Detecting and responding to detection of a detected vehicle

Abstract

The invention discloses detecting a damaged vehicle and responding to the detection of the damaged vehicle. A method for detecting a damaged vehicle and responding to the detection of the damaged vehicle includes: receiving one or more monitored inputs of the vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; detecting a detected vehicle track; comparing the predicted vehicle trajectory with the detected vehicle trajectory; determining a trajectory deviation value between the predicted vehicle trajectory and the detected vehicle trajectory; generating a responsive action in response to determining, by the computing device, that the trajectory deviation value exceeds the trajectory deviation threshold; and realizing a response action.

Description

Technical Field

[0001] The present disclosure relates generally to detecting and responding to detection of a compromised vehicle, and more particularly to methods, systems, and non-transitory computer-readable media for detecting a compromised vehicle and implementing responsive actions to handle the compromised vehicle. Background Art

[0002] Modern vehicles are increasingly connected to wireless networks and rely on software and electronic components for vehicle operation. In addition, vehicles increasingly incorporate automated driving features and digitization of the vehicle's driving process (e.g., acceleration of the vehicle is achieved by an electronic signal generated by pressing an accelerator pedal, rather than a strictly manual process of accelerating the engine). However, such connection and use of software and electronic components introduces the risk of the vehicle being compromised (e.g., by hackers or malicious hardware or software), which may result in partial or full control of the vehicle's systems being usurped (e.g., "compromised") by a malicious third party. In such a scenario, the driving of the compromised vehicle may be partially or fully controlled by a third party (rather than by, for example, the driver or the vehicle's automated driving system). In addition, the third party may hide the compromised nature of the vehicle such that the driver of the vehicle and / or the vehicle's safety systems may not notice or detect the change in control of the vehicle's systems.

[0003] Therefore, there is a need for systems, methods, and non-transitory computer-readable media that analyze a vehicle's driver behavior and the vehicle's trajectory to detect whether the vehicle has been compromised and is being controlled by a third party. Summary of the invention

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

[0005] The second aspect A2 comprises the method according to the first aspect A1, wherein the vehicle may comprise a car.

[0006] A third aspect A3 comprises a method according to the first aspect A1 or the second aspect A2, wherein the one or more monitored inputs may be received from a vehicle sensing system, a driver of the vehicle, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

[0007] A fourth aspect A4 includes a method according to any one of aspects A1-A3, wherein the one or more monitored inputs may include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering smoothness input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a user-interfacing vehicle system input, or a combination thereof.

[0008] The fifth aspect A5 includes a method according to any one of aspects A1-A4, wherein the method may also include at least any one of the following items: a computing device compares at least one of the monitored inputs, a detected vehicle trajectory, or both with a driver profile; a computing device determines a driver profile deviation value between the driver profile and the detected vehicle trajectory, at least one of the monitored inputs, or both; and in response to the computing device determining that the driver profile deviation value exceeds a profile deviation threshold, generates a response action.

[0009] A sixth aspect A6 comprises the method according to aspect A5, wherein the driver profile can be generated from historical inputs of the driver of the vehicle, the vehicle's autonomous driving system, or a combination thereof.

[0010] A seventh aspect A7 includes a method according to any of aspects A1-A6, wherein the response action may include at least one of a security scan, a disconnection of a connection, an alert to a driver of the vehicle, an alert to a third party, a shut-off of the vehicle, a shut-off of a component of the vehicle, a shut-off of a system of the vehicle, an override instruction, an instruction check, or a combination thereof.

[0011] According to aspect 8 B1, a system for detecting a damaged vehicle and responding to the detection of a damaged vehicle may include a vehicle sensing system of the vehicle and a computing device including a memory component, wherein the memory component stores logic that, when executed by the computing device, causes the system to perform at least the following operations: receiving one or more monitored inputs of the vehicle; using at least one of the monitored inputs to predict a predicted vehicle trajectory; detecting the detected vehicle trajectory by the vehicle sensing system; comparing the predicted vehicle trajectory with the detected vehicle trajectory; determining a trajectory deviation value between the predicted vehicle trajectory and the detected vehicle trajectory; generating a response action in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold; and implementing the response action using the computing device, the vehicle, or both.

[0012] A ninth aspect B2 includes the system according to the eighth aspect B1, wherein the vehicle may include a car.

[0013] Aspect B3 includes a system according to Aspect B1 of Aspect B2 or Aspect B2, wherein the one or more monitored inputs can be received from a vehicle sensing 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 a system according to any of aspects B1-B3, wherein the one or more monitored inputs may include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering smoothness input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a vehicle system input of a user interface, or a combination thereof.

[0015] Aspect 12 B5 includes a system according to any one of aspects B1-B4, wherein the system may also perform at least any one of the following items: comparing the detected vehicle trajectory, at least one of the monitored inputs, or both with the driver profile; determining a driver profile deviation value between the driver profile and the detected vehicle trajectory, at least one of the monitored inputs, or both; and generating a response action when the driver profile deviation value exceeds a profile deviation threshold.

[0016] Aspect B6 of the thirteenth aspect includes a system according to aspect B5, wherein the driver profile can be generated from historical input from the driver of the vehicle, the vehicle's autonomous driving system, or a combination thereof.

[0017] A fourteenth aspect B7 includes a system according to any one of aspects B1 to B6, wherein the response action may include at least one of a security scan, a shutdown of a connection, an alarm to a driver of the vehicle, an alarm to a third party, a shutdown of the vehicle, a shutdown of a component of the vehicle, a shutdown of a system of the vehicle, an override command, a command check, or a combination thereof.

[0018] According to aspect C1 of the fifteenth aspect, a non-transitory computer-readable medium for detecting a damaged vehicle and responding to the detection of a damaged vehicle may store logic that, when executed by a computing device, causes the computing device to perform at least the following operations: receiving one or more monitored inputs of the vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; detecting the detected vehicle trajectory by a vehicle sensing system; comparing the predicted vehicle trajectory with the detected vehicle trajectory; determining a trajectory deviation value between the predicted vehicle trajectory and the detected vehicle trajectory; generating a response action in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold; and implementing the response action using the computing device, the vehicle, or both.

[0019] Aspect C2 of the sixteenth aspect includes a non-transitory computer-readable medium according to Aspect C1 of the fifteenth aspect, wherein the one or more monitored inputs may be received from a vehicle sensing system, a driver of the vehicle, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

[0020] Aspect seventeen C3 includes a non-transitory computer-readable medium according to aspect fifteen C1 or aspect sixteen C2, wherein the one or more monitored inputs may include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering smoothness input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a vehicle system input of a user interface, or a combination thereof.

[0021] Aspect C4 of the eighteenth aspect includes a non-transitory computer-readable medium according to any one of aspects C1-C3, wherein the system may also perform at least any one of the following items: comparing the detected vehicle trajectory, at least one of the monitored inputs, or both with the driver profile; determining a driver profile deviation value between the driver profile and the detected vehicle trajectory, at least one of the monitored inputs, or both; and generating a response action in response to determining that the driver profile deviation value exceeds a profile deviation threshold.

[0022] Aspect C5 of the nineteenth aspect includes a non-transitory computer-readable medium according to Aspect C4 of the eighteenth aspect, wherein a driver profile can be generated from historical inputs from a driver of the vehicle, an autonomous driving system of the vehicle, or a combination thereof.

[0023] Aspect C6 of the twentieth aspect includes a non-transitory computer-readable medium according to any of aspects C1-C5, wherein the response action may include at least one of a security scan of the vehicle, a disconnection of a connection of the vehicle, an alert to the driver of the vehicle, an alert to a third party, a disconnection of the vehicle, a disconnection of a component of the vehicle, a disconnection of a system of the vehicle, an override command, a command check, or a combination thereof.

[0024] A twenty-first aspect C7 includes the non-transitory computer-readable medium according to any one of aspects C1-C6, wherein the vehicle may be a car.

[0025] Other features and advantages of the various aspects described herein will be set forth in the following detailed description, and for those skilled in the art, some features and advantages will be readily apparent from the detailed description, or will be recognized by practicing the various aspects described herein, including the following detailed description, claims, and 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 character of the claimed subject matter. The accompanying drawings are included to provide a further understanding of the various aspects and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate various aspects described herein and, together with the description, serve to explain the principles and operation of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The embodiments illustrated in the drawings are illustrative and exemplary in nature and are not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments may be understood when read in conjunction with the accompanying drawings, wherein like structures are represented by like reference numerals, and wherein:

[0028] Figure 1 is a flow chart of a first method for responding to detection of a compromised vehicle in accordance with one or more embodiments shown and described herein;

[0029] Figure 2 depicts a vehicle traversing a roadway in accordance with one or more embodiments shown and described herein;

[0030] Figure 3A schematically depicts hardware for detecting a compromised vehicle and for implementing responsive actions according to one or more embodiments shown and described herein;

[0031] Figure 3Bschematically depicts hardware for detecting a compromised vehicle and for implementing responsive actions according to one or more embodiments shown and described herein;

[0032] Figure 4 depicts a vehicle crossing a road and a predicted vehicle trajectory of the vehicle according to one or more embodiments shown and described herein;

[0033] Figure 5A depicts a vehicle crossing a road, a predicted vehicle trajectory of the vehicle, and a first detected vehicle trajectory of the vehicle according to one or more embodiments shown and described herein;

[0034] Figure 5B depicts a vehicle crossing a road, a predicted vehicle trajectory of the vehicle, and a second detected vehicle trajectory of the vehicle in accordance with one or more embodiments shown and described herein;

[0035] Figure 5C depicts a vehicle crossing a road, a predicted vehicle trajectory of the vehicle, and a third detected vehicle trajectory of the vehicle in accordance with one or more embodiments shown and described herein;

[0036] Figure 5D depicts a vehicle crossing a road, a predicted vehicle trajectory of the vehicle, and a third detected vehicle trajectory of the vehicle in accordance with one or more embodiments shown and described herein;

[0037] Figure 6 schematically depicts hardware for implementing a response action according to one or more embodiments shown and described herein; and

[0038] Figure 7 is a flow chart of a second method for responding to detection of a compromised vehicle in accordance with one or more embodiments shown and described herein. DETAILED DESCRIPTION

[0039] Due to, for example, the vehicle's connection to wireless networks, the vehicle's reliance on software and electronic components, and the incorporation of autonomous driving systems in vehicle systems, the vehicle (and / or its systems and / or components) may be compromised, for example, by a malicious third party or software. When the vehicle is compromised, the vehicle may still operate normally or appear to operate normally, even if an intervening malicious third party or software has taken control of the vehicle (and / or its systems and / or components). Although the vehicle may be equipped with a safety system to prevent damage to the vehicle, the safety system may still have weaknesses that allow a malicious third party or software to damage the vehicle (and / or its systems and / or components), and therefore a mechanism for determining whether the vehicle has been compromised based on observation and for generating response actions accordingly may still provide a safety mechanism against the risk of the vehicle being compromised.

[0040] It should be understood that, as described herein, a "component" of a vehicle is a physical device, sensor, device, and / or any other functional component of a vehicle. For example, in an embodiment, a component of a vehicle may include an accelerator pedal, a brake pedal, brakes, an engine, a steering wheel, wheels, an air conditioning and / or heating unit, windshield wipers, interior and / or exterior lights, manual controls (e.g., knobs, buttons, and / or levers) for other components, a vehicle user display, and / or any other functional physical component of a vehicle.

[0041] It should be understood that, as described herein, a "system" of a vehicle is a digital infrastructure, physical infrastructure, and / or combination thereof that provides one or more functions to the vehicle 200. For example, in an embodiment, a system of a vehicle may include a vehicle safety system, a vehicle autonomous driving system, a digital user interface, a vehicle software infrastructure, and / or a combination of components that serve a specified function (e.g., an air conditioning system including manual controls or interfaces, an air conditioning unit, and / or other software and / or hardware that provides air conditioning functions).

[0042] 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, for example, the driver of the vehicle or the vehicle's autonomous driving system. That is, a vehicle that is partially or fully controlled by a third party without the desire or intent of any, some, or all of the vehicle driver and / or the vehicle's autonomous driving system.

[0043] When a vehicle is compromised, the compromised nature of the vehicle may not immediately become apparent to the driver or safety systems of the vehicle. For example, a malicious third party or software may still allow the vehicle to operate normally or replicate the normal functions of the vehicle (e.g., by replicating electrical signals generated by components of the vehicle, such as the signal generated by the accelerator pedal when depressed by the driver) to provide the illusion that the vehicle is not compromised. Moreover, as the development and use of autonomous driving systems increases, the compromised nature of the vehicle may be further obscured to the driver because 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.

[0044] 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 full or partial control by a human driver. That is, an autonomous driving system is a collection of software, hardware, or any combination thereof that enables partial or full control of the driving of a vehicle to be managed by the system rather than the driver, for example, to assist the driver in driving the vehicle or to be fully or partially responsible for driving the vehicle.

[0045] Thus, the embodiments described herein may provide methods, systems, and non-transitory computer-readable media that enable identification of the nature of a vehicle's tampering even if the vehicle's safety systems fail to prevent the vehicle from being tampered with and / or identify that the vehicle has been tampered with.

[0046] Reference will now be made in detail to various embodiments of methods, systems, and non-transitory computer-readable media for detecting and responding to the detection of a compromised vehicle, particularly for detecting a compromised vehicle by monitoring inputs to the compromised vehicle and / or a detected trajectory and implementing a response action to handle the compromised vehicle. Specifically, in an embodiment, the method described herein includes receiving one or more monitored inputs of a vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; detecting the detected vehicle trajectory; comparing the predicted vehicle trajectory to the detected vehicle trajectory; determining a trajectory deviation value between the predicted vehicle trajectory and the detected vehicle trajectory; generating a response action in response to determining that the trajectory deviation value exceeds a trajectory deviation threshold; and implementing the response action.

[0047] Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from a particular value and / or to another particular value. Similarly, when a value is expressed as an approximation by using the antecedent "about," it is understood that the particular value forms another embodiment. It is also understood that the endpoints of each range are meaningful relative to and independent of the other endpoints.

[0048] Directional terms used herein, such as up, down, right, left, front, back, top, and bottom, are only described with reference to the drawings and do not mean absolute directions.

[0049] Unless otherwise expressly stated, it is not intended that any method set forth herein be interpreted as requiring that its steps be performed in a particular order, nor as requiring any particular orientation of the apparatus. Thus, in the absence of a method claim actually reciting an order in which its steps are to be followed, or any apparatus claim actually reciting an order or orientation of individual components, or in the absence of other specific recitations in the claims or specification that the steps are to be limited to a particular order, or in the absence of a particular order or orientation of components of the apparatus, no order or orientation is intended to be inferred in any respect. This applies to any possible non-explicit basis for interpretation, including: matters of logic regarding the arrangement of steps, operational flow, order of components, or orientation of components; plain meaning derived from grammatical organization or punctuation, and the number or type of embodiments described in the specification.

[0050] 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 dictates otherwise.

[0051] Reference now Figure 1-Figure 2 , a method 100 for responding to detection of a compromised vehicle includes a block 110 of receiving one or more monitored inputs of a vehicle 200. Figure 2 In the embodiment of FIG. 2 , vehicle 200 is traveling in lane 213 of road 210. Figure 2 In the embodiment of FIG. 2 , the vehicle 200 is driven by the driver 202, but in other embodiments, the vehicle 200 may be driven by an autonomous driving system of the vehicle 200 (e.g., as described below with reference to FIG. 2 ). Figure 6 In an embodiment, one or more monitored inputs of the vehicle 200 may include inputs from the driver 202, the vehicle 200's autonomous driving system (e.g., as described below with reference to FIG. 3 ), and the autonomous driving system 330G of the vehicle 200. Figure 6 The autonomous driving system 330G of the vehicle 200 described in further detail), the vehicle computing device of the vehicle 200 (e.g., as described below with reference to Figure 3A The vehicle computing device 310 described in further detail), a remote computing device (e.g., as described below with reference to Figure 3B In one embodiment, the vehicle 200 may be a vehicle 200 that is configured to monitor the input of the vehicle 200 and / or any combination thereof. In an embodiment, the one or more monitored inputs of the vehicle 200 may include inputs from any system and / or component of the vehicle 200 that controls or otherwise affects the driving 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 seafaring vessel, an airplane, a helicopter, and / or any other type of vehicle.

[0052] In embodiments, any, some, or all of the at least one monitored input may include a single input (e.g., a change in gear mode of the vehicle 200) or multiple inputs (e.g., several instances of the driver 202 braking in a braking mode) by a user of the vehicle 200 (e.g., the driver 202 and / or the autonomous driving system of the vehicle 200) that indicates a change related to, or otherwise affects and / or determines, the quality, operation, and / or other aspects of the systems, components, and / or functions of the vehicle 200. In embodiments, the monitored input may include data including instructions (e.g., from the driver 202 and / or the autonomous driving system of the vehicle 200) and / or observed behavior of the vehicle 200 related to one or more of pedal throttle mode, braking mode, linear acceleration, lateral acceleration, cornering smoothness, turning radius, operating mode, engine mode, gear mode, vehicle systems of a user interface, any other quality, operation, and / or other aspects of the systems, components, and / or functions of the vehicle 200, or combinations thereof. Thus, in an embodiment, one or more monitored inputs may include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering stability input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a vehicle system input of a user interface, or a combination thereof.

[0053] 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 shift pedal) is depressed and / or released, and / or other data related to the use of the pedal in an instant and / or over time (e.g., by the driver 202 or an autonomous driving system of the vehicle 200). In an embodiment, the braking pattern may be one or more of the braking frequency, braking magnitude, speed, degree, and / or frequency at which a brake pedal of the vehicle 200 is depressed and / or released, and / or other data related to the braking of the vehicle 200 in an instant and / or over time (e.g., by the driver 202 or an autonomous driving system). In an embodiment, the linear acceleration may be one or more of the frequency of acceleration, magnitude of acceleration, rate at which an accelerator pedal of the vehicle 200 is depressed and / or released, and / or other data related to the acceleration of the vehicle 200 in an instant and / or over time (e.g., by the driver 202 or an autonomous driving system). In an embodiment, the turn smoothness may be one or more of the degree of turn, the turn rate (e.g., the change in speed and the rate of change in speed of the vehicle or 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 turn smoothness of the vehicle 200 at an instant and / or over time (e.g., implemented by the driver 202 or the autonomous driving system). In an embodiment, the turn radius may be the radius of the turn that the vehicle 200 turns (under the control of, for example, the driver 202 or the autonomous driving system of the vehicle 200). In an embodiment, the operating mode of the vehicle 200 may be a condition or mode of operation of the vehicle 200 (e.g., as requested and / or set by the driver 202 or the autonomous driving system of the vehicle 200), such as a charging mode (e.g., in which the electric vehicle is connected to a charger and receives energy from the charger), a "sport" mode (e.g., a mode that increases the amount of acceleration for a given level of depression of the accelerator pedal), a "towing" mode (e.g., in which the vehicle is configured to function with a towed device or vehicle's (e.g., electrical) system), or any other operating mode of the vehicle 200. In an embodiment, the engine mode may be a state of the engine of the vehicle 200, such as a "drive" mode in which the vehicle 200 is configured to move forward, a "reverse" mode in which the vehicle 200 is configured to move backward, a "parking" mode in which the parking brake of the vehicle 200 is engaged and the vehicle 200 is configured not to move and / or resist movement, a "neutral" mode in which the engine does not move the vehicle 200, and / or other modes of the engine of the vehicle 200. In an embodiment, the gear mode may be a shift state of a manual or automatic transmission of the vehicle 200, such as first gear, second gear, and / or third gear.In an embodiment, the vehicle systems of the user interface may include components and / or systems of the vehicle 200 that can be controlled by a user of the vehicle 200 (e.g., the driver 202 or a passenger) via control mechanisms (e.g., manual knobs, buttons and / or switches, a touch screen digital interface, and / or voice and / or other non-touch controls), such as an air conditioning system, a heating system, windshield wipers, a radio system, an audio system, windshield wipers and associated windshield wiper speeds and frequencies, and / or any other system, component, or aspect or quality thereof that can be controlled by a user of the vehicle.

[0054] refer to Figure 3A In an embodiment, a system 300 for detecting and responding to detection of a compromised vehicle may include the vehicle 200, components and / or systems of the vehicle 200, and / or hardware (e.g., electrical components and / or manual controls) and / or software that controls and / or operates the vehicle 200 or any of its components or systems and / or performs any, some, or all of the vehicle's data or signal processing, data communications, and / or data or signal output. In an embodiment, the system 300 may include or be coupled to a vehicle computing device 310, a vehicle sensing system 320, and a vehicle control system 330. In an embodiment, the system 300 may provide or receive input data, and generate and implement results of detection of a compromised vehicle and generation of response actions.

[0055] In an embodiment, the vehicle computing device 310 may include or be coupled to a transceiver 312 (which may be configured as a transmitter, a receiver, and / or a transceiver) and / or a vehicle sensing system 320, from which the vehicle computing device 310 may receive one or more monitored inputs of the vehicle 200. Depending on the embodiment, the remote computing device 350 may receive sensor data from tens, hundreds, or even thousands of different hardware located throughout the vehicle 200, or otherwise sense data regarding the driving or operation of the vehicle 200. In an embodiment, the vehicle computing device 310 may include a vehicle data storage component 311 (in Figure 3A denoted as δ in the figure), for storing data corresponding to monitored inputs of the vehicle 200 received (in an embodiment, from the vehicle sensing system 320, and / or in an embodiment, via the transceiver 312). Depending on the specific embodiment, the vehicle data storage component 311 can be configured as a random access memory (RAM), a read-only memory (ROM), a register, a database, and / or other hardware for storing data corresponding to received monitored inputs of the vehicle 200. As such, the vehicle data storage component 311 can be part of a wider memory component, a data storage component, and / or part of other data storage infrastructure of the vehicle computing device 310.

[0056] The vehicle computing device 310 may also include a vehicle input monitor 313 (in Figure 3A 310 ), which in embodiments may represent software operations performed on data corresponding to monitored inputs of the vehicle 200 obtained from either or both of the transceiver 312 and / or the vehicle sensing system 320. The vehicle data memory 311 may store logic (e.g., trajectory deviation logic for calculating trajectory deviation values ​​and / or driver profile deviation logic for calculating driver profile deviation values ​​as described in further detail below) that, when executed by the vehicle input monitor 313, may cause the system 300 to perform any, some, or all of the following: receive one or more monitored inputs of the vehicle 200, predict a predicted vehicle trajectory (e.g., as described in further detail below), Figure 4-5D 2 and described in further detail below), detecting a detected vehicle trajectory (e.g., Figure 5A-5D 222, 224, 226, 228 described in further detail below), the predicted vehicle trajectory is compared with the detected vehicle trajectory (as described below with reference to Figure 1 and Figure 5A-5D ) to compare the predicted vehicle trajectory with the detected vehicle trajectory to determine the trajectory deviation value (as shown in the following reference Figure 1 and Figure 5A-5D ), generating a response action (as described in further detail below) Figure 1 and Figure 3A-3B ), implement the response action (as described in further detail below Figure 1 and Figure 6 ), comparing the detected vehicle trajectory, the monitored input, or both, to the driver profile (as described in further detail below with reference to Figure 5A-5D and Figure 7 ), and / or determining a driver profile deviation value from a detected vehicle trajectory, a monitored input, or both (as described below with reference to Figure 3A-3B and Figure 7 Detailed description further below).

[0057] In an embodiment, the vehicle sensing system 320 is coupled to the vehicle computing device 310 and may include or be coupled to one or more vehicle sensors that detect information about the driving or operation of the vehicle 200. In an embodiment, the vehicle sensing system 320 may include or be coupled to a physical sensor that may detect a manual (e.g., the driver 202 depresses a pedal of the vehicle 200 or turns a steering wheel of the vehicle 200) instruction about the driving or operation of the vehicle 200 received from, for example, the driver 202 or an autonomous driving system of the vehicle 200. In an embodiment, the vehicle sensing system 320 may include or be coupled to a digital sensor that may detect a digital instruction about the driving or operation of the vehicle 200 received from, for example, the driver 202 or an autonomous driving system of the vehicle 200 (e.g., a signal from the autonomous driving system of the vehicle 200 or from the driver 202 operating electrical hardware (such as a pedal or a steering wheel) of the vehicle 200). In an embodiment, the vehicle sensing system 320 may include a sensor for detecting the movement, position, or state of the vehicle 200 or a component or system of the vehicle 200.

[0058] Back to Figure 3A , the vehicle sensing system 320 may include a pedal sensor 320A, which may detect, for example, a pedal throttle mode of a pedal of the vehicle 200 (e.g., when the pedal is depressed by the driver 202), a linear acceleration input, a braking mode, and / or an electrical signal related to the above items. In an embodiment, the pedal sensor 320A may include, for example, a pressure sensor (e.g., for detecting depression of a pedal of the vehicle 200), a position sensor (e.g., for detecting the position of a pedal of the vehicle 200), and / or other sensors.

[0059] In an embodiment, the vehicle sensing system 320 may include an acceleration sensor 320B, which may detect, for example, the speed, linear acceleration, lateral acceleration, and / or braking mode 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 by comparison of the position information of the vehicle 200), and / or other sensors.

[0060] In an embodiment, the vehicle sensing system 320 may include a steering sensor 320C, which may detect, for example, a lateral acceleration input of the vehicle 200 (including, for example, a steering wheel of the vehicle 200), a turning stability input, a lateral acceleration input, and / or electrical signals related to the above items. In an embodiment, the steering sensor 320C may include, for example, a gyroscope (e.g., for detecting the orientation of the steering wheel of the vehicle 200), a pressure sensor (e.g., for detecting the force of turning the steering wheel of the vehicle 200), and / or other sensors.

[0061] In an embodiment, the vehicle sensing system 320 may include wheel sensors 320D that may detect, for example, the speed (e.g., lateral or rotational speed relative to an axle), orientation (e.g., relative to a chassis of the vehicle 200), braking mode, linear acceleration, and / or lateral acceleration of one or more wheels of the vehicle 200. In an embodiment, the wheel sensors 320D may include a tachometer (e.g., for measuring the rotational speed and / or acceleration of a wheel of the vehicle 200), a gyroscope (e.g., for measuring the orientation of a wheel of the vehicle 200), an accelerometer (e.g., for measuring the lateral speed and / or acceleration of a wheel of the vehicle 200), and / or other sensors.

[0062] In an embodiment, the vehicle sensing system 320 may include a position sensor 320E that may detect, for example, the position, speed, braking pattern, linear acceleration, lateral acceleration, turning stability, and / or turning radius of the vehicle 200. In an embodiment, the position sensor 320E may include, for example, a global positioning unit (which may, for example, communicate wirelessly with a satellite or other system to calculate the position of the vehicle 200) and / or other sensors.

[0063] In an embodiment, the vehicle sensing system 320 may include a vehicle operating mode sensor 320F for detecting, for example, an operating mode of the vehicle 200 or an electrical signal regarding the operating mode. In an embodiment, the vehicle operating mode sensor 320F may include, for example, a sensor coupled to a manual vehicle operating mode selector (e.g., a button, knob, or lever for use by the driver 202) and / or other sensors.

[0064] In an embodiment, the vehicle sensing system 320 may include an engine mode sensor 320G for detecting, for example, an engine mode of the vehicle 200 or an electrical signal regarding the engine mode. In an embodiment, the engine mode sensor 320G may include, for example, a sensor coupled to a manual vehicle engine mode selector (e.g., a button, knob, or lever for use by the driver 202), a sensor coupled to the engine of the vehicle 200, and / or other sensors.

[0065] In an embodiment, the vehicle sensing system 320 may include a gear mode sensor 320H for detecting, for example, a gear mode of the vehicle 200 or an electrical signal regarding the gear mode. In an embodiment, the gear mode sensor 320H may include, for example, a sensor configured to determine the position of a gear lever of the vehicle 200 and / or other sensors.

[0066] In an embodiment, the vehicle sensing system 320 may include a vehicle system of user interface 320J for detecting and receiving, for example, instructions regarding the driving or operation of the vehicle 200 (e.g., from the driver 202 or passengers of the vehicle 200) via, for example, a touch screen, voice controls, or other interactive mechanisms. In an embodiment, the vehicle system of user interface 320J may include a digital mechanism, such as, for example, a touch screen, a digital screen, and / or a digital user interface, for controlling various aspects of the vehicle 200, such as, for example, an air conditioning system, a heating system, windshield wipers, a digital user interface, an operating mode, an engine mode, a gear mode, and / or other components, systems, or modes of the vehicle 200. In an embodiment, the vehicle system of user interface 320J may also include sensors for biometric analysis of the driver 202 and / or interaction of the driver 202 with the vehicle 200. For example, in an embodiment, the vehicle system 320J of the user interface may include a camera (for monitoring, for example, 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 on the steering wheel of the vehicle 200 (for monitoring, for example, the grip pressure of the driver 202 on the steering wheel to determine, for example, whether the driver 202 has his or her hands on the steering wheel), and / or other such sensors for monitoring the driver 202.

[0067] In an embodiment, the vehicle sensing system 320 may include a manual user interface system 320K for detecting and receiving, for example, instructions regarding the driving or operation of the vehicle 200 (e.g., from the driver 202 or a passenger of the vehicle 200) via, for example, a manual button, knob, or lever. In an embodiment, the manual user interface system 320K may include a manual mechanism such as, for example, a button, knob, or lever for controlling various aspects of the vehicle 200 (such as, for example, an air conditioning system, a heating system, windshield wipers, an operating mode, an engine mode, a gear mode, and / or other components, systems, or modes of the vehicle 200).

[0068] In an embodiment, the vehicle sensing system 320 may include external vehicle sensors 320L for detecting, for example, the surrounding environment of the vehicle 200. In an embodiment, the external vehicle sensors 320L may include one or more cameras, radar sensors, wireless connection sensors (configured to communicate with other vehicles or devices, for example, via, for example, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth, Zigbee, other wireless connection systems, etc.), and / or other sensors. In an embodiment, the external vehicle sensors 320L may be used to determine or generate data that can be used to determine the actual travel of the vehicle 200 by comparing data about the surrounding environment of the vehicle 200 over time (e.g., linear acceleration data, lateral acceleration data, braking pattern data, turning stability data, turning radius data, etc.). In an embodiment, the external vehicle sensors 320L may be used by the autonomous driving system of the vehicle 200 to drive the vehicle 200 (in whole or in part).

[0069] In an embodiment, the vehicle sensing system 320 may include a vehicle software and hardware infrastructure system 320M that may provide, generate, and / or receive signals from any, some, or all of the sensors and / or systems of the vehicle sensing system 320 as monitored inputs. In an embodiment, the vehicle software and hardware infrastructure system 320M may include software for operating (partially and / or in whole) any, some, or all of the sensors and / or systems of the vehicle sensing system 320 and / or components and / or systems of the vehicle 200 and processing instructions therefrom. 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 for generating, sending, or receiving signals, data, inputs, or outputs.

[0070] In an embodiment, the vehicle sensing system 320 may include any, some, or all of a pedal sensor 320A, an acceleration sensor 320B, a steering sensor 320C, a wheel sensor 320D, a position sensor 320E, a vehicle operating mode sensor 320F, an engine mode sensor 320G, a gear mode sensor 320H, a vehicle user interface system 320J, a manual user interface system 320K, an external vehicle sensor 320L, a vehicle software and hardware infrastructure system 320M, and / or other sensors, components, or systems. Thus, in an embodiment, each of the one or more monitored inputs may be generated by any, some, or all of the sensors and systems of the vehicle sensing system 320, and in a manner such as Figure 3A In embodiments where some or all of the sensors and systems of vehicle sensing system 320 are depicted in FIG. 1 , at least one monitored input may be received by only one, only some, or all of the sensors of vehicle sensing system 320 .

[0071] As will be appreciated by one of ordinary skill in the art, in embodiments, the vehicle sensing system 320 may include any other sensors and / or systems that may provide relevant monitored inputs for use in the method 100. Furthermore, in embodiments, in addition to providing monitored inputs for the vehicle 200, the sensors and / or systems of the vehicle sensing system 320 may also provide other functionality for the vehicle 200 or enable other uses for the vehicle 200, and thus, in certain such embodiments, any component and / or system of the vehicle 200 may provide the monitored inputs in block 110 of the method 100.

[0072] Reference again Figure 1 and Figure 3A In an embodiment, the vehicle computing device 310 may receive any, some, or all of the one or more monitored inputs of the block 110 from the vehicle sensing system 320. However, in an embodiment, the vehicle computing device 310 may receive any, some, or all of the one or more monitored inputs of the block 110 from a source external to the vehicle computing device 310 and / or the vehicle 200 via, for example, the transceiver 312.

[0073] refer to Figure 3B In an embodiment, the system 300 may include a remote computing device 350 (e.g., a cloud computing system or other computing device), which in an embodiment may be coupled to the vehicle 200, the vehicle computing device 310, the vehicle sensing system 320, and / or the vehicle control system 330 (via, for example, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth, Zigbee, other wireless connection systems, etc.). In an embodiment, the remote computing device 350 may include any, some, or all of the remote data storage component 351, the remote vehicle input monitor 353, and / or the remote vehicle input monitor output converter 354. The vehicle computing device 310, the vehicle sensing system 320, and / or the vehicle control system 330 (e.g., via, for example, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth, Zigbee, other wireless connection systems, etc.) Figure 3A) may alternatively reside within the remote computing device 350, and any, some, or all of the processing of the method 100 and / or performed by the vehicle computing device 310, the vehicle sensing system 320, and / or the vehicle control system 330 may alternatively occur within and / or be performed by the remote computing device 350. Thus, in embodiments, the remote data memory component 351 may perform any, some, or all of the functions of the vehicle data memory component 311 separately, in parallel, and / or in conjunction with the vehicle data memory component 311, and in embodiments, may store any, some, or all of the data and / or logic stored within the vehicle data memory component 311 as described above. In embodiments, the remote vehicle input monitor 353 may perform any, some, or all of the functions of the vehicle input monitor 313 separately, in parallel, and / or in conjunction with the vehicle input monitor 313. In an embodiment, the remote vehicle input monitor output converter 354 may perform any, some, or all of the functions of the vehicle control system 330 separately from, in parallel with, and / or in conjunction with the vehicle control system 330 .

[0074] Therefore, reference Figure 1 and Figure 3BIn block 110, in embodiments, any, some, or all of the one or more monitored inputs may be generated by the vehicle sensing system 320 and received by the vehicle data storage component 311 and / or the remote computing device 350. Further, in embodiments, any, some, or all of the one or more monitored inputs may be generated by the remote computing device 350 and received by the remote data storage component 351 and / or the vehicle data storage component 311 (via, for example, a wireless coupling between a transceiver 352 of the remote computing device 350 and the transceiver 312). In some such embodiments, any, some, or all of the sensors and / or systems of the vehicle sensing system 320 may be included in part or in whole in the remote computing device 350. For example, the position sensor 320E may generate monitored inputs corresponding to, for example, speed, linear acceleration, lateral acceleration, and / or braking mode via processing (e.g., of position data and associated time values ​​associated with the vehicle 200) by the remote computing device 350. In embodiments, generation, processing, receipt, analysis, and / or modification of any, some, or all inputs to vehicle sensing system 320 (including any, some, or all of sensors 320A, 320B, 320C, 320D, 320E, 320F, 320G, 320H, 320L, and / or systems 320J, 320K, 320M) may be performed by remote computing device 350. In certain such embodiments, any such generation, processing, receipt, analysis, and / or modification of inputs may be performed by remote computing device 350 via edge computing (e.g., to offload such necessary computations to remote computing device 350 due to, for example, computational power limitations of vehicle computing device 310).

[0075] Reference again Figure 1 And refer to Figure 4 , block 120 of method 100 includes predicting a predicted vehicle trajectory 220 using at least one monitored input. Figure 4As shown, 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 in the lane 213 as defined by the shoulder line 211 and the center line 212. In an embodiment, the predicted vehicle trajectory 220 may include the predicted vehicle trajectory of the vehicle 200 at a predetermined and / or variable amount of time in the future (e.g., a number of seconds) and / or a predetermined and / or variable distance (e.g., a number of feet, yards, meters, etc.) that the vehicle 200 is to travel (depending on, for example, the content and quality of the data included in at least one monitored input). In an embodiment, the predicted vehicle trajectory 220 may be a projection of the predicted travel distance and direction of the vehicle 200 0.1 seconds in the future, 0.5 seconds in the future, 1 second in the future, 5 seconds in the future, 10 seconds in the future, or any amount of time in the future. In an embodiment, the predicted vehicle trajectory 220 may be a projection of the predicted travel distance and direction of the vehicle 200 1 meter, 10 meters, 100 meters, or any other distance that the vehicle 200 is to travel.

[0076] In an embodiment, the predicted vehicle trajectory 220 may be predicted via monitored inputs generated by the vehicle sensing system 320. For example, in an embodiment, the pedal sensor 320A may generate data including monitored inputs of a pedal throttle pattern input caused by the driver 202 depressing an accelerator pedal of the vehicle 200, and in an embodiment, the pedal throttle pattern input may be received by the vehicle computing device 310 and / or the remote computing device 350 and used to predict the predicted vehicle trajectory 220 by the vehicle input monitor 313 and / or the remote vehicle input monitor 353. In another example, in an embodiment, one or more of the sensors and / or systems of the vehicle sensing system 320 may be used to predict the predicted vehicle trajectory 220, such as, in an embodiment, linear acceleration input data generated by the pedal sensor 320A via the driver 202 depressing the accelerator pedal of the vehicle 200, lateral acceleration input data generated by the steering sensor 320C via instructions generated by the autonomous driving system of the vehicle 200 (e.g., used as a driver assistance system when the driver 202 is driving the vehicle 200), and linear acceleration data generated by a combination of the acceleration sensor 320B (e.g., located in the vehicle 200) and the position sensor 320E (e.g., located in either or both of the vehicle 200 and the remote computing device 350). Thus, in an embodiment, the predicted vehicle trajectory 220 may 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 sensing system 320.

[0077] In embodiments, the predicted vehicle trajectory 220 may be predicted via a neural network, a deep learning algorithm, an optimization algorithm, and / or other AI algorithm, and in certain such embodiments, such a neural network, deep learning algorithm, optimization algorithm, or other AI algorithm may be stored on either or both of the vehicle data storage component 311 and / or the remote data storage component 351 and executed (via, e.g., edge computing) by either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353. In embodiments, the predicted vehicle trajectory may be predicted by a physical simulation algorithm (using, e.g., speed, direction, and rate of change of speed or direction, as determined by monitored inputs generated, e.g., by the vehicle sensing system 320). In certain such embodiments, the physical simulation algorithm may be stored on either or both of the vehicle data storage component 311 and / or the remote data storage component 351 and executed (e.g., via edge computing) by either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353. In an embodiment, the predicted vehicle trajectory 220 may be predicted by a statistical analysis algorithm (using, for example, a driver profile, as described in further detail below) to predict the probability of the driving behavior of the driver 202 and / or the autonomous driving system of the vehicle 200 (based on, for example, past driver behavior, the location of the vehicle 200, and / or the route of the vehicle 200), thereby determining the predicted vehicle trajectory 220 and / or additional predicted vehicle trajectories and / or the associated probability value of each predicted vehicle trajectory. In some such embodiments, the statistical analysis algorithm may be stored on either or both of the vehicle data storage component 311 and / or the remote data storage component 351 and executed by either or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353 (e.g., via edge computing).

[0078] In embodiments, either or both of the vehicle computing device 310 and / or the remote computing device 350 may generate multiple predicted vehicle trajectories (rather than, for example, a single predicted vehicle trajectory such as predicted vehicle trajectory 220) by, for example, respectively processing different monitored inputs of the at least one monitored input of block 110 and / or using different methods of predicting a predicted vehicle trajectory using one or more of the at least one monitored input of block 110. In some such embodiments, a single predicted vehicle trajectory (e.g., predicted vehicle trajectory 220) may still be generated (e.g., by either or both of the computing devices 310, 350) using multiple predicted vehicle trajectories by combining any, some, or all of the multiple predicted vehicle trajectories (e.g., via an averaging function, a weighting function, etc.).

[0079] Reference again Figure 1 ,refer to Figure 5A-5C , block 130 of method 100 includes detecting a detected vehicle trajectory. For example, Figure 5A In the embodiment of FIG. 2 , the first detected vehicle trajectory 222 indicates that the vehicle 200 is traveling toward the oncoming lane 214 and toward the center line 212, rather than following the predicted vehicle trajectory 220 and traveling within the lane 213. In another example, in FIG. Figure 5B In an embodiment of the present invention, the second detected vehicle trajectory 224 indicates that the vehicle 200 is stopping, rather than moving forward as predicted in the predicted vehicle trajectory 220. In another example, in Figure 5C In the embodiment of FIG. 2 , the third detected vehicle trajectory 226 indicates that the vehicle 200 is traveling along the predicted vehicle trajectory 220 .

[0080] Reference again Figure 3A-3B In an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226 of the vehicle 200 may be detected by the vehicle sensing system 320 and / or any or both of the vehicle computing device 310 and / or the remote computing device 350 (e.g., by processing detected data inputs detected by the vehicle sensing system 320). In an embodiment, any or both of the vehicle computing device 310 and / or the remote computing device 350 may generate multiple detected vehicle trajectories (rather than, for example, a single detected vehicle trajectory such as any, some, or all of the detected vehicle trajectories 222, 224, 226) by, for example, separate processing of different detected data inputs and / or different methods for detecting detected vehicle trajectories using one or more detected data inputs. In some such embodiments, a single detected vehicle trajectory (e.g., any, some, or all of detected vehicle trajectories 222, 224, 226) may still be generated (by, for example, either or both of computing devices 310, 350) using multiple predicted vehicle trajectories (via, for example, an averaging function, a weighting function, etc.) by combining any, some, or all of the multiple detected vehicle trajectories.

[0081] Reference again Figure 1 , block 140 of method 100 includes comparing the predicted vehicle trajectory 220 with the detected vehicle trajectory (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226) of block 130. In an embodiment, the predicted vehicle trajectory 220 may be compared with the detected vehicle trajectory (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226) by any 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.

[0082] refer to Figure 5A , in this embodiment, comparing the predicted vehicle trajectory 220 to the first detected vehicle trajectory 222 may, in embodiments, include determining that the first detected vehicle trajectory 222 veers from the predicted vehicle trajectory 220. Additionally, in embodiments, comparing the predicted vehicle trajectory 220 to the first detected vehicle trajectory 222 may also include determining that the first detected vehicle trajectory 222 endangers the vehicle 200 by directing the vehicle 200 (e.g., by a malicious third party or software initiating a sharp turn contrary to the operation of the vehicle 200 by the driver 202 or the instructions of the autonomous driving system of the vehicle 200) into the oncoming lane 214 and / or towards the oncoming vehicle 230 (as detected by, for example, the external vehicle sensor 320L).

[0083] refer to Figure 5B , in this embodiment, comparing the predicted vehicle trajectory 220 with the second detected vehicle trajectory 224 may, in embodiments, include determining that the second detected vehicle trajectory 224 suddenly stops, rather than continuing along the predicted vehicle trajectory 220. Additionally, in embodiments, comparing the predicted vehicle trajectory 220 with the second detected vehicle trajectory 224 may also include determining that the second detected vehicle trajectory 224 endangers the vehicle 200 by suddenly reducing the speed of the vehicle 200 (as compared to the speed detected by, for example, the acceleration sensor 320B) (e.g., by a malicious third party or software initiating braking contrary to the driver 202's operation of the vehicle 200 or the instructions of the vehicle 200's autonomous driving system).

[0084] refer to Figure 5C , in this embodiment, comparing the predicted vehicle trajectory 220 to the third detected vehicle trajectory 226 may, in embodiments, include determining that the third detected vehicle trajectory 226 is along or substantially similar to the predicted vehicle trajectory 220. Additionally, in embodiments, comparing the predicted vehicle trajectory 220 to the third detected vehicle trajectory 226 may also include determining that the vehicle 200 (and / or its components or systems) is operating normally and, therefore, is not controlled and / or does not appear to be under the control of, for example, a malicious third party or software.

[0085] However, in an embodiment and as described above, even if the vehicle 200 (and / or its components or systems) is under the control of a malicious third party or software, the operation of the vehicle 200 may not be as drastic or dangerous to the vehicle 200 as, for example, the detected vehicle trajectories 224, 226, because the malicious third party or software can still replicate the normal functions of the vehicle 200 based on, for example, the operation of the vehicle 200 by the driver 202 and / or the autonomous driving system of the vehicle 200.

[0086] On the contrary, reference Figure 5D In an embodiment, fourth detected vehicle trajectory 228 may differ only slightly from predicted vehicle trajectory 220, and differ slightly in a manner that does not endanger vehicle 200 (e.g., by staying within lane 213 rather than Figure 5A In embodiments, such a difference between the fourth detected vehicle trajectory 228 and the predicted vehicle trajectory 220 may be benign (e.g., not the result of damage to the vehicle 200 and / or its systems and / or components) due to inaccuracies in one or more monitored inputs of the block 110 (e.g., due to a malfunction in a sensor or system of the vehicle sensing 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). However, and as described above and as further described in detail below, in embodiments, such a difference may alternatively indicate that the vehicle 200 (and / or its components and / or systems) is compromised and, for example, under the control of a malicious third party or software that hides the compromised nature of the vehicle 200 by replicating the normal functionality of the vehicle 200, but still causes a difference between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228 due to, for example, delays in the systems and / or components of the vehicle 200 caused by the compromised nature of the vehicle 200. Thus, in embodiments, while deviations between the driver 202's monitored inputs and the detected vehicle trajectories 224, 226 may be apparent to the driver 202, smaller deviations, such as between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228, may not be apparent to the human eye (e.g., by a malicious third party or software compromising the vehicle 200 and introducing increased latency between the driver 202's inputs and the systems and / or components of the vehicle 200 receiving those inputs), the deviations may still be detected 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 has been compromised. In certain such embodiments, the system 300 (e.g., via the vehicle computing device 310 and / or the remote computing device 350) is able to detect minor deviations (e.g., between the predicted vehicle trajectory 220 and the fourth detected vehicle trajectory 228) by analyzing tens, hundreds, and / or even thousands of monitored inputs and doing so within a time frame of less than ten seconds, less than five seconds, and / or even less than one second. Thus, in an embodiment, even in the event that the vehicle 200 is compromised but the compromised nature does not (yet) pose an immediate threat of damage to the vehicle 200, the compromised nature of the vehicle 200 may still be detected.

[0087] Reference again Figure 1 and Figure 3A-3B , block 150 of method 100 includes determining a trajectory deviation value of the predicted vehicle trajectory 220 from the detected vehicle trajectory (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226, 228). In an embodiment, the trajectory deviation value may be a function of any, some, or all of the differences in speed, braking mode, linear acceleration, lateral acceleration, cornering stability, turning radius, operating mode, engine mode, gear mode, and / or other detected differences in operation, speed, and / or direction of the detected vehicle trajectory of the vehicle 200 (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226, 228) when compared to the predicted vehicle trajectory 220. In an embodiment, the trajectory deviation value may have a magnitude (e.g., on a scale of 1-10), where the magnitude of the trajectory deviation value indicates the degree to which the detected vehicle trajectory (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226, 228) deviates from the predicted vehicle trajectory 220. Thus, in an embodiment, the calculated magnitude of the trajectory deviation value may be a function of the magnitude of the difference (in speed, braking mode, linear acceleration, lateral acceleration, cornering stability, turning radius, operating mode, engine mode, gear mode, and / or other detected operation of the vehicle 200) between the predicted vehicle trajectory 220 and any, some, or all of the detected vehicle trajectories (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226, 228) (e.g., the residual between, for example, the position, speed, and / or direction of the predicted vehicle trajectory 220 and the position, speed, and / or direction of the detected vehicle trajectory). In an embodiment, the trajectory deviation value may 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 .

[0088] For example, refer to Figure 5A-5DIn an embodiment, in box 140, the detected vehicle trajectories 222, 224 are compared with the predicted vehicle trajectory 220, so that in box 150, a trajectory deviation value is calculated, and the magnitude of the deviation value is greater than the trajectory deviation value calculated by comparing the fourth detected vehicle trajectory 228 with the predicted vehicle trajectory 220. This is because, for example, the degree of difference between the detected vehicle trajectories 222, 224 and the predicted vehicle trajectory 220 is greater than the degree of difference between the fourth detected vehicle trajectory 228 and the predicted vehicle trajectory 220. Furthermore, in an embodiment, comparing the second detected vehicle trajectory 224 with the predicted vehicle trajectory 220 may result in the calculation of a trajectory deviation value having a magnitude that is less than a trajectory deviation value calculated by comparing the first detected vehicle trajectory 222 with the predicted vehicle trajectory 220 due to, for example, the second detected vehicle trajectory 224 following the predicted vehicle trajectory 220 more closely than the first detected vehicle trajectory 222 and / or the second detected vehicle trajectory 224 appearing due to, for example, the driver 202 suddenly braking faster than the system 300 is able to generate a new predicted vehicle trajectory based on the braking of the driver 202.

[0089] In an embodiment, comparing the third detected vehicle trajectory 226 to the predicted vehicle trajectory 220 in block 140 may result in calculating a trajectory deviation value in block 150 that is less in magnitude than trajectory deviation values ​​calculated based on the comparison of the predicted vehicle trajectory 220 to the detected vehicle trajectories 222, 224, 228 because the third detected vehicle trajectory 226 more closely follows the predicted vehicle trajectory 220. However, comparing the predicted vehicle trajectory 220 to the third detected vehicle trajectory 226 may still result in calculating a non-zero trajectory deviation value (e.g., because the third detected vehicle trajectory 226 does not extend as far as the predicted vehicle trajectory 220).

[0090] Reference again Figure 1 , block 160 of method 100 includes generating a response action in response to determining that the trajectory deviation value (e.g., determined in block 150) exceeds the trajectory deviation threshold. In an embodiment, the trajectory deviation value may be determined to exceed the trajectory deviation threshold 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. In an embodiment, the response action may be generated 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. In an embodiment, one response action may be generated in block 160. In an embodiment, multiple response actions may be generated in block 160.

[0091] In embodiments, the trajectory deviation threshold may be predetermined, and in certain such embodiments, the trajectory deviation threshold may be predetermined based on, for example, a method for calculating a trajectory deviation value and / or a statistically normal deviation (e.g., within a standard deviation) between a detected vehicle trajectory and a predicted vehicle trajectory. For example, in embodiments, the trajectory deviation value of the detected vehicle trajectories 222, 224 from the predicted vehicle trajectory 220 may exceed a predetermined trajectory deviation value, while the trajectory deviation value of the third detected vehicle trajectory 226 from the predicted vehicle trajectory 220 may not exceed a predetermined trajectory deviation value. However, the trajectory deviation threshold may not be fixed, and may instead change with, for example, technological improvements that improve the ability of the system 300 to calculate predicted vehicle trajectories, the conditions under which the vehicle 200 is operating (e.g., the trajectory deviation threshold may be increased in highly variable driving conditions (such as high winds or ice on the road 210)), degradation of the quality of the sensors and / or systems of the vehicle sensing system 320, and other changing circumstances. Thus, in an embodiment, in a first context (e.g., substantially ideal driving conditions on road 210), a trajectory deviation value may exceed a trajectory deviation threshold, while in a second context (e.g., where road 210 is icy), the same trajectory deviation value may not exceed the trajectory deviation threshold. Figure 5D In an embodiment, the fourth detected vehicle trajectory 228 and the predicted vehicle trajectory 220 may result in a determined trajectory deviation value exceeding the trajectory deviation threshold in the first context described above, while in the second context described above, the determined trajectory deviation value may not exceed the trajectory deviation threshold. In an embodiment, the trajectory deviation value may be associated with a confidence interval, and the trajectory deviation threshold may be fixed so that factors that may reduce the accuracy of the predicted vehicle trajectory 220 (such as those described above) may instead result in a lower confidence interval associated with the predicted vehicle trajectory 220, and therefore a lower trajectory deviation value for the predicted vehicle trajectory 220 and one or more of the detected vehicle trajectories 222, 224, 226, 228.

[0092] In an embodiment, the trajectory deviation value may be determined in part or in whole by a safety threshold. In an embodiment, the safety threshold may indicate a level of risk posed to the vehicle 200 and / or the driver 202 by the predicted vehicle trajectory 220. In an embodiment, the safety threshold may be exceeded, for example, because the predicted vehicle trajectory 220 indicates that the vehicle 200 will exit the lane 213, collide with another vehicle in the road 210, and / or otherwise endanger the vehicle 200 and / or the driver 202. For example, referring to Figure 5A , if the predicted vehicle trajectory 220 is predicted not to follow Figure 5A222, the predicted vehicle trajectory 220 may exceed the safety threshold due to the vehicle 200 entering the oncoming lane 214 and risking a collision with, for example, the oncoming vehicle 230. In an embodiment, by detecting the driver 202 (e.g., via a camera and / or steering wheel pressure sensor of the vehicle system 320J of the user interface, as described above with reference to FIG. Figure 3A As described above, safety thresholds may also be exceeded and / or may be adjusted. For example, in an embodiment, a camera of the vehicle software and hardware infrastructure system 320M may detect that the driver 202 is not looking at the road 210, and thus, when this condition is detected, the safety threshold may be exceeded and / or the safety threshold may be lowered (e.g., compared to the predicted vehicle trajectory 220). As another example, in an embodiment, a steering wheel pressure sensor of the vehicle software and hardware infrastructure system 320M may detect that the driver 202 is not placing his or her hands on the steering wheel of the vehicle 200, and thus, when this condition is detected, the safety threshold may be exceeded and / or the safety threshold may be lowered (e.g., compared to the predicted vehicle trajectory 220).

[0093] After determining the trajectory deviation value of the predicted vehicle trajectory 220 from the detected vehicle trajectory (e.g., in an embodiment, any, some, or all of the detected vehicle trajectories 222, 224, 226, 228), in an embodiment, any or both of the vehicle input monitor 313 and / or the remote vehicle input monitor 353 may generate a response action. In an embodiment, the response action may be any action that can increase the safety of the vehicle 200 and / or the driver 202 (e.g., by controlling or pausing a component or system of the vehicle 200 to prevent the vehicle 200 from following the detected trajectory) and / or address or remedy the potential or actual compromised nature of the vehicle 200. For example, in an embodiment, the response action may include at least one of a security scan, a shutoff of a connection of the vehicle 200, an alert to the driver 202 and / or passengers of the vehicle 200, an alert to a third party, a shutoff of the vehicle 200, a shutoff of a component of the vehicle 200, a shutoff of a system of the vehicle 200, an override command, a command check, or a combination thereof. In an embodiment, a security scan may include a security check of the vehicle 200 (and / or its components and / or systems) by a security system of the vehicle 200 to assess whether the vehicle 200 (and / or its components and / or systems) may be compromised. In an embodiment, a shutdown of the connection of the vehicle 200 may include stopping the connection between the vehicle 200 (and / or its components and / or systems) and any, some, or all of the external vehicles, systems, software, and / or computing devices (via, for example, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth, Zigbee, other wireless connection systems, etc.). In an embodiment, the alert to the driver 202 and / or passengers of the vehicle 200 may include one or more visual alerts (e.g., via the vehicle system 320J of the user interface, the display of the vehicle 200, and / or other visual signals of the vehicle 200) and / or audio alerts (e.g., via the speakers of the vehicle 200) that the driver 202 and / or passengers of the vehicle 200 may be impaired by, for example, describing the potential or actual systems and / or components of the vehicle 200 that may be affected by the potential or actual impaired nature of the vehicle 200, describing how the system 300 addresses the potential or actual impaired nature of the vehicle 200 (including, for example, describing other responsive actions being taken by the system 300), and / or communicating any other relevant information about the vehicle 200 (and / or its components and / or systems) and / or the actual or potential impaired nature of the vehicle 200. In an embodiment, the alert to a third party may include one or more visual alerts, audio alerts, and / or signaled alerts to a third party.In an embodiment, a visual alert to a third party may include, for example, illuminating an external signal (e.g., hazard lights or other warning lights) of the vehicle 200 to notify other vehicles (e.g., oncoming vehicles 230) or personnel in the vicinity of the vehicle 200 that the vehicle 200, for example, may and / or actually be damaged, pose a threat to other vehicles or personnel around it, and / or is otherwise not operating normally. In an embodiment, an audio alert to a third party may include, for example, playing through a speaker of the vehicle 200, warning other vehicles (e.g., oncoming vehicles 230) that the vehicle 200, for example, may and / or actually be damaged, pose a threat to other vehicles or personnel around it, and / or is otherwise not operating normally. In an embodiment, a signal transmission alert to a third party may be a (wired or wireless) communication of the system 300 to a third party (e.g., other vehicles in the vicinity of the vehicle 200, emergency services, and / or other third parties who may be able to assist or wish to avoid the vehicle 200 in the event that the vehicle 200 is compromised) via, for example, a telecommunications network (e.g., calling emergency services), an electrical signal (e.g., alerting other compromised vehicle response systems, the manufacturer of the vehicle 200, and / or emergency services), an electromagnetic signal (e.g., via, for example, radio waves, Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth, Zigbee, other wireless connection systems, etc.) to alert, for example, other nearby vehicles, the manufacturer of the vehicle 200, and / or emergency services. In an embodiment, shutting down the vehicle 200, components of the vehicle 200, and / or systems of the vehicle 200 may include disabling, powering off, or otherwise stopping the functionality of the vehicle 200, components of the vehicle 200, and / or systems of the vehicle 200. In an embodiment, the override instruction may include an instruction for the vehicle 200 and / or its components and / or systems (e.g., from the vehicle computing device 310 and / or the remote computing device 350) to ignore an operating instruction of a component and / or system of the vehicle (e.g., to stop an instruction from the braking system of the vehicle 200 to engage the parking brake of the vehicle 200 when the braking is causing or will cause the third detected vehicle trajectory 226). In an embodiment, the instruction check may include a signal transmission to a component and / or system of the vehicle 200 from which the instruction (e.g., an instruction to engage the braking system of the vehicle 200 that is causing or will cause the third detected vehicle trajectory 226) is received to confirm that the instruction is valid (and not the result of a system error, such as causing a fault instruction).

[0094] refer to Figure 1 and Figure 6, block 170 of method 100 includes implementing a response action. In an embodiment, the response action may be implemented by the vehicle control system 330, the vehicle computing device 310, the remote computing device 350, a component and / or system of the vehicle 200, and / or any external computing device or hardware to which the vehicle 200 may be electrically coupled (e.g., via Wi-Fi, WiMax, LTE, 4G, 5G, 6G, Bluetooth, Zigbee, other wireless connection systems, etc.). In an embodiment, the vehicle control system 330 may include any component and / or system that controls aspects of the functionality of the vehicle in whole or in part. In an embodiment, a single response action may be implemented by one or more components and / or systems of the vehicle control system 330, the vehicle computing device 310, and / or the remote computing device 350. Since, in an embodiment, in block 160, multiple response actions may be generated, in some such embodiments, in block 170, multiple response actions may be implemented (via, for example, one or more components and / or systems of the vehicle control system 330, the vehicle computing device 310, and / or the remote computing device 350). In embodiments where the response action is generated by the vehicle input monitor 313 in block 160, the response action may be transmitted (e.g., via a local communication interface of the system 300 coupling the vehicle computing device 310 to the vehicle control system 330) to the vehicle control system 330 as an instruction to implement the response action in block 170. In embodiments where the response action is generated by the remote computing device 350, the response action may be transmitted (e.g., via a transceiver 355 of the remote computing device 350) to the transceiver 331 of the vehicle control system 330 as an instruction to implement the response action.

[0095] In an embodiment, the vehicle control system 330 may include a vehicle mode controller 330A for controlling, for example, an operating mode, an engine mode, and / or a gear mode of the vehicle 200. Thus, in an embodiment, the vehicle mode controller 330A may implement responsive actions such as shutting down the engine of the vehicle 200, receiving an override instruction (e.g., from the vehicle computing device 310 and / or the remote computing device 350) and / or an instruction check on the vehicle mode controller 330A (e.g., an instruction to switch the engine mode of the vehicle 200 from "drive" to "reverse" when the vehicle 200 is driving in the road 210), shutting down the connection of the vehicle mode controller 330A to the autonomous driving system 330G of the vehicle 200 (as described in further detail below), and / or other responsive actions.

[0096] In an embodiment, the vehicle control system 330 may include a vehicle security system 330B (configured as software and / or non-transitory computer-readable media, e.g., executed by the vehicle computing device 310 and / or the remote computing device 350), which may be configured to protect the digital and / or physical systems and / or components of the vehicle 200 from security threats (e.g., 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 components and / or systems of the vehicle 200 have been compromised. Thus, in an embodiment, the vehicle security system 330B may implement response actions, such as performing a security scan of one or more systems and / or components of the vehicle 200 (e.g., the vehicle software and hardware infrastructure systems 320M), sending override commands and / or command checks to systems and / or components of the vehicle 200, shutting down connections to systems and / or components of the vehicle 200, shutting down the vehicle 200, shutting down components of the vehicle 200, shutting down systems of the vehicle 200, and / or other response actions.

[0097] In an embodiment, the vehicle control system 330 may include a vehicle user interface display 330C that 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, a response action to an alert by the driver 202 of the vehicle 200 by displaying a visual alert or playing an audio alert (e.g., via a speaker of the vehicle 200 and / or a speaker of the vehicle user interface display 330C) and / or other response actions.

[0098] 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, including, for example, an accelerator pedal, an engine, and / or a system that controls the operation of the vehicle 200. Therefore, in an embodiment, the vehicle acceleration controller 330D may implement response actions, such as shutting down the engine or other components of the vehicle 200, shutting down the connection of the vehicle acceleration controller 330D (and / or its systems and / or components) to the autonomous driving system 330G, receiving command checks and / or override commands, and / or other response actions.

[0099] 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 braking of the vehicle 200, including, for example, a brake pedal, a drum brake, a parking brake, a parking brake lever, and / or a system that controls the operation of the vehicle 200. Therefore, in an embodiment, the vehicle brake controller 330E may implement response actions, such as turning off the parking brake or other components of the vehicle 200, turning off the connection between the vehicle brake controller 330E (and / or its systems and / or components) and the autonomous driving system 330G, receiving a command check and / or an override command, and / or other response actions.

[0100] 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, including, for example, a steering wheel, an axle connected to the wheels of the vehicle 200 and / or its control system, and / or a system for controlling the operation of the vehicle 200. Therefore, in an embodiment, the vehicle steering controller 330F may implement response actions, such as shutting off the steering wheel or other components of the vehicle 200, shutting off the connection between the vehicle steering controller 330F (and / or its systems and / or components) and the autonomous driving system 330G, receiving command checks and / or override commands, and / or other response actions.

[0101] In an embodiment, the vehicle control system 330 may include an autonomous driving system 330G, which may include a combination of software and / or hardware configured to drive in place of the driver 202 or alongside the driver (e.g., as a driver assistance feature) and / or otherwise operate the vehicle 200. Thus, in an embodiment, the autonomous driving system 330G may implement responsive actions, such as shutting down the autonomous driving system 330G, overriding instructions of the autonomous driving system 330G (e.g., to any, some, or all of the vehicle mode controller 330A, the vehicle acceleration controller 330D, the vehicle braking controller 330E, and / or the vehicle steering controller 330F), receiving instruction checks and / or overriding instructions, and / or other responsive actions.

[0102] In an embodiment, the vehicle control system 330 may include a pre-collision system 330H, which may be configured to 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 braking controller 330E, and / or the vehicle steering controller 330F) when the pre-collision system 330H determines (e.g., via the external vehicle sensors 320L) that the vehicle 200 is about to collide with an external object (e.g., an oncoming vehicle 230) and therefore controls components and / or systems of the vehicle 200 in whole or in part to prevent and / or mitigate damage from the impending collision (e.g., by activating the brakes of the vehicle 200). Thus, in an embodiment, the pre-collision system 330H can implement response actions, such as shutting down the pre-collision system 330H, overriding instructions of the pre-collision system 330H (e.g., to any, some, or all of the vehicle mode controller 330A, the vehicle acceleration controller 330D, the vehicle braking controller 330E, and / or the vehicle steering controller 330F), receiving instruction checks and / or overriding instructions, and / or other response actions.

[0103] As described above, in embodiments, the sensors and / or systems of the vehicle sensing system 320 may perform functions separate from providing monitored inputs, and in embodiments, any, some, or all of the systems and / or sensors of the vehicle sensing system 320 may also be included in the vehicle control system 330. For example, in embodiments, the position sensor 320E may be included in the vehicle control system 330, and a response action such as sending an alert to a third party (e.g., sending the location of the vehicle 200 to emergency services) may be implemented.

[0104] In an embodiment, the damaged vehicle can also be detected by comparing the monitored input with the driver profile.It should be understood that the term "driver profile" used herein refers to a data structure established by recording the behavior of the (human or automated) driver of the vehicle (e.g., driver 202 and / or automatic driving system 330G), which can be used to predict the common behavior of the driver.In an embodiment, the driver profile may include recording (by, for example, vehicle computing device 310 and / or remote computing device 350) input (including, for example, any, some or all of the above-mentioned monitored input) and using the recorded input to create a baseline, whereby the driver's future driving behavior can be compared with the baseline to determine whether the driver's behavior matches the driver profile (e.g., by being similar to the behavior of how the driver drove in the past).For example, the driver profile may include an acceleration profile, which can be used to predict how the driver will accelerate in a background similar to the background in which data has been recorded to create the driver profile (e.g., how fast the driver can accelerate when the red light, the driver's vehicle stops thereafter, turns green). In an embodiment, the driver profile may include data related to any, some, or all of the driver's acceleration history (e.g., an "acceleration profile"), the driver's braking history (e.g., a "braking" profile), the driver's steering history (including, for example, lateral acceleration, turning smoothness, and / or turning radius) (e.g., a "steering profile"), and / or other data. In an embodiment, the driver profile may include data related to the driver's behavior at specific coordinates (e.g., a stop sign where the driver regularly slows down instead of coming to a complete stop at a ramp, on a slope, etc.).

[0105] In an embodiment, a driver profile may be generated by authenticating a particular driver to determine who or what is driving the vehicle (e.g., logging when the autonomous driving system 330G is driving the vehicle 200, logging when the driver 202 is driving the vehicle 200, and logging when another regular driver of the vehicle 200 is driving the vehicle 200), such as by using biometric analysis (e.g., recognizing the face, voice, or fingerprint of the driver 202), asking the driver to manually identify themselves (e.g., via a user interface of the vehicle system 320J), sensing the driver in other ways (e.g., via a particular key for operating the vehicle 200 and / or a connection of the driver's device such as a phone via Bluetooth to the vehicle 200), and / or determining the particular driver through observation (e.g., via comparing monitored inputs to previously recorded inputs and / or monitoring predicted vehicle trajectories 220). Thus, in an embodiment, the driver profile can predict and / or analyze the driver's behavior by generating, for example, standard deviations of driver inputs and / or actions when compared to the driver profile, confidence intervals (e.g., Bayesian confidence intervals) associated with monitored driver inputs and / or detected vehicle trajectories. Additionally, in an embodiment, the driver profile can be used to determine that the human driver of the vehicle is not the driver with the existing driver profile (but, for example, a hijacker of the vehicle). In an embodiment, the driver profile can be a digital twin of the driver, which in some such embodiments is stored in the vehicle data storage component 311 and / or the remote data storage component 351.

[0106] Reference now Figure 7 In an embodiment, the method 700 for detecting and responding to the detection of an impaired vehicle may include blocks 110, 120, 130, 140, 150, 160, 170 of method 100, and may further or alternatively include additional blocks related to the analysis of the driver profile (including any, some, or all of blocks 145, 155, 165 in an embodiment, as described in further detail below). In an embodiment, any, some, or all of blocks 145, 155, 165 may occur at the same time, before, or after any, some, or all of blocks 110, 120, 130, 140, 150, 160. In an embodiment, the method 700 may not include any, some, or all of blocks 110, 120, 130, 140, 150, 160.

[0107] Thus, 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 monitored input (e.g., at least one monitored input of block 110), or both to a driver profile (e.g., of driver 202 and / or autonomous driving system 330G). For example, in an embodiment, third detected vehicle trajectory 226 may be compared to a driver profile of driver 202. In an embodiment, vehicle computing device 310 and / or remote computing device 350 may compare detected vehicle trajectory, monitored input, or both to a driver profile.

[0108] In an embodiment, block 155 of method 700 includes determining a driver profile deviation value of the driver profile from a detected vehicle trajectory (e.g., any, some, or all of the detected vehicle trajectories 222, 224, 226, 228), at least one monitored input (e.g., of block 110), or both. In an embodiment, determining the driver profile deviation value of the driver profile may include comparing the detected vehicle trajectory (e.g., any, some, or all of the detected vehicle trajectories 222, 224, 226, 228), at least one monitored input (e.g., of block 110), or both to, for example, a standard deviation predicted by the driver profile, a confidence interval of the driver profile, and / or one or more of other contents of the driver profile. In an embodiment, the driver profile deviation value may have a magnitude (e.g., on a scale of 1-10), wherein the magnitude of the driver profile deviation value indicates the degree to which the detected vehicle trajectory, the monitored input, or both deviate from the driver profile predicted and / or expected trajectory and / or input. In an embodiment, the driver profile deviation value may be determined by the vehicle computing device 310 and / or the remote computing device 350 .

[0109] For example, refer to Figure 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 third detected vehicle trajectory 226 to predicted vehicle trajectory 220 may indicate that vehicle 200 accelerates slower than predicted by the driver profile of driver 202 (as indicated by the shorter length of third detected vehicle trajectory 226 compared to predicted vehicle trajectory 220). Therefore, in the same example, the difference between predicted vehicle trajectory 220 and third detected vehicle trajectory 226 may increase the magnitude of the driver profile deviation value of third detected vehicle trajectory 226 from the driver profile.

[0110] Reference again Figure 7, the frame 165 of the method 700 includes generating a response action in response to determining that the driver profile deviation value exceeds the profile deviation threshold. In an embodiment, the driver profile deviation value can be determined to exceed the driver profile deviation threshold by any one 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. In an embodiment, the response action can be generated by any one 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. In an embodiment, a response action can be generated in frame 165. In an embodiment, a plurality of response actions can be generated in frame 165. In an embodiment, the response action generated in frame 165 can include any one, some or all of the above-mentioned response actions. In an embodiment, after the response action is generated in frame 165, the response action can be implemented as described above with reference to frame 170 (e.g., via the vehicle control system 330 and / or the remote computing device 350).

[0111] It should now be understood that the present disclosure relates to various embodiments of methods, systems, and non-transitory computer-readable media for detecting a compromised vehicle and responding to the detection of a compromised vehicle, and in particular, for detecting a compromised vehicle by monitoring inputs and / or detected trajectories of the compromised vehicle and implementing responsive actions to handle the compromised vehicle.

[0112] Although specific embodiments have been illustrated and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. In addition, although various aspects of the claimed subject matter have been described herein, these aspects do not need to be used in combination. Therefore, the appended claims are intended to cover all such changes and modifications within the scope of the claimed subject matter.

Claims

1. A method for detecting a damaged vehicle and responding to the detection of a damaged vehicle, the method comprising: receiving, by a computing device, one or more monitored inputs of a vehicle; predicting, by a computing device and using at least one of the monitored inputs, a predicted vehicle trajectory; detecting, by a vehicle sensing system, a detected vehicle trajectory; comparing, by a computing device, the predicted vehicle trajectory with the detected vehicle trajectory; Determining, by a computing device, a trajectory deviation value between a predicted vehicle trajectory and a detected vehicle trajectory; In response to determining, by the computing device, that the trajectory deviation value exceeds a trajectory deviation threshold, generating a responsive action; and The responsive action is implemented using a computing device, a vehicle, or both.

2. The method according to claim 1, wherein: Vehicles include cars.

3. The method according to claim 1, wherein: The one or more monitored inputs are received from a vehicle sensing system, a driver of the vehicle, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

4. The method according to claim 1, wherein: The one or more monitored inputs include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering stability input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a vehicle system input of a user interface, or a combination thereof.

5. The method according to claim 1, wherein: The method further includes: comparing, by a computing device, at least one of the monitored inputs, the detected vehicle trajectory, or both, to a driver profile; determining, by a computing device, a driver profile deviation value of the driver profile from the detected vehicle trajectory, at least one of the monitored inputs, or both; and In response to determining, by the computing device, that the driver profile deviation value exceeds a profile deviation threshold, a responsive action is generated.

6. The method according to claim 5, wherein: The driver profile is generated from historical input from a driver of the vehicle, an automated driving system of the vehicle, or a combination thereof.

7. The method according to claim 1, wherein: The response action includes at least one of a security scan, a shutdown of a connection, an alert to a driver of the vehicle, an alert to a third party, a shutdown of the vehicle, a shutdown of a component of the vehicle, a shutdown of a system of the vehicle, an override command, a command check, or a combination thereof.

8. A system for detecting and responding to detection of a compromised vehicle, the system comprising a vehicle sensing system of a vehicle and a computing device comprising a memory component, wherein the memory component stores logic that, when executed by the computing device, causes the system to at least: receiving one or more monitored inputs from a vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; detecting, by a vehicle sensing system, a detected vehicle trajectory; comparing the predicted vehicle trajectory with the detected vehicle trajectory; Determining a trajectory deviation value between a predicted vehicle trajectory and a detected vehicle trajectory; In response to determining that the trajectory deviation value exceeds the trajectory deviation threshold, generating a responsive action; and The responsive action is implemented using a computing device, a vehicle, or both.

9. The system according to claim 8, wherein: Vehicles include cars.

10. The system according to claim 8, wherein: The one or more monitored inputs are received from a vehicle sensing system, a driver of the vehicle, a remote computing device, an autonomous driving system of the vehicle, or a combination thereof.

11. The system according to claim 8, wherein: The one or more monitored inputs include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering stability input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a vehicle system input of a user interface, or a combination thereof.

12. The system according to claim 8, wherein: The system also performs at least the following operations: comparing the detected vehicle trajectory, at least one of the monitored inputs, or both, to a driver profile; determining a driver profile deviation value from the detected vehicle trajectory, at least one of the monitored inputs, or both; as well as When the driver profile deviation value exceeds a profile deviation threshold, a response action is generated.

13. The system according to claim 12, wherein: The driver profile is generated from historical input from a driver of the vehicle, an automated driving system of the vehicle, or a combination thereof.

14. The system according to claim 8, wherein: The response action includes at least one of a security scan, a shutdown of a connection, an alert to a driver of the vehicle, an alert to a third party, a shutdown of the vehicle, a shutdown of a component of the vehicle, a shutdown of a system of the vehicle, an override command, a command check, or a combination thereof.

15. A non-transitory computer-readable medium for detecting a compromised vehicle and responding to detection of a compromised vehicle, wherein the non-transitory computer-readable medium stores logic that, when executed by a computing device, causes the computing device to at least: receiving one or more monitored inputs from a vehicle; predicting a predicted vehicle trajectory using at least one of the monitored inputs; detecting, by a vehicle sensing system, a detected vehicle trajectory; comparing the predicted vehicle trajectory with the detected vehicle trajectory; Determining a trajectory deviation value between a predicted vehicle trajectory and a detected vehicle trajectory; In response to determining that the trajectory deviation value exceeds the trajectory deviation threshold, generating a responsive action; and The responsive action is implemented using a computing device, a vehicle, or both.

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

17. The computer-readable medium of claim 15, wherein: The one or more monitored inputs include at least one of a pedal throttle mode input, a braking mode input, a linear acceleration input, a lateral acceleration input, a cornering stability input, a turning radius input, an operating mode input, an engine mode input, a gear mode input, a vehicle system input of a user interface, or a combination thereof.

18. The computer-readable medium of claim 15, wherein: The system also performs at least the following operations: comparing the detected vehicle trajectory, at least one of the monitored inputs, or both, to a driver profile; determining a driver profile deviation value from the detected vehicle trajectory, at least one of the monitored inputs, or both; as well as In response to determining that the driver profile deviation value exceeds the profile deviation threshold, a responsive action is generated.

19. The computer-readable medium of claim 18, wherein: The driver profile is generated from historical input from a driver of the vehicle, an automated driving system of the vehicle, or a combination thereof.

20. The computer-readable medium of claim 15, wherein: The response action includes at least one of a security scan of the vehicle, a shutoff of a connection of the vehicle, an alert to a driver of the vehicle, an alert to a third party, a shutoff of the vehicle, a shutoff of a component of the vehicle, a shutoff of a system of the vehicle, an override command, a command check, or a combination thereof.

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