Method and device for evaluating vehicle risk and system for monitoring attack

Abstract

The invention discloses a method and a device for evaluating vehicle risks and a system for monitoring attacks. The method comprises the following steps: acquiring a secret false information injection FDI attack signal for a vehicle; according to the secret FDI attack signal, a first reachable set and a danger set are determined, the first reachable set comprises the dynamic state of the vehicle changing along with the secret FDI attack signal, and the danger set comprises the dynamic state of the vehicle when a danger situation occurs; and determining the influence of the secret FDI attack signal on the vehicle according to the distance between the first reachable set and the dangerous set. The method can be applied to an intelligent vehicle and a new energy vehicle, and can evaluate the influence of a secret FDI attack signal on the vehicle safety so as to guide the design of an attack detection system of the vehicle, thereby improving the vehicle safety and reducing the vehicle risk.

Description

Method and device for assessing vehicle risk and system for monitoring attacks Technical Field

[0001] The present application relates to the field of vehicle security, and more particularly, to a method and device for assessing vehicle risks and a system for monitoring attacks. Background Art

[0002] Cooperative and adaptive cruise control (CACC) can be used as an automatic vehicle following system. Based on vehicle-to-vehicle (V2V) wireless communication, it shares local sensor information between vehicles, enabling connected and automated vehicles (CAVs) to travel on the road in a tightly coupled manner. However, CAV sensors and communication systems are vulnerable to external attacks. For example, vehicle radar, lidar, cameras, and global navigation satellite systems (GNSS) can be vulnerable to various types of external attacks. Both in-vehicle and out-of-vehicle networks can be subject to multiple types of attacks. This allows false information to spread among multiple CAVs, potentially leading to traffic accidents and, in the worst case, even casualties.

[0003] In view of this, a risk assessment and attack monitoring solution that can improve vehicle safety is urgently needed to be developed.

[0004] Summary of the Invention

[0005] Embodiments of the present application provide a method and device for assessing vehicle risk and a system for monitoring attacks. The method for assessing vehicle risk can assess the impact of covert false data injection (FDI) attacks on vehicles, and the system for monitoring attacks can improve the vehicle's ability to resist covert FDI attacks.

[0006] The method provided in the present application can be applied to vehicles. The vehicle is a vehicle in a broad sense, which can be a means of transportation (such as commercial vehicles, passenger cars, trucks, motorcycles, airplanes, flying cars, trains, ships, etc.), industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), agricultural equipment (such as mowers, harvesters, etc.), amusement equipment, toy vehicles, etc. The embodiments of the present application do not specifically limit the type of vehicle.

[0007] In a first aspect, a method for assessing vehicle risk is provided, the method comprising: obtaining a covert FDI attack signal directed at the vehicle; determining a first reachable set and a danger set based on the covert FDI attack signal, the first reachable set including the dynamic state of the vehicle that changes with the covert FDI attack signal, and the danger set including the dynamic state of the vehicle when a dangerous situation occurs; and determining the impact of the covert FDI attack signal on the vehicle based on the distance between the first reachable set and the danger set.

[0008] Exemplarily, the first device may be a sensor of the vehicle, for example, may include at least one of an inertial measurement unit (IMU), a lidar, a millimeter-wave radar, an ultrasonic radar, or may be other sensors, which is not specifically limited in this application.

[0009] For example, a covert FDI attack signal can include an attack signal that a detector (or monitor) cannot detect. For example, a covert FDI attack signal can be an attack signal within the detector's allowable error range, such as an attack signal hidden in a vehicle's system interference. Alternatively, a covert FDI attack signal can be an attack signal that the detector does not detect at all. For example, if the detector is not configured to monitor abnormal Global Positioning System (GPS) signals, all GPS spoofing signals can be considered covert FDI attack signals.

[0010] In some possible implementations, the obtained covert FDI attack signal targeting the vehicle is in the form of a mathematical expression, or may be in other forms.

[0011] In some possible implementations, the first reachable set includes all dynamic states of the vehicle that change in response to the covert FDI attack signal. "All dynamic states" may include the dynamic states of the vehicle under the influence of any possible covert FDI attack signal and / or system disturbance signal.

[0012] In some possible implementations, when the distance between the first reachable set and the danger set is less than or equal to zero, it means that the covert FDI attack signal is likely to affect vehicle safety, for example, it may cause the vehicle to exceed speed or deviate; when the distance between the first reachable set and the danger set is greater than zero, it means that the covert FDI attack signal will not cause danger to the vehicle.

[0013] In the above technical solution, by determining the reachable set and danger set of the vehicle, it is possible to evaluate whether the covert FDI attack signal will cause danger to the vehicle, so as to guide the redesign of the vehicle's attack monitoring system (hereinafter sometimes referred to as the vehicle system), thereby improving vehicle safety.

[0014] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: quantitatively evaluating the impact of the covert FDI attack signal on the vehicle based on the volume of the first reachable set.

[0015] In some possible implementations, the volume of the first reachable set can be determined based on the approximate set of the first reachable set; alternatively, the first reachable set can be determined by a simulation method (such as Monte Carlo simulation) and then the volume of the first reachable set can be determined.

[0016] In the above technical solution, the impact of the covert FDI attack signal on the existing vehicle system can be evaluated by the volume of the first reachable set. The larger the volume of the first reachable set, the greater the disturbance effect of the covert attack signal on the vehicle dynamic state, which means that the covert FDI attack signal has a greater impact on the vehicle system.

[0017] In combination with the first aspect, in certain implementations of the first aspect, the covert FDI attack signal is determined based on at least one of the following: a residual signal of a monitor, an estimation error of an observer, and sensor noise; wherein the observer is used to determine an estimation value of the sensor at a next moment based on a measurement value of the sensor; and the monitor is used to monitor the attack on the vehicle based on a difference between the measurement value of the sensor and the estimation value of the observer.

[0018] In some possible implementations, when the difference between the sensor's measurement and the observer's estimate is less than a threshold value, the monitor will not issue an alarm. Therefore, any attack signal that causes the difference between the sensor's measurement and the observer's estimate to be less than the threshold value can be considered a covert FDI attack signal.

[0019] In combination with the first aspect, in certain implementations of the first aspect, the covert FDI attack signal is associated with at least one of the following: a vehicle-to-vehicle (V2V) communication network, a global navigation satellite system (GNSS), a millimeter-wave radar, an ultrasonic radar, and an in-vehicle communication network.

[0020] The term "associated" can be understood as meaning that the covert FDI attack signal is hidden within the noise of the V2V communication network signal, the noise of the GPS signal, the noise of the millimeter-wave radar signal, or the noise of the ultrasonic radar signal and is transmitted to the vehicle. Alternatively, the term "associated" can be understood as meaning that the covert FDI attack signal targets at least one of the V2V communication network, GNSS, millimeter-wave radar, ultrasonic radar, and the in-vehicle communication network.

[0021] In the above technical solution, the impact of covert FDI attack signals on existing vehicle systems in various scenarios can be evaluated, which helps to guide the redesign of the vehicle's attack monitoring system and thus improve vehicle safety.

[0022] In combination with the first aspect, in certain implementations of the first aspect, determining a first reachable set based on the covert FDI attack signal includes: determining an approximate set of the first reachable set based on the covert FDI attack signal, the approximate set being determined by solving an optimization problem with vehicle queue chord stability of a vehicle formation and / or an estimation error of an observer as constraints, wherein the vehicle is in the vehicle formation.

[0023] In combination with the first aspect, in certain implementations of the first aspect, determining the first reachable set according to the covert FDI attack signal includes: determining the first reachable set according to the covert FDI attack signal using a Monte Carlo simulation method.

[0024] In the above technical solution, the first reachable set is determined by a simulation method, which can reduce the computational complexity of the process of determining the first reachable set.

[0025] In combination with the first aspect, in certain implementations of the first aspect, the dangerous situation includes at least one of the following: the speed of the vehicle is greater than or equal to a speed threshold, the acceleration of the vehicle is greater than or equal to an acceleration threshold, and the distance between the vehicle and the nearest preceding vehicle is less than or equal to a distance threshold.

[0026] For example, the speed threshold in different road sections may be the maximum speed allowed in that road section; the acceleration threshold may be determined based on the comfort and safety of the driver and passengers; the distance threshold may be set to 0 (a collision occurs when it is less than or equal to 0), or the distance threshold may also be set to a higher value to eliminate safety hazards.

[0027] For example, the distance between the vehicle and the most immediately preceding vehicle may be the distance between the rear bumper of the most immediately preceding vehicle and the front bumper of the vehicle.

[0028] In combination with the first aspect, in certain implementations of the first aspect, when the distance between the first reachable set and the danger set is less than or equal to a preset threshold, the gain of the monitor and / or observer is determined based on the covert FDI attack signal; wherein the gain is associated with a second reachable set, and the distance between the second reachable set and the danger value is greater than the preset threshold.

[0029] Exemplarily, the preset threshold may be the above-mentioned preset threshold, that is, 0, or may be other values, which is not specifically limited in the embodiments of the present application.

[0030] It should be understood that the distance between the second reachable set and the danger value is greater than the preset threshold, which means that the covert FDI attack signal will not threaten vehicle safety, for example, it will not cause the vehicle to exceed speed or deviate.

[0031] Among them, "the gain corresponds to the second reachable set" can be understood as after the gain is determined by the covert FDI attack signal, the set formed by the dynamic state of the vehicle changing with the covert FDI attack signal is the second reachable set.

[0032] In the above technical solution, the gain of the monitor and / or observer can be re-determined based on the covert FDI attack signal to reduce the impact of the covert attack signal on vehicle dynamics. The second reachable set has a smaller volume, and the distance between the second reachable set and the risk value is greater than the preset threshold. This means that the re-determined gain of the monitor and / or observer reduces the impact of the covert attack signal on vehicle safety, i.e., the vehicle risk is lowered.

[0033] In a second aspect, a device for assessing vehicle risk is provided, which includes: an acquisition unit for acquiring a covert false information injection (FDI) attack signal targeting the vehicle; a processing unit for determining a first reachable set and a danger set based on the covert FDI attack signal, wherein the first reachable set includes the dynamic state of the vehicle that changes with the covert FDI attack signal, and the danger set includes the dynamic state of the vehicle when a dangerous situation occurs; and based on the distance between the first reachable set and the danger set, determining the impact of the covert FDI attack signal on the vehicle.

[0034] In combination with the second aspect, in certain implementations of the second aspect, the processing unit is further used to: determine the impact of the covert FDI attack signal on the vehicle based on the volume of the first reachable set.

[0035] In combination with the second aspect, in certain implementations of the second aspect, the covert FDI attack signal is determined based on at least one of the following: a threshold of a monitor, an estimation error of an observer, sensor noise, and communication network noise; wherein the observer is used to determine an estimated value of the sensor at the next time step based on the measurement value of the sensor; and the monitor is used to monitor the attack on the vehicle based on the difference between the measurement value of the sensor and the estimated value of the observer.

[0036] In combination with the second aspect, in certain implementations of the second aspect, the covert FDI attack signal is associated with at least one of the following: a vehicle-to-vehicle (V2V) communication network, a global navigation satellite system (GNSS), a millimeter-wave radar, an ultrasonic radar, and an in-vehicle communication network.

[0037] In combination with the second aspect, in certain implementations of the second aspect, the processing unit is used to: determine an approximate set of the first reachable set based on the covert FDI attack signal, wherein the approximate set is determined by solving a convex optimization problem with the stability of the vehicle queue of the vehicle formation and / or the estimation error of the observer as constraints, wherein the vehicle is in the vehicle formation.

[0038] In combination with the second aspect, in certain implementations of the second aspect, the processing unit is configured to: determine the first reachable set by using a Monte Carlo simulation method according to the covert FDI attack signal.

[0039] In conjunction with the second aspect, in certain implementations of the second aspect, the dangerous situation includes at least one of the following: the speed of the vehicle is greater than or equal to a speed threshold, the acceleration of the vehicle is greater than or equal to an acceleration threshold, and the relative distance between the vehicle and other vehicles is less than a relative distance threshold.

[0040] In combination with the second aspect, in certain implementations of the second aspect, the processing unit is further used to: determine the gain of the monitor and / or observer based on the covert FDI attack signal when the distance between the first reachable set and the danger set is less than or equal to a preset threshold; wherein the gain is associated with a second reachable set, and the distance between the second reachable set and the danger value is greater than the preset threshold.

[0041] In a third aspect, a system for monitoring attacks is provided. The system for monitoring attacks provided in this application can be set in a computing platform of a vehicle. Exemplarily, the computing platform may include at least one of an advanced driving assistance system (ADAS), a vehicle control unit (VCU), and an in-vehicle infotainment system (IVI); or it may also include other computing platforms, such as an in-car application-server (ICAS) controller, a body domain controller (BDC), a special equipment system (SAS), a media graphics unit (MGU), a body super core (BSC), an ADAS super core, etc., which is not limited in this application. Among them, the ICAS may include at least one of the following: a vehicle control server ICAS1, an intelligent driving server ICAS2, an intelligent cockpit server ICAS3, and an infotainment server ICAS4.

[0042] The system includes: an observer, configured to determine, based on first data obtained from a first device of a vehicle at a first moment, second data of the first device at a second moment, wherein the second moment is a moment after the first moment; a monitor, configured to determine a difference between the second data and third data, wherein the third data is data obtained from the first device at the second moment; and outputting an alarm message when the difference is greater than or equal to a first threshold, wherein the alarm message is used to indicate that the vehicle is under attack; wherein the gain of the monitor is determined based on an FDI attack signal injected with covert false information.

[0043] In the above technical solution, the gain of the vehicle's attack monitoring system is updated according to the covert FDI attack signal, which helps to improve the vehicle's ability to resist covert FDI attacks, thereby improving vehicle safety and reducing vehicle risks.

[0044] In combination with the third aspect, in certain implementations of the third aspect, a gain of the observer is determined according to the covert FDI attack signal.

[0045] In combination with the third aspect, in certain implementations of the third aspect, the system further includes a controller for implementing basic vehicle control performance, and a gain of the controller is determined based on the covert FDI attack signal.

[0046] For example, the basic vehicle control performance includes the stability of the vehicle tracking error system, the required convergence speed of the vehicle tracking error system, the chord stability of the vehicle platoon, etc.

[0047] In some possible implementations, the controller can control the vehicle's motion state by sending a desired vehicle motion state to the vehicle's actuators. The desired vehicle motion state may include a desired speed, desired acceleration, desired steering wheel angle, etc. In addition to meeting basic vehicle control performance requirements, the controller must also ensure low vehicle risk.

[0048] In combination with the third aspect, in certain implementations of the third aspect, the gain is determined based on a first reachable set and a danger set of the vehicle; wherein, the first reachable set is determined based on the initial gains of the observer, the monitor, and the controller, the first reachable set includes the dynamic state of the vehicle that changes with the covert FDI attack signal, and the danger set includes the dynamic state of the vehicle when a dangerous situation occurs, wherein the distance between the first reachable set and the danger set is less than or equal to a second threshold.

[0049] Exemplarily, the second threshold value may be 0, or may be other values, which is not specifically limited in the embodiments of the present application.

[0050] In combination with the third aspect, in certain implementations of the third aspect, a distance between the second reachable set and the dangerous set is greater than the second threshold, and the second reachable set corresponds to the gain.

[0051] Among them, "the second reachable set corresponds to the gain" can be understood as after the gain is determined by the covert FDI attack signal, the set formed by all dynamic states of the vehicle changing with the covert FDI attack signal is the second reachable set.

[0052] In combination with the third aspect, in certain implementations of the third aspect, the volume of the second reachable set is smaller than the volume of the first reachable set.

[0053] In a fourth aspect, a device for assessing vehicle risk is provided, which includes a processing unit and a storage unit, wherein the storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit to enable the device to execute the method in any possible implementation of the first aspect.

[0054] Optionally, the above-mentioned processing unit may include at least one processor, and the above-mentioned storage unit may be a memory, wherein the memory may be a storage unit within the chip (for example, a register, a cache, etc.), or a storage unit located outside the chip within a mobile carrier (for example, a read-only memory, a random access memory, etc.).

[0055] In a fifth aspect, a vehicle is provided, comprising the system according to any one of the implementations of the third aspect.

[0056] In a sixth aspect, a server is provided, which includes the device in any implementation of the second aspect or the fourth aspect.

[0057] In a seventh aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in any one of the possible implementations of the first aspect.

[0058] It should be noted that the above-mentioned computer program code can be stored in whole or in part on the first storage medium, wherein the first storage medium can be packaged together with the processor or separately packaged with the processor, and the embodiments of the present application do not specifically limit this.

[0059] In an eighth aspect, a computer-readable medium is provided, wherein the computer-readable medium stores instructions. When the instructions are executed by a processor, the processor implements the method in any possible implementation of the first aspect.

[0060] In a ninth aspect, a chip is provided, comprising a processor for calling a computer program or computer instruction stored in a memory so that the processor executes the method in any possible implementation of the first aspect.

[0061] In combination with the ninth aspect, in one possible implementation, the processor is coupled to the memory through an interface.

[0062] In combination with the ninth aspect, in one possible implementation, the chip system also includes a memory, in which a computer program or computer instructions are stored. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] FIG1 is a functional block diagram of a vehicle provided in an embodiment of the present application;

[0064] FIG2 is a schematic diagram of a residual-based detector provided in an embodiment of the present application;

[0065] FIG3 is an exemplary flow chart of a method for assessing vehicle risk provided in an embodiment of the present application;

[0066] FIG4 is a schematic diagram of a reachable set and its approximate set provided in an embodiment of the present application;

[0067] FIG5 is a schematic diagram of a relationship between a reachable set and a dangerous set provided in an embodiment of the present application;

[0068] FIG6 is a schematic diagram of the relationship between the reachable set and the dangerous set after gain adjustment provided by an embodiment of the present application;

[0069] FIG7 is a schematic block diagram of a system for monitoring attacks provided in an embodiment of the present application;

[0070] FIG8 is a schematic block diagram of a device for assessing vehicle risk provided in an embodiment of the present application;

[0071] FIG9 is a schematic block diagram of a device for assessing vehicle risk provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

[0073] FIG1 is a functional block diagram of a vehicle 100 according to an embodiment of the present application. Vehicle 100 may include a perception system 120 and a computing platform 150. Perception system 120 may include a variety of sensors for sensing information about the environment surrounding vehicle 100. For example, perception system 120 may include a positioning system, which may be one or more of GPS, the BeiDou system or other positioning systems, an IMU, a lidar, a millimeter-wave radar, an ultrasonic radar, and a camera.

[0074] Some or all functions of vehicle 100 may be controlled by computing platform 150. Computing platform 150 may include processors 151 to 15n (n is a positive integer). A processor is a circuit capable of processing signals. In one implementation, the processor may be a circuit capable of reading and executing instructions, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a type of microprocessor), or a digital signal processor (DSP). In another implementation, the processor may implement certain functions through the logical relationships of hardware circuits. The logical relationships of the hardware circuits may be fixed or reconfigurable. For example, the processor may be a hardware circuit implemented as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field programmable gate array (FPGA). In a reconfigurable hardware circuit, the process of the processor loading a configuration file to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. In addition, the computing platform 150 can also include a memory for storing instructions. Some or all of the processors 151 to 15n can call the instructions in the memory and execute the instructions to achieve corresponding functions.

[0075] In some possible implementations, the vehicle 100 may include an advanced driving assistant system (ADAS). The ADAS utilizes one or more sensors in the perception system 120 (including but not limited to: lidar, millimeter-wave radar, camera, ultrasonic sensor, global positioning system, inertial measurement unit) to obtain information from the vehicle's surroundings and analyze and process the obtained information to implement functions such as obstacle perception, target recognition, vehicle positioning, automatic vehicle following, path planning, driver monitoring / reminders, etc., thereby improving the safety, automation and comfort of vehicle driving.

[0076] As mentioned above, CACC can be used as an automatic vehicle following system, sharing local sensor information between vehicles based on V2V wireless communication, enabling vehicles to travel on the road in a tightly coupled manner. However, the vehicle's sensors and communication systems are vulnerable to external attacks.

[0077] A novel residual-based detector can be used to identify sensor failures or external attacks on a vehicle's communication system. In some cases, when the communication system is under external attack, sensor signals may be subject to FDI attacks, potentially compromising vehicle safety. As shown in Figure 2(a), when using the novel residual-based detector to detect failures or random FDI attacks, a state observer (estimator) is first used to predict the sensor's measurement value for the next time step (hereinafter referred to as the estimated value) based on the sensor's measurement value from an unattacked or unfaulted state and the vehicle's mathematical model. Then, at the next time step, the difference between the sensor's measurement value and the estimated value provided by the state observer (hereinafter referred to as the observer) is calculated as the residual. If the sensor is not under attack or has no fault, the residual will be less than or equal to a preset threshold. If the residual is greater than the preset threshold, the sensor is considered to have been attacked or faulted, and the monitor will output an alarm. When the monitor outputs an alarm, the vehicle can take necessary response measures to mitigate the further impact of the attack, such as shutting down the engine to prevent speeding or even a collision with the vehicle ahead.

[0078] In this application, an attack signal that cannot be detected by a detector (or monitor) is referred to as a stealth or stealthy FDI attack signal, and an attack caused by a stealth FDI attack signal is referred to as a stealth FDI attack. For example, a stealth FDI attack signal can be an attack signal within the error range allowed by the detector, such as an attack signal hidden in the system interference of a vehicle. As shown in (b) in Figure 2, the stealth FDI attack signal will not trigger the monitor to output an alarm message, affecting the driving safety of the vehicle, and further affecting the process of automated platooning. For example, it may cause vehicle speeding or vehicle collision. It should be understood that in the embodiment of the present application, automated platooning refers to a formation state in which multiple vehicles are driven in a very small distance based on the support of autonomous driving technology and V2V technology. The multiple vehicles traveling in this formation are called a vehicle formation (platoon).

[0079] Exemplarily, vehicle system interference can include the following three categories: sensor noise, network communication noise, and vehicle modeling uncertainty. Sensor noise can be understood as the unwanted signal in the sensor output signal, and its formation factors can include external factors and internal factors. Exemplarily, external factors include factors such as human or natural interference outside the sensor circuit, such as electromagnetic radiation; internal factors include sensor measurement errors, internal conductive particle discontinuities, the capacitance effect of semiconductor PN junctions, and the irregular movement of electrons within conductors caused by high temperatures. Network communication noise can include: network noise caused by delays, packet loss, quantization, etc. in network communications, random thermal noise inherent in the channel and persisting, and impact noise caused by external factors. Vehicle modeling uncertainty can include: some unpredictable vehicle dynamics inherent to the vehicle itself.

[0080] In view of this, embodiments of the present application provide a method and apparatus for assessing vehicle risk. By quantifying the impact of covert FDI attack signals on vehicle dynamics, the risk level of a vehicle when it is subjected to a covert FDI attack can be assessed. When a risk is detected in the vehicle, the system for detecting attack signals can be redesigned based on the covert FDI attack signals. By monitoring attacks through this system, it is possible to promptly detect and trigger response measures when a vehicle system failure or a random FDI attack occurs. On this basis, the monitoring system can also reduce the impact of covert FDI attack signals on vehicle dynamics, thereby improving driving safety.

[0081] The types of covert FDI attacks involved in the embodiments of this application include but are not limited to the following:

[0082] 1. Stealth GPS attack: This attack slightly adjusts the GPS position so that the resulting false navigation route matches the actual road shape. This then triggers physical instructions to direct the vehicle to the incorrect destination indicated by the false navigation route. Because the amplitude of the GPS spoofing signal is small, it is smaller than the effect of GPS drift and does not trigger a monitor to output an alarm. Therefore, this stealth attack can bypass the monitor and remain undetected.

[0083] 2. Covert attack against radar: By hiding the attack signal against the radar in the vehicle's system interference, the attack can bypass the monitor when the attack signal cannot trigger the monitor to output an alarm information. This attack is a covert FDI attack.

[0084] 3. Covert attacks on speedometers and accelerators: Vehicle speedometers and accelerators may be vulnerable to electromagnetic signals or physical attacks. When these attack signals are hidden in the vehicle's system interference and cannot trigger the monitor to output an alarm information, the attack can bypass the monitor. This attack is a covert FDI attack.

[0085] 4. Covert Attacks on V2V Channels: Assume that for each vehicle in a platoon, the observer and controller in the attack detection detector are designed to achieve optimal estimation and control performance without considering safety. In this case, if a vehicle in a platoon sends sensor measurements to another vehicle in the platoon, it can attack the other vehicle by injecting false data into its sensor measurements. If the injected false data fails to trigger the monitor to output an alarm, the attack can bypass the monitor, and this attack is a covert FDI attack.

[0086] 5. Covert attack on the in-vehicle communication network: The sensor signal is changed during the in-vehicle network transmission process. The amplitude of the signal change cannot trigger the monitor to output an alarm information. The attack can bypass the monitor. This attack is a covert FDI attack.

[0087] FIG3 shows a schematic flow chart of a method 300 for assessing vehicle risk according to an embodiment of the present application. The attack monitoring system designed by the method 300 can be applied to the vehicle 100 shown in FIG1 . The method 300 may include:

[0088] S301, obtaining a covert FDI attack signal targeting a vehicle.

[0089] Exemplarily, the covert FDI attack signal may include at least one of the following: a covert FDI attack signal introduced through a V2V communication network, a covert FDI attack signal introduced through a GPS signal, a covert FDI attack signal introduced through a millimeter-wave radar or an ultrasonic radar, and a covert FDI attack signal introduced through an in-vehicle communication network.

[0090] The following uses a covert FDI attack signal introduced through a V2V communication network as an example to illustrate a method for determining a covert FDI attack signal.

[0091] In order to determine the covert FDI attack signal against a vehicle, it is first necessary to determine the vehicle motion state model, observer model, and detector model of the vehicle. For example, the following steps (1) to (3) may be included:

[0092] Step (1): Determine the vehicle motion state model of the vehicle.

[0093] Taking the vehicle as the i-th vehicle in a CACC-based platoon as an example, the following system model can be constructed for the vehicle's longitudinal motion state:

[0094]

[0095] Among them, x i =[ξ i v i a i u i Δv i a i-1 ] T ,([·] T represents the transpose of the matrix), ξ i is the tracking error, v i is the actual speed of the i-th vehicle, a i and a i-1 are the actual accelerations of the i-th and i-1-th vehicles, Δv i is the relative speed between the i-th and i-1-th vehicles, u i and u i-1 are the expected accelerations of the i-th and i-1-th vehicles, δ i Abnormal signals in the V2V network may be injected by malicious vehicles or caused by abnormalities in the V2V network; ui The disturbance in the V2V network may be caused by factors such as network packet loss, delay, quantization, etc., and meets the following conditions: is the perturbation boundary, It should be understood that is a set of positive real numbers. K=[k p k d] is the gain of the vehicle controller. h is the time interval constant, τ represents the transmission system dynamics constant, k p and k d Also a constant.

[0096] Assume that each vehicle has a millimeter-wave radar, an acceleration sensor, and a speed sensor, which can directly or indirectly measure ξ i ,v i ,a i ,u i ,Δv i , the vehicle's sensor signal can be expressed as follows:

[0097]

[0098] Among them, ω i is the sensor noise and satisfies Since the vehicle is in an abnormal state (such as being secretly attacked), the received network signals are all discrete, so the formulas (1) and (2) are discretized to obtain the following system model:

[0099] x i (k+1)=Ax i (k)+B1u i-1 (k)+B2(u i-1 (k)+δ i (k)+ω ui (k)),

[0100] y i (k) = Cx i (k)+ω i (k), (3)

[0101] in, x i (k) is the state value of the vehicle at time k.

[0102] Step (2): Determine the model description of the vehicle's state observer.

[0103] The observer model can be expressed as:

[0104]

[0105] in, is the gain of the observer ( represents the matrix X as an m×n dimensional real matrix),

[0106] Let the estimated error of the observer be Then e i (k) can be obtained as follows:

[0107]

[0108] in, By solving the linear matrix inequality (LMI), the observer gain L can be determined so that when there is no abnormality in the V2V network, that is, δ i When (k) = 0, Approaching x i (k), that is, the estimated error e i (k) approaches 0. The specific design method can refer to the design of Luenberger observer.

[0109] make Then we can get the following formula:

[0110]

[0111] It can be seen that the residual r i (k) Indirectly affected by abnormal V2V network signal δ i (k-1). When there is no abnormality in the V2V network, that is, δ i When (k) is 0, e i (k) approaches 0, ω i (k) approaches 0, then r i (k+1) also approaches 0.

[0112] Step (3): Determine the model description of the vehicle's monitor.

[0113] In order to monitor whether the vehicle is abnormal, such as whether it is attacked by random FDI, the quadratic form of the residual signal can be considered and defined as in is a positive semidefinite matrix, and consider the following monitor:

[0114] if The monitor then outputs an alarm message, that is, the value of the alarm signal changes from 0 to 1.

[0115] Therefore, the monitor gain π ensures that the ellipsoid Contains all possible ui (k) and ω i (k) The dynamic trajectory of the residual system (6) caused by π. In addition, the volume of the ellipsoid can be minimized by adjusting π, making the detection method more sensitive to abnormal signals.

[0116] Furthermore, according to the characteristics of the covert FDI attack signal (i.e. it will not trigger the monitor to send out an alarm), it is necessary to ensure that the covert FDI attack signal is within the z i (k)≤1, so the stealthy FDI attack signal must satisfy the following mathematical expression:

[0117]

[0118] in, And for all k≥0, satisfy r i (k) T ∏r i (k)≤1. e i (k) is a bounded signal, That is, δ can be represented by a series of bounded signals i (k).

[0119] Furthermore, when implementing the method of the present application to evaluate the impact of the covert FDI attack signal related to the V2V communication network on the vehicle, it is necessary to first obtain the above-mentioned covert FDI attack signal δ i (k).

[0120] S302, determining a first reachable set and a dangerous set based on the covert FDI attack signal, wherein the first reachable set includes the dynamic state of the vehicle that changes with the covert FDI attack signal, and the dangerous set includes the dynamic state of the vehicle when a dangerous situation occurs.

[0121] For example, the first reachable set can be understood as: for the existing design of the vehicle system, that is, the gains of the observer, detector and controller are known, under the influence of the covert FDI attack signal and / or the system disturbance signal, all possible sets of the vehicle's dynamic state that can be achieved. δ i (k) Satisfaction When all x i (k) possible set. is the first reachable set at time k. It should be noted that, due to It cannot be directly obtained mathematically, so it can be solved Ellipsoid approximation set Approximation by ellipsoid Approximately represents the first reachable set.

[0122] Exemplarily, the system disturbance signal may include at least one of the following: observer estimation error, residual signal, sensor noise (e.g., radar noise, speed sensor noise, etc.), network communication noise, and disturbance caused by attack signals. The attack signals include faulty or random FDI attack signals and covert FDI attack signals.

[0123] Solving the first reachable set may include the following steps (1) to (2):

[0124] Step (1): Solve The ellipsoid approximation set.

[0125] In order to solve the first reachable set, the system can be transformed into a linear time-invariant system and the extended state ζ is defined as i (k)= Substituting formula (7) into formulas (3) and (6) yields a linear time-invariant system of the following form:

[0126]

[0127] in,

[0128] It should be understood that in this linear time-invariant system, all disturbance signals are bounded signals. is the first reachable set of system (8). Since x i Yes i The part can be solved first Approximate set of Then In the mapping to x i Plane

[0129] For example, by solving the following linear matrix inequality, we can obtain

[0130] For a given a∈(0,1), if there exist constants a1, a2, a3, a4 and a matrix P such that the following linear matrix inequality holds:

[0131]

[0132] in, W a =diag[(1-a1)W1,…,(1-a4)W4], W4=Π, then for all k, we have and If the objective equation in solving (9) is to minimize -log(det(P)) (det(P) represents the determinant of the matrix P), then the obtained ellipsoid approximation set is It is the tightest approximation set, that is, it has the highest degree of approximation to the first reachable set.

[0133] Step (2): Solve The ellipsoid approximation set.

[0134] For example, the matrix P can be decomposed as follows:

[0135]

[0136] in, Then for all k, we have and Among them, P x =P1-P2(P3) -1 (P2) T , and ((P3) -1 is the inverse matrix of matrix P3).

[0137] For example, the relationship between the reachable set and its ellipsoidal approximation can be shown in FIG4 .

[0138] Optionally, we can further approximate the ellipsoid Sure The approximate volume of The volume of P can be quantitatively characterized by -log(det(P)). Based on this approximate volume as an indicator for assessing vehicle risk, the impact of covert FDI attack signals on vehicle dynamics is determined.

[0139] For example, the danger set of a vehicle is defined as the set of dynamic states of the vehicle when the vehicle violates traffic rules or is in danger, such as speeding or colliding with the vehicle in front, which can be expressed as v i (k)>v max or d i (k)<0. Among them, v max It can be the maximum speed that the i-th vehicle can reach, or it can be the maximum speed limit of the road on which the i-th vehicle is traveling, or it can be the maximum speed allowed to ensure the safety of the vehicle formation during driving, or it can be other speed thresholds, and the embodiments of the present application do not make specific limitations on this.

[0140] In some possible implementations, the vehicle's tracking error ε i (k) satisfies ε i (k) = d i (k)-(s i +hvi (k)), where d i (k) is the actual distance between the i-th vehicle and the i-1-th vehicle at the k-th moment, s i +hv i (k) is the expected distance between the i-th vehicle and the i-1-th vehicle at the k-th moment, v i (k) is the speed of the i-th vehicle at the k-th moment, and h is the time interval during which the vehicle is expected to maintain a certain speed. The specific value of h can be set by the vehicle user or can be a default value when the vehicle leaves the factory. This embodiment of the application does not specifically limit this; i is the distance between the two vehicles when they are expected to stop (also called the standstill distance), s i The specific value of may be the default value when the vehicle leaves the factory. In some possible implementations, s i The specific value of varies with the vehicle model. For example, for a small car, the value of i It can be 4.92 feet, or any value between 4.5 and 5.5 feet; for trucks, the value can be greater than 5.5 feet.

[0141] For example, the distance between the two vehicles may be the distance between the rear bumper of the leading vehicle and the front bumper of the trailing vehicle.

[0142] Then when d i When (k)<0, there should be ε i (k)+s i +hv i (k)<0. Then the danger set can be expressed as:

[0143]

[0144] in,

[0145] S303: Determine the impact of the covert FDI attack signal on the vehicle based on the distance between the first reachable set and the dangerous set.

[0146] For example, the distance between the first reachable set and the dangerous set can be determined according to the following formula:

[0147]

[0148] For example, if (As shown in (a) in Figure 5), the vehicle will not overspeed or collide under the covert attack, that is, the covert FDI attack signal will not threaten the safety of the vehicle. The vehicle may be in danger of speeding or collision under the covert attack, that is, the covert FDI attack signal may threaten the safety of the vehicle. For example, in the case shown in Figure 5 (b), since the danger set intersects with the first reachable set, the covert FDI attack signal will cause the vehicle to be in danger; in the case shown in Figure 5 (c), although However, since the danger set does not intersect with the first reachable set, the vehicle will not be in danger under the influence of the covert FDI attack signal.

[0149] In some possible implementations, when the method for assessing vehicle risk provided in the embodiments of the present application is applied to vehicle control in automated platooning, it is necessary to introduce estimation error, vehicle tracking error system stability, and vehicle queue string stability as constraints when solving the ellipsoid approximation set (or optimization problem). Alternatively, the false alarm rate of the detector can also be introduced as a constraint. It should be understood that vehicle queue string stability can ensure that small disturbances in the vehicle will not be amplified along the vehicle queue, thereby maintaining safety. For example, vehicle queue string stability ensures that sudden braking of the lead vehicle will not cause a collision with its followers.

[0150] Alternatively, the first reachable set can be determined using Monte Carlo simulation. For example, in each simulation, given an initial vehicle condition, a state trajectory can be obtained by adding a covert FDI attack signal and using model iteration. By repeating this process with multiple different initial vehicle conditions, multiple state trajectories can be obtained, which can serve as a rough approximation of the reachable set.

[0151] Alternatively, an approximate set of the reachable set may be determined by other methods besides the ellipsoidal approximation. For example, a polyhedron approximation or a Zeno polyhedron approximation may be used to determine an approximate set of the reachable set; or a reachability analysis may be performed using a machine learning method, which is not specifically limited in this embodiment of the present application.

[0152] The present invention provides a method for assessing vehicle risk. Using two risk assessment metrics (the volume of a reachable set and the distance between the reachable set and the dangerous set), the method can evaluate the impact of covert FDI attacks on vehicle dynamics, helping to guide vehicle system redesign. Specifically, the method incorporates system disturbance signals during vehicle modeling, making the model more similar to the actual vehicle and improving the accuracy of the assessment results.

[0153] In many cases, existing vehicle system designs (including detectors and controllers) primarily focus on performance in the absence of anomalies. For example, a Kalman filter can achieve optimal estimation of the vehicle's state in the presence of Gaussian white noise, and a vehicle controller must meet optimal basic vehicle control performance. However, an optimal design for performance in the absence of anomalies cannot guarantee secure vehicle control when the vehicle is attacked by a covert FDI attack.

[0154] Therefore, if an existing design of a vehicle attack detection system is assessed as presenting a risk, it can be redesigned. Exemplarily, as shown in Figure 7, the attack detection system may include a detector (including an observer and a monitor) and, alternatively, a controller. Exemplarily, the observer is configured to obtain first data from a first device in the vehicle at a first moment and determine second data from the first device at a second moment, where the second moment is a moment after the first moment. The monitor is configured to determine the difference between the second data and third data, where the third data is data obtained from the first device at the second moment. When the difference is greater than or equal to a first threshold, the monitor outputs an alarm indicating that the vehicle is under attack. The controller is configured to meet basic vehicle control requirements and may input its output to the observer. The controller output may include the desired vehicle motion state, such as a desired speed, desired acceleration, or desired steering wheel angle. It should be understood that these desired motion states, after being executed by the vehicle's actuators, may become the vehicle's actual motion state. Exemplarily, in a CACC system, the controller output may include a desired acceleration; in other intelligent driving systems, the controller output may also include a desired speed, desired steering wheel angle, or the like. In this attack monitoring system, at least one of the observer gain, the controller gain, and the monitor gain is determined based on the covert FDI attack signal. This redesigned attack monitoring system can improve a vehicle's ability to resist covert FDI attacks, thereby reducing the impact of the covert FDI attack signal on vehicle safety.

[0155] It should be noted that system redesign is still a solution to the optimization problem (9). However, unlike the vehicle risk assessment stage, the observer gain L, the monitor gain π, and the controller gain K are unknown variables. This makes Formula (9) no longer a linear matrix inequality, but a nonlinear matrix inequality. In order to linearize Formula (9) when the observer gain L, the monitor gain π, and the controller gain K are unknown variables, the matrix P can be set to have the following structure during the solution of Formula (9):

[0156]

[0157] in, are all semi-positive definite matrices. p k d ] decoupled from matrix A and defined new variables Then formula (9) can be transformed into is a linear matrix inequality in the variables .

[0158] It should be noted that when solving formula (9) to determine the above gain, k must also be satisfied. p >0,k d >k p τ, thus satisfying the stability of the vehicle queue (string) of CACC. Thus, the new observer gain can be obtained New controller gains Furthermore, a new monitor gain π can be obtained by solving.

[0159] As shown in Figure 6, after adjusting the gain of at least one of the observer, controller, and monitor, the distance between the reachable set and the dangerous set changes from the negative distance shown in Figure 6(a) to the positive distance shown in Figure 6(b). In other words, in the case shown in Figure 6(b), the vehicle is not in danger under the influence of the covert FDI attack signal.

[0160] In an attack monitoring system provided by an embodiment of the present application, the attack monitoring system is redesigned based on a covert FDI attack signal. This can reduce the impact of the covert FDI attack signal on vehicles using the attack monitoring system, thereby helping to improve vehicle safety. The performance of the attack monitoring system can also be guaranteed. For example, the observer can ensure a bounded estimation error; the monitor can monitor the attack signal with an acceptable false alarm rate; and the controller can ensure the dynamic stability of the vehicle tracking error and the string stability of the vehicle queue.

[0161] It should be noted that the attack detection system redesigned based on the covert FDI attack signal can be installed in the vehicle at the factory. Alternatively, the existing attack detection system in the vehicle can be updated through a software upgrade, for example, by updating the redesigned attack detection system to the vehicle via over-the-air (OTA) technology. In some possible implementations, when updating the vehicle's attack detection system, only one or more gains determined during the redesign process can be updated.

[0162] It should also be noted that the vehicle risk assessment method and attack monitoring system provided by this application are not only applicable to CACC driving scenarios, but also to driving scenarios such as adaptive cruise control (ACC), navigation cruise assistant (NCA), or integrated cruise assistant (ICA). In addition, the above embodiment is described using the example of covert FDI attack signals related to V2V communication networks. It should be understood that those skilled in the art, based on the concept of this application, can evaluate the impact of covert FDI attack signals related to sensors such as GNSS, millimeter wave radar, ultrasonic radar, and in-vehicle communication networks on vehicles, and redesign the attack monitoring system based on the above covert FDI attack signals. These solutions should also be included in the scope of protection of this application.

[0163] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between the various embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0164] The method provided in the embodiments of the present application is described in detail above with reference to Figures 2 to 7 . The apparatus provided in the embodiments of the present application will be described in detail below with reference to Figures 8 and 9 . It should be understood that the description of the apparatus embodiment corresponds to the description of the method embodiment. Therefore, for matters not described in detail, reference can be made to the method embodiment above, and for the sake of brevity, no further description will be given here.

[0165] FIG8 shows a schematic block diagram of a device 2000 for assessing vehicle risk provided in an embodiment of the present application. The device 2000 includes an acquisition unit 2010 and a processing unit 2020 .

[0166] The apparatus 2000 may include units for executing the method in Figure 3. Furthermore, each unit in the apparatus 2000 and the other operations and / or functions described above are for implementing corresponding processes of the method embodiment in Figure 3.

[0167] When the device 2000 is used to execute the method 300 in FIG. 3 , the acquisition unit 2010 may be used to execute S301 in the method 300 , and the processing unit 2020 may be used to execute S302 and S303 in the method 300 .

[0168] Specifically, the acquisition unit 2010 is configured to acquire a covert false information injection (FDI) attack signal targeting a vehicle. The processing unit 2020 is configured to determine, based on the covert FDI attack signal, a first reachable set comprising the vehicle's dynamic state as a function of the covert FDI attack signal, and a danger set comprising the vehicle's dynamic state when a dangerous situation occurs. The processing unit 2020 is configured to determine, based on the distance between the first reachable set and the danger set, the impact of the covert FDI attack signal on the vehicle. Specifically, the processing unit 2020 is configured to determine whether the covert FDI attack signal will cause the vehicle to be in danger.

[0169] Optionally, the processing unit 2020 is further configured to determine the impact of the covert FDI attack signal on the vehicle based on the volume of the first reachable set.

[0170] Optionally, the covert FDI attack signal is determined based on at least one of the following: a monitor residual signal, an observer estimation error, and sensor noise; wherein the observer is used to determine the estimated value of the sensor at the next time step based on the measurement value of the sensor; and the monitor is used to monitor the attack on the vehicle based on the difference between the measurement value of the sensor and the estimated value of the observer.

[0171] Optionally, the covert FDI attack signal is associated with at least one of the following: a vehicle-to-vehicle (V2V) communication network, a global positioning system (GPS) signal, a millimeter-wave radar, an ultrasonic radar, and an in-vehicle communication network.

[0172] Optionally, the processing unit 2020 is used to: determine an approximate set of the first reachable set based on the covert FDI attack signal, wherein the approximate set is determined by solving a convex optimization problem with the vehicle queue chord stability of the vehicle formation and / or the estimation error of the observer as constraints, wherein the vehicle is in the vehicle formation.

[0173] Optionally, the processing unit 2020 is configured to: determine the first reachable set by a Monte Carlo simulation method according to the covert FDI attack signal.

[0174] Optionally, the dangerous situation includes at least one of the following: the speed of the vehicle is greater than or equal to a speed threshold, the acceleration of the vehicle is greater than or equal to an acceleration threshold, and the distance between the vehicle and the most immediately preceding vehicle is less than or equal to a distance threshold.

[0175] Optionally, the processing unit 2020 is further used to: determine the gain of the monitor and / or observer based on the covert FDI attack signal when the distance between the first reachable set and the danger set is less than or equal to a preset threshold; wherein the gain is associated with a second reachable set, and the distance between the second reachable set and the danger value is greater than the preset threshold.

[0176] It should be understood that the division of the various units in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity, or they may be physically separated. Furthermore, the units in the device may be implemented in the form of a processor calling software; for example, the device includes a processor connected to a memory storing instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of the various units in the device. The processor may be a general-purpose processor, such as a CPU or a microprocessor, and the memory may be a memory within the device or external to the device. Alternatively, the units in the device may be implemented in the form of hardware circuits, and the functions of some or all of the units may be implemented through the design of the hardware circuits. The hardware circuits may be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units may be implemented through the design of the logical relationships between the components within the circuits. In another implementation, the hardware circuit may be implemented using a PLD, such as an FPGA, which may include a large number of logic gate circuits. The connections between the logic gate circuits are configured using a configuration file to implement the functions of some or all of the above units. All units of the above apparatus may be implemented entirely in the form of software called by a processor, or entirely in the form of hardware circuits, or partially in the form of software called by a processor and the rest in the form of hardware circuits.

[0177] In an embodiment of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor may be a circuit with the ability to read and execute instructions, such as a CPU, a microprocessor, a GPU, or a DSP. In another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit may be fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0178] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0179] In addition, the various units in the above devices can be fully or partially integrated together, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a system-on-a-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or implementing the functions of the various units of the device. The type of the at least one processor can be different, for example, including a CPU and FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0180] In a specific implementation process, the operations performed by the above-mentioned acquisition unit 2010 and the processing unit 2020 can be executed by the same processor, or can also be executed by different processors, for example, by multiple processors respectively.

[0181] An embodiment of the present application also provides a device, which includes a processing unit and a storage unit, wherein the storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit so that the device executes the method or steps performed by the above embodiment.

[0182] FIG9 is a schematic block diagram of a device for assessing vehicle risk according to an embodiment of the present application. The device 2100 for assessing vehicle risk shown in FIG9 may include: a processor 2110, a transceiver 2120, and a memory 2130. The processor 2110, the transceiver 2120, and the memory 2130 are connected via an internal connection path. The memory 2130 is used to store instructions, and the processor 2110 is used to execute the instructions stored in the memory 2130, and to receive / send some parameters through the transceiver 2120. Optionally, the memory 2130 may be coupled to the processor 2110 via an interface or integrated with the processor 2110.

[0183] It should be noted that the transceiver 2120 may include but is not limited to a transceiver device such as an input / output interface to implement communication between the device 2100 and other devices or a communication network.

[0184] Processor 2110 can be a general-purpose CPU, microprocessor, ASIC, GPU, or one or more integrated circuits, configured to execute relevant programs to implement the vehicle risk assessment method of the present invention. Processor 2110 can also be an integrated circuit chip with signal processing capabilities. In specific implementations, the various steps of the vehicle risk assessment method of the present invention can be performed by hardware integrated logic circuits or software instructions within processor 2110. Processor 2110 can also be a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules can be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other such storage media. The storage medium is located in the memory 2130 , and the processor 2110 reads the information in the memory 2130 and executes the method for assessing vehicle risk in accordance with the embodiment of the method of the present application in combination with its hardware.

[0185] The memory 2130 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0186] The transceiver 2120 uses a transceiver device such as, but not limited to, a transceiver to implement communication between the device 2100 and other devices or a communication network. For example, the transceiver 2120 can be used to obtain user location information.

[0187] An embodiment of the present application further provides a server, which may include the above-mentioned device 2000 or the above-mentioned device 2100.

[0188] An embodiment of the present application further provides a computer program product, which includes: computer program code, which enables the computer to execute the method in the above embodiment when the computer program code is run on a computer.

[0189] An embodiment of the present application further provides a computer-readable storage medium, which stores program code or instructions. When the computer program code or instructions are executed by a processor of a computer, the processor implements the method in the above embodiment.

[0190] An embodiment of the present application also provides a chip, comprising: at least one processor and a memory, wherein the at least one processor is coupled to the memory and is configured to read and execute instructions in the memory to execute the method in the above embodiment.

[0191] This application will present various aspects, embodiments, or features around systems including multiple devices, components, modules, etc. It should be understood and appreciated that each system may include additional devices, components, modules, etc., and / or may not include all of the devices, components, modules, etc. discussed in conjunction with the figures. Furthermore, combinations of these aspects may also be used.

[0192] Additionally, in the embodiments of this application, words such as "exemplary" and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner.

[0193] In the embodiments of the present application, “corresponding” and “relevant” may sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent.

[0194] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0195] In this application, "at least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: including the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0196] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0197] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0199] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0200] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0201] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0202] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for assessing vehicle risk, characterized in that: include: Obtaining FDI attack signals for covert false information injection targeting vehicles; Determining a first reachable set and a dangerous set based on the covert FDI attack signal; wherein the first reachable set includes the dynamic state of the vehicle that changes with the covert FDI attack signal, and the dangerous set includes the dynamic state of the vehicle when a dangerous situation occurs; An impact of the covert FDI attack signal on the vehicle is determined according to a distance between the first reachable set and the dangerous set.

2. The method according to claim 1, wherein The method further comprises: The impact of the covert FDI attack signal on the vehicle is quantitatively evaluated based on the volume of the first reachable set.

3. The method according to claim 1 or 2, wherein: The covert FDI attack signal is determined based on at least one of the following: The residual signal of the monitor, the estimation error of the observer, the sensor noise, and the communication network noise; The observer is used to determine an estimated value of the sensor at a next moment based on the measurement value of the sensor; and the monitor is used to monitor attacks against the vehicle based on a difference between the measurement value of the sensor and the estimated value of the observer.

4. The method according to any one of claims 1 to 3, characterized in that The covert FDI attack signal is associated with at least one of the following: Vehicle-to-vehicle (V2V) communication network, Global Navigation Satellite System (GNSS) signals, millimeter-wave radar, ultrasonic radar, and in-vehicle communication network.

5. The method according to any one of claims 1 to 4, characterized in that The dangerous situation includes at least one of the following: the speed of the vehicle is greater than or equal to a speed threshold, the acceleration of the vehicle is greater than or equal to an acceleration threshold, and the distance between the vehicle and the most immediately preceding vehicle is less than or equal to a distance threshold.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: When the distance between the first reachable set and the dangerous set is less than or equal to a preset threshold, determining a gain of a monitor and / or observer according to the covert FDI attack signal; The gain is associated with a second reachable set, and a distance between the second reachable set and the risk value is greater than the preset threshold.

7. A device for assessing vehicle risk, characterized in that: include: An acquisition unit, used for acquiring a covert false information injection (FDI) attack signal targeting a vehicle; a processing unit configured to determine a first reachable set and a dangerous set based on the covert FDI attack signal; wherein the first reachable set includes the dynamic state of the vehicle that changes with the covert FDI attack signal, and the dangerous set includes the dynamic state of the vehicle when a dangerous situation occurs; The impact of the covert FDI attack signal on the vehicle is quantitatively evaluated according to the distance between the first reachable set and the dangerous set.

8. The device according to claim 7, wherein The processing unit is further configured to: The impact of the covert FDI attack signal on the vehicle is determined according to the volume of the first reachable set.

9. The device according to claim 7 or 8, characterized in that The covert FDI attack signal is determined based on at least one of the following: The residual signal of the monitor, the estimation error of the observer, the sensor noise, and the communication network noise; The observer is used to determine an estimated value of the sensor at a next moment based on the measurement value of the sensor; and the monitor is used to monitor attacks against the vehicle based on a difference between the measurement value of the sensor and the estimated value of the observer.

10. The device according to any one of claims 7 to 9, characterized in that The covert FDI attack signal is associated with at least one of the following: Vehicle-to-vehicle (V2V) communication network, Global Navigation Satellite System (GNSS), millimeter-wave radar, ultrasonic radar, and in-vehicle communication network.

11. The device according to any one of claims 7 to 10, characterized in that The dangerous situation includes at least one of the following: the speed of the vehicle is greater than or equal to a speed threshold, the acceleration of the vehicle is greater than or equal to an acceleration threshold, and the distance between the vehicle and the most immediately preceding vehicle is less than or equal to a distance threshold.

12. The device according to any one of claims 7 to 11, characterized in that The processing unit is further configured to: When the distance between the first reachable set and the dangerous set is less than or equal to a preset threshold, determining a gain of a monitor and / or observer according to the covert FDI attack signal; The gain is associated with a second reachable set, and a distance between the second reachable set and the risk value is greater than the preset threshold.

13. A system for monitoring attacks, characterized in that: include: an observer, configured to determine second data of a first device of the vehicle at a second moment based on first data acquired from the first device at a first moment, wherein the second moment is a moment after the first moment; a monitor configured to determine a difference between the second data and third data, the third data being data acquired from the first device at the second moment; and output an alarm message when the difference is greater than a first threshold, the alarm message being configured to indicate that the vehicle is under attack; The gain of the monitor is determined according to the covert FDI attack signal.

14. The system according to claim 13, wherein: The gain of the observer is determined according to the covert FDI attack signal.

15. The system according to claim 13 or 14, characterized in that The system further includes a controller for achieving basic vehicle control performance, wherein a gain of the controller is determined according to the covert FDI attack signal.

16. The system according to any one of claims 13 to 15, characterized in that The gain is determined according to the first reachable set and the dangerous set of the vehicle; The first reachable set is determined based on the initial gains of the observer, the monitor, and the controller. The first reachable set includes the dynamic state of the vehicle that changes with the covert FDI attack signal. The dangerous set includes the dynamic state of the vehicle when a dangerous situation occurs. The distance between the first reachable set and the dangerous set is less than or equal to a second threshold.

17. The system according to claim 16, wherein: A distance between a second reachable set and the dangerous set is greater than the second threshold, and the second reachable set corresponds to the gain.

18. A device for assessing vehicle risk, characterized in that: include: memory for storing computer programs; A processor, configured to execute the computer program stored in the memory, so that the apparatus performs the method according to any one of claims 1 to 6.

19. A vehicle, characterized in that: Comprising a system as claimed in any one of claims 13 to 17.

20. A server, characterized in that: Comprising the apparatus of any one of claims 7 to 12, or the apparatus of claim 18.

21. A computer-readable storage medium, characterized in that Instructions are stored thereon, and when the instructions are executed by a processor, the processor is caused to implement the method according to any one of claims 1 to 6.

22. A chip, characterized in that: The chip includes a processor and a data interface, and the processor reads instructions stored in a memory through the data interface to execute the method according to any one of claims 1 to 6.

23. A computer program product, characterized in that The computer program product comprises: a computer program code, and when the computer program code is run on a computer, the computer is caused to perform the method according to any one of claims 1 to 6.

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