodiment, the vehicle data memory component 311 may be configured as a random access memory (RAM), read only memory (ROM), register, database, and / or other hardware that stores data corresponding to the monitoring inputs received by the vehicle 200. Thus, the vehicle data memory component 311 may be part of a broader memory component of the vehicle computing device 310, a data storage component, and / or part of other data storage infrastructure.

[0046] The vehicle computing device 310 may further include a vehicle input monitor 313 (represented as μC in FIG. 3A), and in an embodiment, the vehicle input monitor 313 may represent software operations performed on data corresponding to monitoring inputs of the vehicle 200 obtained from either or both of the transceiver 312 and / or the vehicle detection system 320. The vehicle data memory 311 may store logic (such as trajectory deviation logic for calculating a trajectory deviation value and / or driver profile deviation logic for calculating a driver profile deviation value, as described in more detail below), and when the logic is executed by the vehicle input monitor 313, it may receive one or more monitoring inputs of the vehicle 200, predict a predicted vehicle trajectory (e.g., the predicted vehicle trajectory 220 depicted in FIGS. 4-5D and described in more detail below), detect a detected vehicle trajectory (e.g., any, part, or all of the detected vehicle trajectories 222, 224, 226, 228 depicted in FIGS. 5A-5D and described in more detail below), compare the predicted vehicle trajectory with the detected vehicle trajectory (as described in more detail below with reference to FIGS. 1 and 5A-5D), determine a trajectory deviation value regarding the predicted vehicle trajectory and the detected vehicle trajectory (as described in more detail below with reference to FIGS. 1 and 5A-5D), generate a response measure (as described in more detail below with reference to FIGS. 1 and 3A-3B), implement the response measure (as described in more detail below with reference to FIGS. 1 and 6), compare the detected vehicle trajectory, the monitoring input, or both with the driver profile (as described in more detail below with reference to FIGS. 5A-5D and 7), and / or determine a driver profile deviation value regarding the driver profile and the detected vehicle trajectory, the monitoring input, or both (as described in more detail below with reference to FIGS. 3A-3B and 7), causing any, part, or all of these to be performed by the system 300.

[0047] In an embodiment, the vehicle detection system 320 is connected to the vehicle computing device 310 and may include or be connected to one or more vehicle sensors that detect information related to the operation or movement of the vehicle 200. In an embodiment, the vehicle detection system 320 may include or be connected to a physical sensor that detects, for example, a manual command (e.g., pressing of a pedal by the driver 202 or rotation of the steering wheel of the vehicle 200) related to the operation or movement of the vehicle 200 received from the driver 202 or the autonomous driving system of the vehicle 200. In an embodiment, the vehicle detection system 320 may include or be connected to a digital sensor that detects, for example, a digital command (e.g., a signal from the autonomous driving system of the vehicle 200 or from the operation of the electrical hardware related to the vehicle 200 such as a pedal or a steering wheel by the driver 202) related to the operation or movement of the vehicle 200 received from the driver 202 or the autonomous driving system of the vehicle 200. In an embodiment, the vehicle detection system 320 may include sensors that detect the movement, position, or state of the vehicle 200 or components or systems of the vehicle 200.

[0048] Referring to FIG. 3A, the vehicle detection system 320 may include a pedal sensor 320A that detects, for example, a pedal throttle pattern, a linear acceleration input, a brake pattern, and / or an electrical signal related to the foregoing of the vehicle 200 (e.g., when the pedal is pressed by the driver 202). In an embodiment, the pedal sensor 320A may include, for example, a pressure sensor (e.g., detecting the pressing of the pedal of the vehicle 200), a position sensor (e.g., detecting the position of the pedal of the vehicle 200), and / or other sensors.

[0049] In an embodiment, the vehicle detection system 320 may include an acceleration sensor 320B, which may detect, for example, the speed, linear acceleration, lateral acceleration, and / or braking pattern of the vehicle 200. In an embodiment, the acceleration sensor 320B may include, for example, a speedometer, an accelerometer, a global positioning unit (which may measure the acceleration, braking, and / or speed of the vehicle 200 through comparison of the location information of the vehicle 200), and / or other sensors.

[0050] In an embodiment, the vehicle detection system 320 may include a steering sensor 320C, which may detect, for example, lateral acceleration input, rotational smoothness input, lateral acceleration input, and / or an electrical signal related to the foregoing of the vehicle 200 (including, for example, the steering wheel of the vehicle 200). In an embodiment, the steering sensor 320C may include, for example, a gyroscope (which may detect the orientation of the steering wheel of the vehicle 200), a pressure sensor (which may detect the force for rotating the steering wheel of the vehicle 200), and / or other sensors.

[0051] In an embodiment, the vehicle detection system 320 may include a wheel sensor 320D, which may detect, for example, the speed (lateral or rotational relative to the axle) of one or more wheels of the vehicle 200, the orientation (relative to the chassis of the vehicle 200), the braking pattern, the linear acceleration, and / or the lateral acceleration. In an embodiment, the wheel sensor 320D may include a tachometer (which may measure the rotational speed and / or acceleration of the wheels of the vehicle 200), a gyroscope (which may measure the orientation of the wheels of the vehicle 200), an accelerometer (which may measure the lateral speed and / or acceleration related to the wheels of the vehicle 200), and / or other sensors.

[0052] In an embodiment, the vehicle detection system 320 may include a location sensor 320E, which may detect, for example, the location, speed, braking pattern, linear acceleration, lateral acceleration, rotational smoothness, and / or turning radius of the vehicle 200. In an embodiment, the location sensor 320E may include, for example, a global positioning unit (which may calculate the position of the vehicle 200 by wirelessly communicating with, for example, a satellite or other system) and / or other sensors.

[0053] In an embodiment, the vehicle detection system 320 may include, for example, a vehicle operation mode sensor 320F that detects an electrical signal related to the operation mode or the aforementioned aspects of the vehicle 200. In an embodiment, the vehicle operation mode sensor 320F may include, for example, a sensor connected to a manual vehicle operation mode selector (such as a button, knob, or stick used by the driver 202) and / or other sensors.

[0054] In an embodiment, the vehicle detection system 320 may include, for example, an engine mode sensor 320G that detects an electrical signal related to the engine mode or the aforementioned aspects of the vehicle 200. In an embodiment, the engine mode sensor 320G may include, for example, a sensor connected to a manual vehicle engine mode selector (such as a button, knob, or stick used by the driver 202), a sensor connected to the engine of the vehicle 200, and / or other sensors.

[0055] In an embodiment, the vehicle detection system 320 may include, for example, a gear mode sensor 320H that detects an electrical signal related to the gear mode or the aforementioned aspects of the vehicle 200. In an embodiment, the gear mode sensor 320H may include, for example, a sensor configured to determine the position of the gear shift stick of the vehicle 200 and / or other sensors.

[0056] In an embodiment, the vehicle detection system 320 may include a user interface vehicle system 320J that detects and receives commands (e.g., from the driver 202 or passengers of the vehicle 200) regarding the operation or movement of the vehicle 200 via, for example, a touch screen, voice control, or other interactive mechanism. In an embodiment, the user interface vehicle system 320J may include digital mechanisms such as, for example, a touch screen, a digital screen, and / or a digital user interface that controls aspects of the vehicle 200 such as, for example, the air conditioning system, heating system, front windshield wipers, digital user interface, operation mode, engine mode, gear mode, and / or other components, systems, or modalities of the vehicle 200. In an embodiment, the user interface vehicle system 320J may further include sensors related to biometric analysis of the driver 202 and / or the interaction of the driver 202 with the vehicle 200. For example, in an embodiment, the user interface vehicle system 320J may include a camera (e.g., monitoring the eyes of the driver 202 to determine, for example, whether the driver 202 is looking at the road 210), a steering wheel pressure sensor of the vehicle 200 for the steering wheel (e.g., monitoring the pressure of the driver 202's grip on the steering wheel to determine, for example, whether the driver 202 is holding their hand on the steering wheel), and / or other such sensors that monitor the driver 202.

[0057] In an embodiment, the vehicle detection system 320 may include a manual user interface system 320K that detects and receives commands (e.g., from the driver 202 or passengers of the vehicle 200) regarding the operation or movement of the vehicle 200 via, for example, a manual button, knob, or stick. In an embodiment, the manual user interface system 320K may include manual mechanisms such as, for example, buttons, knobs, or sticks that control aspects of the vehicle 200 such as, for example, the air conditioning system, heating system, front windshield wipers, operation mode, engine mode, gear mode, and / or other components, systems, or modalities of the vehicle 200.

[0058] In an embodiment, the vehicle detection system 320 may include, for example, an external vehicle sensor 320L that detects the surroundings of the vehicle 200. In an embodiment, the external vehicle sensor 320L may include one or more cameras, a radar sensor, a wireless connection sensor (configured to communicate with other vehicles or devices via, for example, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth®, Zigbee®, or other wireless connection systems), and / or other sensors. In an embodiment, the external vehicle sensor 320L may be used to determine or generate data (such as linear acceleration data, lateral acceleration data, brake pattern data, rotational smoothness data, turning radius data, etc.) that can be used to determine the actual movement of the vehicle 200 through a comparison of data over time regarding the surroundings of the vehicle 200. In an embodiment, the external vehicle sensor 320L may be used by an autonomous driving system of the vehicle 200 that is used in (complete or partial) operation of the vehicle 200.

[0059] In an embodiment, the vehicle detection system 320 may include a vehicle software and hardware infrastructure system 320M that can provide, generate, and / or receive signals from any, some, or all of the sensors and / or systems of the vehicle detection system 320 as monitoring inputs. In an embodiment, the vehicle software and hardware infrastructure system 320M may include software that (partially and / or fully) operates and processes instructions from any, some, or all of the sensors and / or systems of the vehicle detection system 320 and / or components and / or systems of the vehicle 200. In an embodiment, the vehicle software and hardware infrastructure system 320M may include a processor, an electronic control unit, a memory, and / or other components that generate, transmit, or receive signals, data, inputs, or outputs.

[0060] In an embodiment, the vehicle detection system 320 may include any one, part, or all of a pedal sensor 320A, an acceleration sensor 320B, a steering sensor 320C, a wheel sensor 320D, a location sensor 320E, a vehicle operation mode sensor 320F, an engine mode sensor 320G, a gear mode sensor 320H, a user interface vehicle system 320J, a manual user interface system 320K, an external vehicle sensor 320L, a vehicle software and hardware infrastructure system 320M, and / or any other sensor, component, or system. Thus, in an embodiment, each of the one or more monitoring inputs may be generated by any one, part, or all of the sensors and systems of the vehicle detection system 320, and in an embodiment having some or all of the sensors and systems of the vehicle detection system 320 as depicted in FIG. 3A, at least one monitoring input may be received by only one, only a part, or all of the sensors of the vehicle detection system 320.

[0061] As will be understood by those skilled in the art, in an embodiment, the vehicle detection system 320 may comprise any other sensors and / or systems that may provide relevant monitoring inputs used in method 100. Further, in an embodiment, the sensors and / or systems of the vehicle detection system 320 may provide other functions or enable other uses of the vehicle 200 in addition to providing monitoring inputs for the vehicle 200, and thus, in certain such embodiments, any component and / or system of the vehicle 200 may provide a monitoring input at block 110 of method 100.

[0062] Referring again to FIGS. 1 and 3A, in an embodiment, the vehicle computing device 310 may receive any one, part, or all of the one or more monitoring inputs of block 110 from the vehicle detection system 320. However, in an embodiment, the vehicle computing device 310 may receive any one, part, or all of the one or more monitoring inputs of block 110 from a source external to the vehicle computing device 310 and / or the vehicle 200, for example, via the transceiver 312.

[0063] Referring to FIG. 3B, in an embodiment, the system 300 may include a remote computing device 350 (e.g., a cloud computing system or other computing device) that may be connected to the vehicle 200, the vehicle computing device 310, the vehicle detection system 320, and / or the vehicle control system 330 (e.g., via Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth®, Zigbee®, or other wireless connection systems). In an embodiment, the remote computing device 350 may include any, some, or all of a remote data memory component 351, a remote vehicle input monitor 353, and / or a remote vehicle input monitor output conversion mechanism 354. Any, some, or all of the components, systems, and / or processes of the vehicle computing device 310, the vehicle detection system 320, and / or the vehicle control system 330 (as depicted, for example, in FIG. 3A) may instead be present within the remote computing device 350, and any, some, or all of the processes related to method 100 and / or performed by the vehicle computing device 310, the vehicle detection system 320, and / or the vehicle control system 330 may instead occur within and / or be performed by the remote computing device 350. Thus, in an embodiment, the remote data memory component 351 may perform any, some, or all of the functions of the vehicle data memory component 311 separately from, in parallel with, and / or in cooperation with the vehicle data memory component 311, and may store any, some, or all of the data and / or logic described above as being stored within the vehicle data memory component 311. In an embodiment, the remote vehicle input monitor 353 may perform any, some, or all of the functions of the vehicle input monitor 313 separately from, in parallel with, and / or in cooperation with the vehicle input monitor 313. In an embodiment, the remote vehicle input monitor output conversion mechanism 354 may perform any, some, or all of the functions of the vehicle control system 330 separately from, in parallel with, and / or in cooperation with the vehicle control system 330.

[0064] Accordingly, referring to FIGS. 1 and 3B, in block 110, in an embodiment, any, some, or all of the one or more monitoring inputs may be generated by the vehicle detection system 320 and received by the vehicle data memory component 311 and / or the remote computing device 350. Further, in an embodiment, any, some, or all of the one or more monitoring inputs may be generated by the remote computing device 350 and received by the remote data memory component 351 and / or the vehicle data memory component 311 (e.g., via a wireless connection between the transceiver 312 and the transceiver 352 of the remote computing device 350). In certain such embodiments, any, some, or all of the sensors and / or systems of the vehicle detection system 320 may be partially or fully included in the remote computing device 350. For example, the location sensor 320E may generate monitoring inputs corresponding to, e.g., speed, linear acceleration, lateral acceleration, and / or braking pattern, via processing (e.g., of location data and associated time values regarding the vehicle 200) performed by the remote computing device 350. In an embodiment, any, some, or all of the generation, processing, reception, analysis, and / or modification of any, some, or all of the inputs of the vehicle detection system 320, including any of sensors 320A, 320B, 320C, 320D, 320E, 320F, 320G, 320H, 320L, and / or systems 320J, 320K, 320M, may be performed by the remote computing device 350. In certain such embodiments, any such generation, processing, reception, analysis, and / or modification of the inputs may be performed by the remote computing device 350 via edge computing (e.g., to offload necessary calculations regarding the foregoing to the remote computing device 350 due to computational capacity limitations of the vehicle computing device 310).

[0065] Referring to FIG. 4 and again to FIG. 1, block 120 of method 100 includes predicting a predicted vehicle trajectory 220 using at least one of the monitoring inputs. As shown in FIG. 4, the predicted vehicle trajectory 220 indicates that the vehicle computing device 310 and / or the remote computing device 350 predicts that the vehicle 200 will remain within the lane 213 as defined by the shoulder line 211 and the center line 212. In embodiments, the predicted vehicle trajectory 220 relates to a predetermined and / or variable amount of time (e.g., multiple seconds) ahead and / or a predetermined and / or variable distance (e.g., multiple feet, multiple yards, multiple meters, etc.) that the vehicle 200 moves, and may include the predicted vehicle trajectory of the vehicle 200. In embodiments, the predicted vehicle trajectory 220 may be an estimate of the predicted movement distance and direction of the vehicle 200 0.1 seconds ahead, 0.5 seconds ahead, 1 second ahead, 5 seconds ahead, 10 seconds ahead, or any amount of time ahead. In embodiments, the predicted vehicle trajectory 220 may be an estimate of the predicted movement distance and direction of the vehicle 200 for 1 meter, 10 meters, 100 meters, or any other distance that the vehicle 200 moves.

[0066] In an embodiment, the predicted vehicle trajectory 220 can be predicted via monitoring inputs generated by the vehicle detection system 320. For example, in an embodiment, the pedal sensor 320A can generate data of monitoring inputs including a pedal throttle pattern input caused by the driver 202 pressing the accelerator pedal of the vehicle 200. In an embodiment, the pedal throttle pattern input is received by the vehicle computing device 310 and / or the remote computing device 350 and can be used by the vehicle input monitor 313 and / or the remote vehicle input monitor 353 to predict the predicted vehicle trajectory 220. In another example, in an embodiment, one or more of the sensors and / or systems of the vehicle detection system 320, for example, in an embodiment, the linear acceleration input data generated by the pedal sensor 320A via the driver 202 pressing the accelerator pedal of the vehicle 200, the lateral acceleration input data generated by the steering sensor 320C via an instruction generated by the automatic driving system of the vehicle 200 (while the driver 202 is driving the vehicle 200, for example, functioning as a driving assistance system), and the linear acceleration data generated by a combination of the acceleration sensor 320B (for example, disposed on the vehicle 200) and the location sensor 320E (for example, disposed on either or both of the vehicle 200 and the remote computing device 350) can be used to predict the predicted vehicle trajectory 220. Thus, in an embodiment, the predicted vehicle trajectory 220 can be predicted by either or both of the vehicle computing device 310 and / or the remote computing device 350 using inputs from one, some, or all of the sensors and / or systems of the vehicle detection system 320.

[0067] In an embodiment, the predicted vehicle trajectory 220 can be predicted via a neural network, a deep learning algorithm, an optimization algorithm, and / or other AI algorithms. In certain such embodiments, such neural network, deep learning algorithm, optimization algorithm, or other AI algorithms are stored in either or both of the vehicle data memory component 311 and / or the remote data memory component 351 and can be executed by either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353 (e.g., via edge computing). In an embodiment, the predicted vehicle trajectory can be predicted by a physics simulation algorithm (e.g., using speed, direction, and rate of change of speed or direction, such as determined by monitoring inputs generated by the vehicle detection system 320). In certain such embodiments, the physics simulation algorithm is stored in either or both of the vehicle data memory component 311 and / or the remote data memory component 351 and can be executed by either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353 (e.g., via edge computing). In an embodiment, the predicted vehicle trajectory 220 predicts the probability of the driving behavior of the driver 202 of the vehicle 200 and / or the autonomous driving system (e.g., based on past driver behavior, the location of the vehicle 200, and / or the route of the vehicle 200) to determine the predicted vehicle trajectory 220 and / or additional predicted vehicle trajectories and / or the associated probability values for each predicted vehicle trajectory, and can be predicted by a statistical analysis algorithm (e.g., using a driver profile as described in more detail below). In certain such embodiments, the statistical analysis algorithm is stored in either or both of the vehicle data memory component 311 and / or the remote data memory component 351 and can be executed by either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353 (e.g., via edge computing).

[0068] In an embodiment, either or both of the vehicle computing device 310 and / or the remote computing device 350 may generate multiple predicted vehicle trajectories (e.g., rather than a single predicted vehicle trajectory, such as predicted vehicle trajectory 220) by, for example, separate processing for different monitoring inputs of at least one monitoring input of block 110, and / or by different methods for predicting a predicted vehicle trajectory using one or more of at least one monitoring input of block 110. In certain such embodiments, nonetheless, a single predicted vehicle trajectory (e.g., predicted vehicle trajectory 220) may be generated using multiple predicted vehicle trajectories (e.g., by either or both of computing devices 310, 350) by combining any, some, or all of the multiple predicted vehicle trajectories (e.g., via an averaging function, a weighting function, etc.).

[0069] Referring to FIGS. 5A-5C and again to FIG. 1, block 130 of method 100 includes detecting a detected vehicle trajectory. For example, in the embodiment of FIG. 5A, a first detected vehicle trajectory 222 indicates that vehicle 200 is moving not along predicted vehicle trajectory 220 and not within lane 213, but rather toward oncoming traffic lane 214 and center line 212. In another example, in the embodiment of FIG. 5B, a second detected vehicle trajectory 224 indicates that vehicle 200 is stopped rather than moving forward as predicted in predicted vehicle trajectory 220. In another example, in the embodiment of FIG. 5C, a third detected vehicle trajectory 226 indicates that vehicle 200 is moving along predicted vehicle trajectory 220.

[0070] Referring again to FIGS. 3A-3B, in an embodiment, any, some, or all of the detected vehicle tracks 222, 224, 226 of the vehicle 200 can be detected by the vehicle detection system 320 and / or by either or both of the vehicle computing device 310 and / or the remote computing device 350 (e.g., by processing the detection data inputs detected by the vehicle detection system 320). In an embodiment, either or both of the vehicle computing device 310 and / or the remote computing device 350 can generate multiple detected vehicle tracks (e.g., not just a single detected vehicle track, e.g., any, some, or all of the detected vehicle tracks 222, 224, 226) by, for example, separate processing of different detection data inputs and / or different methods for detecting detected vehicle tracks using one or more detection data inputs. In certain such embodiments, nevertheless, a single detected vehicle track (e.g., any, some, or all of the detected vehicle tracks 222, 224, 226) can be generated using multiple predicted vehicle tracks (e.g., by either or both of the computing devices 310, 350) by combining any, some, or all of the multiple detected vehicle tracks (e.g., via an average function, a weighting function, etc.).

[0071] Referring again to FIG. 1, block 140 of method 100 includes comparing the predicted vehicle track 220 with the detected vehicle tracks of block 130 (e.g., in an embodiment, any, some, or all of the detected vehicle tracks 222, 224, 226). In an embodiment, the predicted vehicle track 220 can be compared with the detected vehicle tracks (e.g., in an embodiment, any, some, or all of the detected vehicle tracks 222, 224, 226) by either or both of the vehicle input monitor 313 of the vehicle computing device 310 and / or the remote vehicle input monitor 353 of the remote computing device 350.

[0072] Referring to FIG. 5A, in the present embodiment, comparing the predicted vehicle trajectory 220 with the first detected vehicle trajectory 222 may include, in an embodiment, determining that the first detected vehicle trajectory 222 deviates from the predicted vehicle trajectory 220. Further, in an embodiment, comparing the predicted vehicle trajectory 220 with the first detected vehicle trajectory 222 may further include determining that the first detected vehicle trajectory 222 puts the vehicle 200 at risk by directing the vehicle 200 (e.g., as detected by the external vehicle sensor 320L) into the oncoming traffic lane 214 and / or towards the oncoming vehicle 230 (e.g., by a malicious third party or software starting a sharp turn contrary to the operation of the vehicle 200 according to the instructions of the driver 202 or the autonomous driving system of the vehicle 200).

[0073] Referring to FIG. 5B, in the present embodiment, comparing the predicted vehicle trajectory 220 with the second detected vehicle trajectory 224 may include, in an embodiment, determining that the second detected vehicle trajectory 224 stops suddenly instead of proceeding along the predicted vehicle trajectory 220. Further, in an embodiment, comparing the predicted vehicle trajectory 220 with the second detected vehicle trajectory 224 may further include determining that the second detected vehicle trajectory 224 puts the vehicle 200 at risk by rapidly reducing the speed of the vehicle 200 (e.g., as compared to the speed detected by the acceleration sensor 320B) (e.g., by a malicious third party or software starting to brake contrary to the operation of the vehicle 200 according to the instructions of the driver 202 or the autonomous driving system of the vehicle 200).

[0074] Referring to FIG. 5C, in the present embodiment, comparing the predicted vehicle trajectory 220 with the third detected vehicle trajectory 226 may include determining, in the embodiment, whether the third detected vehicle trajectory 226 is along the predicted vehicle trajectory 220 or is substantially the same as the predicted vehicle trajectory 220. Further, in the embodiment, comparing the predicted vehicle trajectory 220 with the third detected vehicle trajectory 226 may further include determining that the vehicle 200 (and / or its components or systems) is operating normally and, thus, is not, for example, controlled by a malicious third party or software and / or does not appear to be under the control of a malicious third party or software.

[0075] However, in the embodiment, as described above, even when the vehicle 200 (and / or its components or systems) is under the control of a malicious third party or software, nevertheless, when the malicious third party or software can reproduce the normal functions of the vehicle 200 that follow the operation of the vehicle 200 by, for example, the driver 202 and / or the autonomous driving system of the vehicle 200, the operation of the vehicle 200 may not be as intense or dangerous for the vehicle 200 as, for example, the detected vehicle trajectories 224, 226.

[0076] Conversely, referring to FIG. 5D, in an embodiment, the fourth detected vehicle trajectory 228 may be slightly different from the predicted vehicle trajectory 220 so as not to expose the vehicle 200 to danger (e.g., by staying within the lane 213 rather than turning the vehicle 200 towards the oncoming vehicle 230 as in the embodiment of FIG. 5A). Such a difference between the fourth detected vehicle trajectory 228 and the predicted vehicle trajectory 220 may, in an embodiment, be due to inaccuracies in one or more monitoring inputs of the block 110 (e.g., due to a sensor or system defect of the vehicle detection system 320), or due to undetected environmental factors that cause a difference between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228 (e.g., due to an undetected increase in the slope or angle of the lane 213), and may be benign (e.g., not as a result of the vehicle 200 and / or its system and / or components being endangered). However, as described above and in more detail below, such a difference may alternatively, in an embodiment, indicate that the vehicle 200 (and / or its components and / or system) is endangered, e.g., under the control of a malicious third party or software that hides the endangered nature of the vehicle 200 by mimicking the normal functions of the vehicle 200, but nevertheless may cause a difference between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228 due to, for example, latency in the system and / or components of the vehicle 200 caused by the endangered nature of the vehicle 200. Thus, in an embodiment, a deviation between the monitoring input of the driver 202 and the detected vehicle trajectories 224, 226 may be noticeable to the driver 202, and a smaller deviation, e.g., between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228, may not be noticeable to the human eye (e.g., because a malicious third party or software endangers the vehicle 200 and introduces additional latency between the input of the driver 202 and the receipt of that input by the system and / or components of the vehicle 200), but nevertheless the deviation may be detectable by the system 300 (e.g., via the vehicle computing device 310 and / or the remote computing device 350) to identify that the vehicle 200 is endangered.In certain such embodiments, the system 300 can analyze dozens, hundreds, and / or even thousands of monitoring inputs (e.g., via the vehicle computing device 310 and / or the remote computing device 350) and do so in a time frame of less than 10 seconds, less than 5 seconds, and / or even less than 1 second, to detect small deviations (e.g., between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228). Thus, in embodiments, even in situations where the vehicle 200 is endangered but the nature of the endangerment does not (yet) present an immediate threat to damage to the vehicle 200, the nature of the endangerment of the vehicle 200 can nevertheless be detected.

[0077] Referring again to FIGS. 1 and 3A - 3B, block 150 of method 100 includes determining an orbit deviation value for a predicted vehicle orbit 220 and a detected vehicle orbit (e.g., in an embodiment, any, some, or all of detected vehicle orbits 222, 224, 226, 228). In an embodiment, the orbit deviation value can be a difference regarding any, some, or all of speed, brake pattern, linear acceleration, lateral acceleration, rotational smoothness, turning radius, operation mode, engine mode, gear mode when compared to the predicted vehicle orbit 220, and / or a function of other detected differences regarding the operation, speed, and / or direction of the detected vehicle orbit (e.g., any, some, or all of detected vehicle orbits 222, 224, 226, 228) of vehicle 200. In an embodiment, the orbit deviation value can have a magnitude (e.g., on a scale of 1 - 10), and the magnitude of the orbit deviation value indicates the degree to which the detected vehicle orbit (e.g., any, some, or all of detected vehicle orbits 222, 224, 226, 228) deviates from the predicted vehicle orbit 220. Thus, in an embodiment, the calculated magnitude regarding the orbit deviation value can be a function of the magnitude of the difference between the predicted vehicle orbit 220 and any, some, or all of the detected vehicle orbit (e.g., any, some, or all of detected vehicle orbits 222, 224, 226, 228), speed, brake pattern, linear acceleration, lateral acceleration, rotational smoothness, turning radius, operation mode, engine mode, gear mode, and / or other detected operations of vehicle 200 (e.g., the residual value between the location, speed, and / or direction of the predicted vehicle orbit 220 and the location, speed, and / or direction of the detected vehicle orbit). In an embodiment, the orbit deviation value can be determined by either or both of the vehicle input monitor 313 of the vehicle computing device 310 and / or the remote vehicle input monitor 353 of the remote computing device 350.

[0078] For example, referring to the embodiments of FIGS. 5A - 5D, in block 140, comparing the detected vehicle trajectories 222, 224 with the predicted vehicle trajectory 220 may cause, for example, the fourth detected vehicle trajectory 228 to be calculated in block 150 with a trajectory deviation value greater than the trajectory deviation value calculated by comparing the predicted vehicle trajectory 220 with the fourth detected vehicle trajectory 228 because the detected vehicle trajectories 222, 224 differ from the predicted vehicle trajectory 220 to a greater extent than the fourth detected vehicle trajectory 228. Further, in an embodiment, comparing the second detected vehicle trajectory 224 with the predicted vehicle trajectory 220 may cause, for example, the second detected vehicle trajectory 224 to follow the predicted vehicle trajectory 220 more similarly than the first detected vehicle trajectory 222, and / or for example, because the second detected vehicle trajectory 224 is caused by the driver 202 suddenly braking faster than the system 300 can generate a new predicted vehicle trajectory based on the brake applied by the driver 202, a trajectory deviation value having a magnitude smaller than the trajectory deviation value calculated by comparing the first detected vehicle trajectory 222 with the predicted vehicle trajectory 220 may be calculated.

[0079] In an embodiment, in block 140, comparing the third detected vehicle trajectory 226 with the predicted vehicle trajectory 220 may cause, in block 150, a trajectory deviation value having a magnitude smaller than the trajectory deviation value calculated based on the comparison of the predicted vehicle trajectory 220 with the detected vehicle trajectories 222, 224, 228 because the third detected vehicle trajectory 226 follows the predicted vehicle trajectory 220 more similarly. However, nevertheless, comparing the predicted vehicle trajectory 220 with the third detected vehicle trajectory 226 may result in the calculation of a non - zero trajectory deviation value (for example, because the third detected vehicle trajectory 226 does not extend as far as the predicted vehicle trajectory 220).

[0080] Referring again to FIG. 1, block 160 of method 100 includes generating a response measure in response to determining that a deviation value (e.g., determined at block 150) exceeds a deviation threshold. In an embodiment, the deviation value may be determined to exceed the deviation threshold by either or both of vehicle input monitor 313 of vehicle computing device 310 and / or remote vehicle input monitor 353 of remote computing device 350. In an embodiment, the response measure may be generated by either or both of vehicle input monitor 313 of vehicle computing device 310 and / or remote vehicle input monitor 353 of remote computing device 350. In an embodiment, one response measure may be generated at block 160. In an embodiment, multiple response measures may be generated at block 160.

[0081] In an embodiment, the trajectory deviation threshold can be a predetermined one. In certain such embodiments, the trajectory deviation threshold can be, for example, a predetermined one based on a method used to calculate a trajectory deviation value and / or a statistically normal (e.g., within a standard deviation range) deviation between a detected vehicle trajectory and a predicted vehicle trajectory. For example, in an embodiment, the trajectory deviation values for the detected vehicle trajectories 222, 224 and the predicted vehicle trajectory 220 can exceed a predetermined trajectory deviation value, while the trajectory deviation values for the third detected vehicle trajectory 226 and the predicted vehicle trajectory 220 may not exceed the predetermined trajectory deviation value. However, the trajectory deviation threshold may not be fixed. Instead, for example, a technical improvement that improves the ability of the system 300 to calculate the predicted vehicle trajectory, the state in which the vehicle 200 is operating (e.g., the trajectory deviation threshold can increase in a more variable driving state such as strong wind or freezing of the road 210), a decrease in the characteristics of the sensors and / or the system of the vehicle detection system 320, and other changing situations can cause it to vary. Thus, in an embodiment, in a first context (e.g., a substantially ideal driving state on the road 210), the trajectory deviation value can exceed the trajectory deviation threshold, while in a second context (e.g., the road 210 is frozen), the same trajectory deviation value may not exceed the trajectory deviation threshold. Thus, in the embodiment of FIG. 5D, the fourth detected vehicle trajectory 228 and the predicted vehicle trajectory 220 can result in a decision trajectory deviation value that exceeds the trajectory deviation threshold in the aforementioned first context, while in the aforementioned second context, the decision trajectory deviation value may not exceed the trajectory deviation threshold. In an embodiment, the trajectory deviation value can be associated with a confidence interval, while the trajectory deviation threshold can be fixed. As a result, factors that can reduce the accuracy of the predicted vehicle trajectory 220 (such as those described above) can instead result in a lower confidence interval associated with the predicted vehicle trajectory 220, and thus a lower trajectory deviation value for one or more of the predicted vehicle trajectory 220 and the detected vehicle trajectories 222, 224, 226, 228.

[0082] In an embodiment, the trajectory deviation value can be partially or fully determined by a safety threshold. In an embodiment, the safety threshold can indicate the level of risk to the vehicle 200 and / or the driver 202 presented by the predicted vehicle trajectory 220. In an embodiment, the safety threshold can be exceeded, for example, if the vehicle 200 is predicted to be driven out of the lane 213 and collide with another vehicle on the road 210 and / or otherwise endanger the vehicle 200 and / or the driver 202 as indicated by the predicted vehicle trajectory 220. For example, referring to FIG. 5A, if the predicted vehicle trajectory 220 is predicted to follow not the predicted vehicle trajectory 220 depicted in FIG. 5A but instead the first detected vehicle trajectory 222, the predicted vehicle trajectory 220 can exceed the safety threshold due to the risk that the vehicle 200 enters the oncoming traffic lane 214 and the risk, for example, of colliding with the oncoming vehicle 230. In an embodiment, the safety threshold can also be exceeded by the detection of the driver 202 (e.g., via the camera and / or the steering wheel pressure sensor of the user interface vehicle system 320J as described above with reference to FIG. 3A) and / or can be variable depending on such detection. For example, in an embodiment, the camera of the vehicle software and hardware infrastructure system 320M can detect that the driver 202 is not looking at the road 210, and upon detecting it, the safety threshold can be exceeded and / or the safety threshold (e.g., compared to the predicted vehicle trajectory 220) can be reduced. As another example, in an embodiment, the steering wheel pressure sensor of the vehicle software and hardware infrastructure system 320M can detect that the driver 202 is not holding his hand on the steering wheel of the vehicle 200, and upon detecting it, the safety threshold can be exceeded and / or the safety threshold (e.g., compared to the predicted vehicle trajectory 220) can be reduced.

[0083] When determining a track deviation value for a predicted vehicle track 220 and a detected vehicle track (e.g., in an embodiment, any, part, or all of the detected vehicle tracks 222, 224, 226, 228), in an embodiment, either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353 may generate a response measure. In an embodiment, the response measure may be any measure that enhances the safety of the vehicle 200 and / or the driver 202 (e.g., by controlling or stopping components or systems of the vehicle 200 to prevent the vehicle 200 from following the detected track) and / or addresses or eliminates potential or actual endangering natures of the vehicle 200. For example, in an embodiment, the response measure may include at least one of a security sweep, disconnecting the connection of the vehicle 200, warning the driver 202 and / or passengers of the vehicle 200, warning a third party, shutting down the vehicle 200, shutting down components of the vehicle 200, shutting down systems of the vehicle 200, invalidating commands, command checking, or a combination thereof. In an embodiment, the security sweep may include a security check of the vehicle 200 (and / or its components and / or systems) by the security system of the vehicle 200 to evaluate whether the vehicle 200 (and / or its components and / or systems) may be endangered. In an embodiment, disconnecting the connection of the vehicle 200 may include stopping the connection between the vehicle 200 (and / or its components and / or systems) and any, part, or all of external vehicles, systems, software, and / or computing devices (e.g., via Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth®, Zigbee®, other wireless connection systems, etc.).In an embodiment, a warning to the driver 202 and / or passengers of the vehicle 200 may be, for example, to represent potential or actual systems and / or components of the vehicle 200 that may be affected by the potential or actual dangerous nature of the vehicle 200, (including, for example, representing other response measures employed by the system 300) to represent how the system 300 is dealing with the potential or actual dangerous nature of the vehicle 200, and / or to convey any other relevant information regarding the vehicle 200 (and / or its components and / or systems) and / or the actual or potential dangerous nature of the vehicle 200, to warn the driver 202 and / or passengers of the vehicle 200 that the vehicle 200 (and / or its components and / or systems) may be in danger, including one or more visual warnings (e.g., via the user interface vehicle system 320J, the display of the vehicle 200, and / or other visual signals of the vehicle 200) and / or an audio warning (e.g., via the speaker of the vehicle 200). In an embodiment, a warning to a third party may include one or more visual warnings, audio warnings, and / or signal transmission warnings to the third party. In an embodiment, a visual warning to a third party may include, for example, activating an external signal of the vehicle 200 (e.g., hazard lights or other warning lights) to inform other vehicles (e.g., oncoming vehicle 230) or people near the vehicle 200 that the vehicle 200 is potentially and / or actually in danger, poses a threat to other vehicles or people in its vicinity, and / or is not functioning properly in other cases. In an embodiment, an audio warning to a third party may include, for example, the speaker of the vehicle 200 reproducing a warning to other vehicles (e.g., oncoming vehicle 230) that the vehicle 200 is potentially and / or actually in danger, poses a threat to other vehicles or people in its vicinity, and / or is not functioning properly in other cases.In an embodiment, the signal transmission warning to a third party can be (wired or wireless) communication by the system 300 to a third party (e.g., another vehicle near the vehicle 200, an emergency service, and / or another third party who may assist or desire to avoid the vehicle 200 if the vehicle 200 is in danger) via, for example, an electrical communication network (e.g., a call to an emergency service), an electrical signal (e.g., to warn another vehicle response system in danger, the manufacturer of the vehicle 200, and / or an emergency service), or an electromagnetic signal (e.g., via radio waves, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth®, Zigbee®, or other wireless connection systems to warn another nearby vehicle, the manufacturer of the vehicle 200, and / or an emergency service). In an embodiment, the shutdown of the vehicle 200, a component of the vehicle 200, and / or a system of the vehicle 200 may include disabling, powering off, or otherwise stopping with respect to the function of the vehicle 200, a component of the vehicle 200, and / or a system of the vehicle 200. In an embodiment, the invalidation command may include a command (e.g., from the vehicle computing device 310 and / or the remote computing device 350) to the vehicle 200 and / or its components and / or systems to ignore the operation command of the vehicle components and / or systems in order to (e.g., stop the command from the brake system of the vehicle 200 to activate the parking brake of the vehicle 200 if the brake results in or will result in a third detected vehicle trajectory 226). In an embodiment, the command check may include signal transmission to the components and / or systems of the vehicle 200 that receive the command (e.g., the command to activate the brake system of the vehicle 200 that results in or will result in a third detected vehicle trajectory 226) to confirm that the command is valid (e.g., not as a result of a system error that results in a defective command).

[0084] Referring to FIGS. 1 and 6, block 170 of method 100 includes implementing a response measure. In an embodiment, the response measure may be implemented by a vehicle control system 330, a vehicle computing device 310, a remote computing device 350, components and / or systems of vehicle 200, and / or any external computing device or hardware to which vehicle 200 may be electrically connected (e.g., via Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth®, Zigbee®, or other wireless connection systems). In an embodiment, the vehicle control system 330 may include any component and / or system that fully or partially controls an aspect of the vehicle's functionality. In an embodiment, a single response measure may be implemented by one or more of the components and / or systems of the vehicle control system 330, the vehicle computing device 310, and / or the remote computing device 350. In an embodiment, since multiple response measures may be generated at block 160, in certain such embodiments, at block 170, multiple response measures may be implemented (e.g., via one or more of the components and / or systems of the vehicle control system 330, the vehicle computing device 310, and / or the remote computing device 350). In an embodiment where the response measure is generated by the vehicle input monitor 313 at block 160, the response measure may be communicated to the vehicle control system 330 at block 170 (e.g., via the local communication interface of system 300 that connects the vehicle computing device 310 to the vehicle control system 330) as instructions for implementing the response measure. In an embodiment where the response measure is generated by the remote computing device 350, the response measure may be communicated to the transceiver 331 of the vehicle control system 330 (e.g., via the transceiver 355 of the remote computing device 350) as instructions for implementing the response measure.

[0085] In an embodiment, the vehicle control system 330 may include, for example, a vehicle mode controller 330A that controls an operation mode, an engine mode, and / or a gear mode of the vehicle 200. Thus, in an embodiment, the vehicle mode controller 330A may implement, for example, a response measure to shut off the engine of the vehicle 200, a disable command, and / or a command check regarding a command of the vehicle mode controller 330A (e.g., a command to switch the engine mode of the vehicle 200 from "drive" to "reverse" while the vehicle 200 is being driven on the road 210) received (e.g., from the vehicle computing device 310 and / or the remote computing device 350), a response measure to disconnect the connection of the vehicle mode controller 330A to the autonomous driving system 330G of the vehicle 200 (as will be described in more detail below), and / or other response measures.

[0086] In an embodiment, the vehicle control system 330 may include a vehicle security system 330B (configured as software and / or a non-transitory computer-readable medium, for example, executed by the vehicle computing device 310 and / or the remote computing device 350), and the vehicle security system 330B may secure the digital and / or physical systems and / or components of the vehicle 200 against security threats (such as malicious third parties and / or software), and / or monitor the digital and / or physical systems and / or components of the vehicle 200 to detect potential signs that the components and / or systems of the vehicle 200 are at risk. Thus, in an embodiment, the vehicle security system 330B may implement, for example, response measures such as performing a security sweep of one or more systems and / or components of the vehicle 200 (such as the vehicle software and the hardware infrastructure system 320M), response measures of sending invalidation commands and / or command checks to the systems and / or components of the vehicle 200, response measures of disconnecting the connections of the systems and / or components of the vehicle 200, response measures of shutting down the vehicle 200, response measures of shutting down the components of the vehicle 200, response measures of shutting down the systems of the vehicle 200, and / or other response measures.

[0087] In an embodiment, the vehicle control system 330 may include a vehicle user interface display 330C, and the vehicle user interface display 330C may provide a graphical display to the driver 202 and / or another passenger of the vehicle 200. Thus, in an embodiment, the vehicle user interface display 330C may implement, for example, response measures such as warning the driver 202 of the vehicle 200 by displaying a visual warning and / or playing an audio warning (for example, via the speakers of the vehicle 200 and / or the vehicle user interface display 330C), and / or other response measures.

[0088] In an embodiment, the vehicle control system 330 may include a vehicle acceleration controller 330D. In an embodiment, the vehicle acceleration controller 330D may include a system for controlling the acceleration of the vehicle 200, for example, including an accelerator pedal, an engine, and / or a system for controlling these operations of the vehicle 200. Therefore, in an embodiment, the vehicle acceleration controller 330D may implement, for example, a response measure for shutting off the engine or other components of the vehicle 200, a response measure for shutting off the connection of the vehicle acceleration controller 330D (and / or its system and / or components) to the automatic driving system 330G, a response measure for receiving command checks and / or invalid commands, and / or other response measures.

[0089] In an embodiment, the vehicle control system 330 may include a vehicle brake controller 330E. In an embodiment, the vehicle brake controller 330E may include a system for controlling the brakes of the vehicle 200, for example, including a brake pedal, a drum brake, a parking brake, a parking brake stick, and / or a system for controlling these operations of the vehicle 200. Therefore, in an embodiment, the vehicle brake controller 330E may implement, for example, a response measure for shutting off the parking brake or other components of the vehicle 200, a response measure for shutting off the connection of the vehicle brake controller 330E (and / or its system and / or components) to the automatic driving system 330G, a response measure for receiving command checks and / or invalid commands, and / or other response measures.

[0090] In an embodiment, the vehicle control system 330 may include a vehicle steering controller 330F. In an embodiment, the vehicle steering controller 330F may include a system for controlling the steering of the vehicle 200, which includes, for example, a steering wheel, an axle connection wheel of the vehicle 200, and / or their control systems, and / or a system for controlling these operations of the vehicle 200. Therefore, in an embodiment, the vehicle steering controller 330F may implement, for example, response measures for blocking the steering wheel or other components of the vehicle 200, response measures for blocking the connection of the vehicle steering controller 330F (and / or its system and / or components) to the autonomous driving system 330G, response measures for receiving command checks and / or invalid commands, and / or other response measures.

[0091] In an embodiment, the vehicle control system 330 may include an autonomous driving system 330G, and the autonomous driving system 330G may include a combination of software and / or hardware configured to drive and / or otherwise operate the vehicle 200 on behalf of or in addition to the driver 202 (e.g., as a driving assistance function). Therefore, in an embodiment, the autonomous driving system 330G may implement, for example, response measures for blocking the autonomous driving system 330G, response measures for invalidating commands of the autonomous driving system 330G (for any, some, or all of, for example, the vehicle mode controller 330A, the vehicle acceleration controller 330D, the vehicle brake controller 330E, and / or the vehicle steering controller 330F), response measures for receiving command checks and / or invalid commands, and / or other response measures.

[0092] In an embodiment, the vehicle control system 330 may include a pre-collision system 330H. The pre-collision system 330H is configured to fully or partially control components and / or systems of the vehicle 200 (e.g., any, some, or all of the vehicle mode controller 330A, the vehicle acceleration controller 330D, the vehicle brake controller 330E, and / or the vehicle steering controller 330F) in a situation where the pre-collision system 330H determines (e.g., via the external vehicle sensor 320L) that the vehicle 200 is about to collide with an external object (e.g., an oncoming vehicle 230), and thus prevent and / or mitigate damage from the impending collision (e.g., by actuating the brakes of the vehicle 200). Accordingly, in an embodiment, the pre-collision system 330H may implement response measures such as, for example, cutting off the pre-collision system 330H, invalidating commands of the pre-collision system 330H (e.g., for any, some, or all of the vehicle mode controller 330A, the vehicle acceleration controller 330D, the vehicle brake controller 330E, and / or the vehicle steering controller 330F), receiving command checks and / or invalid commands, and / or other response measures.

[0093] As described above, in an embodiment, the sensors and / or systems of the vehicle detection system 320 may perform functions other than providing monitoring inputs. In an embodiment, any, some, or all of the systems and / or sensors of the vehicle detection system 320 may also be included in the vehicle control system 330. For example, in an embodiment, the location sensor 320E may be included in the vehicle control system 330 and may implement response measures such as sending a warning to a third party (e.g., sending the location of the vehicle 200 to emergency services).

[0094] In an embodiment, an endangered vehicle may also be detected by comparing the monitoring input to a driver profile. As used herein, the term "driver profile" refers to a data structure constructed by logging the behavior of a (human or automated) driver of a vehicle (e.g., driver 202 and / or automated driving system 330G) that can be used to predict the common behavior of the driver. In an embodiment, a driver profile may include logging an input (e.g., by vehicle computing device 310 and / or remote computing device 350) (e.g., any, some, or all of the monitoring inputs described above), and using the logged input to create a baseline against which a driver's future driving behavior can be compared to determine whether the driver's behavior (e.g., by operating in a manner similar to how the driver has driven in the past) matches the driver profile. For example, a driver profile may include an acceleration profile that can be used to predict how a driver accelerates (e.g., how quickly a driver can accelerate when a red light at which the driver's vehicle has stopped turns green) in a context similar to that in which data was logged to create the driver profile. In an embodiment, a driver profile may include data regarding any, some, or all of a driver's acceleration history (e.g., an "acceleration profile"), a driver's braking history (e.g., a "braking" profile), a driver's steering history (e.g., a "steering profile") (e.g., including lateral acceleration, rotational smoothness, and / or turning radius), and / or other data. In an embodiment, a driver profile may include data regarding a driver's behavior at specific coordinates (e.g., a stop sign where the driver decelerates uniformly rather than coming to a complete stop at a lamp, on a lamp, etc.).

[0095] In an embodiment, a driver profile can be generated by verifying a particular driver to determine who or what is driving the vehicle, for example, by using biometric analysis (e.g., identifying the face, voice, or fingerprint of driver 202), requiring the driver to manually identify himself / herself (e.g., via user interface vehicle system 320J), detecting the driver in other cases (e.g., via a specific key used to operate vehicle 200 and / or a connection of the driver's device, such as a phone, to vehicle 200 via Bluetooth®), and / or making an empirical determination of a particular driver (e.g., via comparison of monitored inputs to previously logged inputs and / or monitoring of predicted vehicle trajectory 220) (e.g., logging when autonomous driving system 330G is driving vehicle 200, logging when driver 202 is driving vehicle 200, logging when another regular driver of vehicle 200 is driving vehicle 200). Thus, in an embodiment, a driver profile can predict and / or analyze a driver's behavior, for example, by generating a standard deviation of driver inputs and / or actions, a confidence interval (e.g., a Bayesian confidence interval) associated with monitored driver inputs, and / or a detected vehicle trajectory when compared to the driver profile. Further, in an embodiment, a driver profile can be used to determine that the human driver of the vehicle is not the driver having the existing driver profile (instead, for example, is a carjacker of the vehicle). In an embodiment, a driver profile can be a digital twin of the driver stored in vehicle data memory component 311 and / or remote data memory component 351 in a particular such embodiment.

[0096] Here, referring to FIG. 7, in an embodiment, a method 700 for detecting an endangered vehicle and responding to the detection of the endangered vehicle may include blocks 110, 120, 130, 140, 150, 160, 170 of method 100, and further or alternatively may include additional blocks related to the analysis of the driver profile (in an embodiment, including any, some, or all of blocks 145, 155, 165, as described in more detail below). In an embodiment, any, some, or all of blocks 145, 155, 165 may occur simultaneously with, before, or after any, some, or all of blocks 110, 120, 130, 140, 150, 160. In an embodiment, method 700 may not include any, some, or all of blocks 110, 120, 130, 140, 150, 160.

[0097] Accordingly, in an embodiment, block 145 of method 700 includes comparing a detected vehicle trajectory (e.g., any, some, or all of detected vehicle trajectories 222, 224, 226, 228), at least one of the monitoring inputs (e.g., at least one monitoring input of block 110), or both with the driver profile (e.g., of driver 202 and / or autonomous driving system 330G). For example, in an embodiment, the third detected vehicle trajectory 226 may be compared with the driver profile of driver 202. In an embodiment, the detected vehicle trajectory, the monitoring input, or both may be compared with the driver profile by vehicle computing device 310 and / or remote computing device 350.

[0098] In an embodiment, block 155 of method 700 includes determining a driver profile deviation value for a driver profile and for at least one of a detected vehicle trajectory (e.g., any, some, or all of detected vehicle trajectories 222, 224, 226, 228), monitoring inputs (e.g., of block 110), or both. In an embodiment, determining a driver profile deviation value for a driver profile may include comparing at least one of a detected vehicle trajectory (e.g., any, some, or all of detected vehicle trajectories 222, 224, 226, 228), monitoring inputs (e.g., of block 110), or both to one or more of, for example, a standard deviation predicted by the driver profile, a confidence interval of the driver profile, and / or other content of the driver profile. In an embodiment, the driver profile deviation value may have a magnitude (e.g., on a scale of 1 to 10), and the magnitude of the driver profile deviation value indicates the degree to which the detected vehicle trajectory, the monitoring input, or both deviate from the trajectory and / or input predicted and / or expected by the driver profile. In an embodiment, the driver profile deviation value may be determined by vehicle computing device 310 and / or remote computing device 350.

[0099] For example, referring to FIG. 5C, in an embodiment, the acceleration profile of the driver profile of driver 202 may predict that driver 202 accelerates vehicle 200 such that vehicle 200 follows predicted vehicle trajectory 220. However, in the same example, comparing a third detected vehicle trajectory 226 to predicted vehicle trajectory 220 may indicate that vehicle 200 is accelerating more slowly than predicted by the driver profile of driver 202 (as indicated by the shorter distance of the third detected vehicle trajectory 226 when compared to predicted vehicle trajectory 220). Thus, in the same example, the difference between predicted vehicle trajectory 220 and the third detected vehicle trajectory 226 may increase the magnitude of the driver profile deviation value for the third detected vehicle trajectory 226 and the driver profile.

[0100] Referring again to FIG. 7, block 165 of method 700 includes generating a response measure in response to determining that a driver profile deviation value exceeds a profile deviation threshold. In an embodiment, the driver profile deviation value can be determined to exceed the profile deviation threshold by either or both of vehicle input monitor 313 of vehicle computing device 310 and remote vehicle input monitor 353 of remote computing device 350. In an embodiment, the response measure can be generated by either or both of vehicle input monitor 313 of vehicle computing device 310 and remote vehicle input monitor 353 of remote computing device 350. In an embodiment, one response measure can be generated at block 165. In an embodiment, multiple response measures can be generated at block 165. In an embodiment, the response measures generated at block 165 can include any, some, or all of the response measures described above. In an embodiment, after generating the response measure at block 165, the response measure can be implemented as described above with reference to block 170 (e.g., via vehicle control system 330 and / or remote computing device 350).

[0101] Here, it should be understood that the present disclosure relates to various embodiments of a method, a system, and a non-transitory computer-readable medium for detecting an endangered vehicle and responding to the detection of the endangered vehicle, and in particular for implementing response measures for dealing with the endangered vehicle by monitoring inputs and / or detection trajectories of the endangered vehicle.

[0102] Although specific embodiments have been shown and described herein, it should be understood that various other changes and modifications can be made without departing from the spirit and scope of the claimed subject matter. Further, while various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. Accordingly, the appended claims are intended to encompass all such changes and modifications that are within the scope of the claimed subject matter.

Claims

1. 1. A method for detecting a compromised vehicle and responding to the detection of the compromised vehicle, the method comprising: receiving, by a computing device, one or more monitoring inputs of a vehicle; predicting, by the computing device, a predicted vehicle trajectory using at least one of the monitored inputs; Detecting a detected vehicle trajectory by a vehicle detection system; comparing, by the computing device, the predicted vehicle trajectory to the detected vehicle trajectory; determining, by the computing device, a trajectory deviation value for the predicted vehicle trajectory and the detected vehicle trajectory; generating, by the computing device, a response action in response to determining that the orbit deviation value exceeds an orbit deviation threshold; implementing the response action using the computing device, the vehicle, or both; and A method comprising:

2. The method of claim 1 , wherein the vehicle comprises an automobile.

3. The method of claim 1 , wherein the one or more supervisory inputs are received from the vehicle detection system, a driver of the vehicle, a remote computing device, an automated driving system of the vehicle, or a combination thereof.

4. 2. The method of claim 1, wherein the one or more monitored inputs comprise at least one of a pedal throttle pattern input, a braking pattern input, a linear acceleration input, a lateral acceleration input, a turning smoothness input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or combinations thereof.

5. The method comprises: comparing, by the computing device, at least one of the monitored inputs, the detected vehicle trajectory, or both, to a driver profile; determining, by the computing device, the driver profile and a driver profile deviation value related to at least one of the detected vehicle trajectory, the monitored inputs, or both; generating, by the computing device, the response action in response to determining that the driver profile deviation value exceeds a profile deviation threshold; The method of claim 1 further comprising:

6. The method of claim 5 , wherein the driver profile is generated from past inputs by a driver of the vehicle, an automated driving system of the vehicle, or a combination thereof.

7. 2. The method of claim 1, wherein the response action comprises at least one of a security sweep, a disconnect, a warning to an operator of the vehicle, a warning to a third party, a disconnect to the vehicle, a disconnect to a component of the vehicle, a disconnect to a system of the vehicle, an override command, a check command, or a combination thereof.

8. 1. A system for detecting a compromised vehicle and responding to the detection of the compromised vehicle, the system comprising: a vehicle detection system for a vehicle; and a computing device comprising a memory component, the memory component storing logic that, when executed by the computing device, causes the system to do at least the following: receiving one or more monitoring inputs of the vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; detecting a detected vehicle trajectory by the vehicle detection system; comparing the predicted vehicle trajectory to the detected vehicle trajectory; determining a trajectory deviation value for the predicted vehicle trajectory and the detected vehicle trajectory; generating a response action in response to determining that the off-track value exceeds an off-track threshold; implementing the response action using the computing device, the vehicle, or both, the vehicle, or both; A system that allows the user to:

9. The system of claim 8 , wherein the vehicle comprises an automobile.

10. 10. The system of claim 8, wherein the one or more supervisory inputs are received from the vehicle detection system, a driver of the vehicle, a remote computing device, an automated driving system of the vehicle, or a combination thereof.

11. 9. The system of claim 8, wherein the one or more monitored inputs comprise at least one of a pedal throttle pattern input, a braking pattern input, a linear acceleration input, a lateral acceleration input, a turning smoothness input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or combinations thereof.

12. The system comprises at least the following: comparing at least one of the detected vehicle trajectory or the monitored inputs, or both, to a driver profile; determining a driver profile and a driver profile deviation value related to at least one of the detected vehicle trajectory, the monitored inputs, or both; generating said response action when said driver profile deviation value exceeds a profile deviation threshold; The system of claim 8 , further comprising:

13. 13. The system of claim 12, wherein the driver profile is generated from past inputs by a driver of the vehicle, an automated driving system of the vehicle, or a combination thereof.

14. 9. The system of claim 8, wherein the response action comprises at least one of a security sweep, a disconnect, a warning to an operator of the vehicle, a warning to a third party, a disconnect to the vehicle, a disconnect to a component of the vehicle, a disconnect to a system of the vehicle, an override command, a check command, or a combination thereof.

15. 1. A non-transitory computer readable medium for detecting a compromised vehicle and for responding to the detection of the compromised vehicle, the non-transitory computer readable medium storing logic that, when executed by a computing device, causes the computing device to do at least the following: Receiving one or more monitoring inputs of a vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; Detecting a detected vehicle trajectory by a vehicle detection system; comparing the predicted vehicle trajectory to the detected vehicle trajectory; determining a trajectory deviation value for the predicted vehicle trajectory and the detected vehicle trajectory; generating a response action in response to determining that the off-track value exceeds an off-track threshold; implementing the response action using the computing device, the vehicle, or both; and A computer-readable medium for causing

16. 16. The computer-readable medium of claim 15, wherein the one or more supervisory inputs are received from the vehicle detection system, a driver of the vehicle, a remote computing device, an automated driving system of the vehicle, or a combination thereof.

17. 16. The computer-readable medium of claim 15, wherein the one or more monitored inputs comprise at least one of a pedal throttle pattern input, a braking pattern input, a linear acceleration input, a lateral acceleration input, a turning smoothness input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a user interface vehicle system input, or combinations thereof.

18. The system comprises at least the following: comparing at least one of the detected vehicle trajectory or the monitored inputs, or both, to a driver profile; determining a driver profile and a driver profile deviation value related to at least one of the detected vehicle trajectory, the monitored inputs, or both; generating the response action in response to determining that the driver profile deviation value exceeds a profile deviation threshold; The computer readable medium of claim 15 , further comprising:

19. 20. The computer-readable medium of claim 18, wherein the driver profile is generated from past inputs by a driver of the vehicle, an automated driving system of the vehicle, or a combination thereof.

20. 16. The computer-readable medium of claim 15, wherein the response action comprises at least one of a security sweep of the vehicle, disconnecting the vehicle, warning an operator of the vehicle, warning a third party, disconnecting the vehicle, disconnecting a component of the vehicle, disconnecting a system of the vehicle, an override command, a check command, or a combination thereof.

Images