A Safety Chip Based on Driverless and Its Information Interaction Method

Abstract

The present invention discloses a security chip based on driverless driving and its information interaction method, which specifically relates to the field of information interaction technology. The security chip of the driverless vehicle verifies the identity of the infrastructure through the cloud trust platform. The cloud distributes temporary session keys to the driverless vehicle and the infrastructure, and uses the cloud as the central node to complete the information interaction between the driverless vehicle and the infrastructure. By real-time monitoring the communication situation between the security chip of the driverless vehicle and the infrastructure, the communication performance between the driverless vehicle and the infrastructure is quantified. The driving data of the vehicle is uploaded to the cloud through the security chip, and the traffic conditions at the intersection are uploaded to the cloud through the infrastructure, so as to quantify the driving situation of the driverless vehicle at the front intersection. The present invention helps to avoid traffic accidents caused by information delay or loss, and through the information interaction between the driverless vehicle, the infrastructure and the cloud, it avoids traffic insecurity situations.

Description

Technical Field

[0001] The present invention relates to the field of information interaction technology, and more specifically, to a safety chip based on driverless and its information interaction method. Background Art

[0002] With the rapid development of driverless technology, more and more driverless vehicles are put into actual roads. Driverless vehicles need to conduct real-time information interaction with road infrastructure (such as traffic lights, traffic monitoring cameras, road surface sensors, etc.) and other vehicles to ensure safe and efficient driving. However, limited by factors such as bandwidth, signal coverage, and vehicle mobility, existing communication systems often cannot guarantee the timely transmission and accuracy of data when driving at high speeds, in complex road conditions, or with unstable communication signals, resulting in inaccurate performance of driverless vehicles in complex traffic environments, which may affect the rationality and safety of driving decisions. Moreover, driverless vehicles need to consider driving safety issues and usually do not make flexible ad hoc decisions. Especially in complex or dynamic traffic environments, too many driverless vehicles will cause a reduction in traffic efficiency.

[0003] To solve the above two defects, a technical solution is provided now. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a safety chip based on driverless and its information interaction method to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A method for information interaction of a safety chip based on driverless specifically includes the following steps:

[0007] S1: The safety chip of the driverless vehicle verifies the identity of the infrastructure through the cloud trust platform. The cloud distributes temporary session keys for the driverless vehicle and the infrastructure, and uses the cloud as the central node to complete the information interaction between the driverless vehicle and the infrastructure;

[0008] S2: By monitoring the communication situation between the safety chip of the driverless vehicle and the infrastructure in real time, determine the communication capability information and safety conversion information of the driverless vehicle, and quantify the communication performance between the driverless vehicle and the infrastructure;

[0009] S3: Upload the driving data of the vehicle to the cloud through the safety chip to obtain the driving information of each driverless vehicle at the front intersection, and upload the traffic conditions at the intersection to the cloud through the infrastructure to obtain the road conditions information of each driverless vehicle at the front intersection;

[0010] S4: Through the comprehensive analysis of the driving information and road conditions of the driverless vehicle, quantify the driving situation of the driverless vehicle at the upcoming intersection and generate a warning signal in a timely manner;

[0011] Among them, the communication ability information of the driverless vehicle is represented by the communication position including the full coefficient, and the safety conversion information of the driverless vehicle is represented by the time delay fluctuation coefficient. Through the comprehensive analysis of the communication ability information and safety conversion information of the driverless vehicle, perform a weighted calculation on the communication position including the full coefficient and the time delay fluctuation coefficient to construct a communication evaluation model and generate a communication evaluation coefficient.

[0012] In a preferred embodiment, the acquisition logic of the communication position including the full coefficient is as follows:

[0013] Each time the driverless vehicle establishes a communication connection with the infrastructure, the system records the GPS coordinates at the time of communication connection and the coordinates of the infrastructure, calculates the distance between the two using the Haversine formula to obtain the communication connection distance between the driverless vehicle and the infrastructure, and marks the communication connection distance between the driverless vehicle and the infrastructure as: , where m = 1, 2, 3,..., M, M is a positive integer, and m is the number of each position at the upcoming intersection;

[0014] Obtain the success rate of the driverless vehicle communicating with the infrastructure at each position, and mark the success rate of the driverless vehicle communicating with the infrastructure at each position as: , where , is the number of successful communications at the m-th position, is the total number of attempted connections;

[0015] According to the historical records of the infrastructure at the upcoming intersection, obtain the historical average success rate of the driverless vehicle communicating with the infrastructure at each position at the upcoming intersection, and mark the historical average success rate of the driverless vehicle communicating with the infrastructure at each position at the upcoming intersection as: ;

[0016] Calculate the communication position including the full coefficient, and the calculation formula is: ; where is the communication position including the full coefficient.

[0017] In a preferred embodiment, the acquisition logic of the time delay fluctuation coefficient is as follows:

[0018] Obtain the average duration required for the driverless vehicle to successfully communicate with the infrastructure at each position, and mark the average duration required for the driverless vehicle to successfully communicate with the infrastructure at each position as: ;

[0019] Calculate the mean and standard deviation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location, and mark the mean and standard deviation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location as: and , where , ;

[0020] Calculate the coefficient of variation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location. The calculation formula is: ; where is the coefficient of variation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location;

[0021] Calculate the time delay fluctuation coefficient. The calculation formula is: ; where is the time delay fluctuation coefficient.

[0022] In a preferred embodiment, quantify the communication performance between the driverless vehicle and the infrastructure, including:

[0023] The calculation formula for the communication evaluation coefficient is: ; where is the communication evaluation coefficient, , are the proportionality coefficients of the communication location full quantity coefficient and the time delay fluctuation coefficient respectively, , are both greater than 0;

[0024] Set the communication evaluation coefficient threshold, compare the communication evaluation coefficient of the driverless vehicle with the communication evaluation coefficient threshold. If the communication evaluation coefficient of the driverless vehicle is less than the communication evaluation coefficient threshold, generate an alarm signal. If the communication evaluation coefficient of the driverless vehicle is greater than the communication evaluation coefficient threshold, do not generate an alarm signal.

[0025] In a preferred embodiment, obtain the driving information of each driverless vehicle at the intersection ahead, including:

[0026] Represent the driving information of each driverless vehicle at the intersection ahead by the vehicle driving data deviation coefficient;

[0027] The acquisition logic of the vehicle driving data deviation coefficient is: Determine the driving data of the vehicle after it enters the intersection through the safety chip of the driverless vehicle. The driving data includes the driving distance between the driverless vehicle and other vehicles. Mark the driving distance between the driverless vehicle and other vehicles as: , where n = 1, 2, 3, ……, N, N is a positive integer, and n is the serial number of the distance between the driverless vehicle and other vehicles during the driving process after it is connected to the intersection infrastructure;

[0028] Set the driverless vehicle distance threshold and mark the driverless vehicle distance threshold as: , obtain the distances between other vehicles during the driving process that are less than the driverless vehicle distance threshold, and re-mark the distances between other vehicles during the driving process that are less than the driverless vehicle distance threshold as: , where i = 1, 2, 3, ……, I, I is a positive integer, and i is the serial number of the distance less than the driverless vehicle distance threshold;

[0029] Calculate the vehicle driving data deviation coefficient, and the calculation formula is: ; where is the vehicle driving data deviation coefficient.

[0030] In a preferred embodiment, obtain the road conditions information of each driverless vehicle at the intersection ahead, including:

[0031] Represent the road conditions information of each driverless vehicle at the intersection ahead by the road condition danger concealment coefficient;

[0032] The acquisition logic of the road condition danger concealment coefficient is: The infrastructure uploads the traffic conditions at the intersection to the cloud, and the traffic conditions include the response time of the driverless vehicle, the response distance of the driverless vehicle, and the number of changes in traffic signals;

[0033] Set the standard value of the response time of the driverless vehicle, the standard value of the response distance of the driverless vehicle, and the standard value of the number of changes in traffic signals, and mark the standard value of the response time of the driverless vehicle as: , mark the standard value of the response distance of the driverless vehicle as: , mark the standard value of the number of changes in traffic signals as: , obtain the response time deviation of the driverless vehicle, the response distance deviation of the driverless vehicle, and the number of changes in traffic signal deviation, and mark the response time deviation of the driverless vehicle, the response distance deviation of the driverless vehicle, and the number of changes in traffic signal deviation as: 、 、 ;

[0034] Calculate the road condition danger concealment coefficient, and the calculation formula is: ; where is the road condition danger concealment coefficient.

[0035] In a preferred embodiment, quantifying the driving situation of the driverless vehicle at the intersection ahead and generating a warning signal in a timely manner, including:

[0036] Through comprehensive analysis of the driving information and road condition information of the driverless vehicle, the deviation coefficient of vehicle driving data and the hidden coefficient of road condition danger are weighted and calculated to construct a driving evaluation model and generate a driving evaluation coefficient. The calculation formula of the driving evaluation coefficient is: ; where is the driving evaluation coefficient of the driverless vehicle, 、 are the proportionality coefficients of the deviation coefficient of vehicle driving data and the hidden coefficient of road condition danger respectively, 、 are both greater than 0;

[0037] Obtain the driving evaluation coefficients of each driverless vehicle on the road ahead, and mark the driving evaluation coefficients of each driverless vehicle on the road ahead as: , where k = 1, 2, 3,..., K, K is a positive integer, and k is the number of the driverless vehicle existing on the road ahead;

[0038] Set the driving evaluation coefficient threshold, mark the driving evaluation coefficient threshold as: , calculate the feedback danger coefficient, and the calculation formula is: ; where is the feedback danger coefficient;

[0039] Set the feedback danger coefficient threshold, compare the feedback danger coefficient of the intersection ahead with the feedback danger coefficient threshold. If the feedback danger coefficient is greater than the feedback danger coefficient threshold, generate a warning signal. If the feedback danger coefficient is less than the feedback danger coefficient threshold, do not generate a warning signal.

[0040] In a preferred embodiment, a safety chip based on driverless driving includes an identity authentication module, a data acquisition module, a secure communication monitoring module, and a driving evaluation module;

[0041] The identity authentication module is used to verify the identities of the infrastructure and the driverless vehicle through the cloud trust platform to ensure the legality of both communication parties. Through public key infrastructure technology, the identity mutual trust between the vehicle and the infrastructure is ensured by using digital certificates and signature mechanisms;

[0042] The data acquisition module is used to collect the deviation coefficient of vehicle driving data and the hidden coefficient of road condition danger, and collect the communication position including the full amount coefficient and the time delay fluctuation coefficient;

[0043] A safety communication monitoring module is used to construct a communication evaluation model, set a threshold for the communication evaluation coefficient, and compare the communication evaluation coefficient of the driverless vehicle with the threshold of the communication evaluation coefficient;

[0044] A driving evaluation module is used to build a driving evaluation model, set a threshold for the feedback danger coefficient, and compare the feedback danger coefficient of the intersection ahead with the threshold of the feedback danger coefficient.

[0045] The technical effects and advantages of the present invention:

[0046] The present invention determines the communication ability information and safety conversion information of the driverless vehicle by real-time monitoring the communication between the safety chip of the driverless vehicle and the infrastructure, quantifies the driverless vehicles capable of cloud information interaction, obtains the driving information and road condition information of each driverless vehicle at the intersection ahead based on the driverless vehicles capable of cloud information interaction, and determines the real-time road condition complexity of the road ahead. The present invention helps to determine the communication stability between the driverless vehicle and the infrastructure at the intersection ahead by real-time monitoring the communication ability and the status of the safety chip, avoids traffic accidents caused by information delay or loss, and through the information interaction between the driverless vehicle, the infrastructure and the cloud, helps the driverless vehicle to share the real-time traffic conditions at the intersection ahead, provides decision support for the path planning of the following vehicles, and avoids traffic congestion or other unsafe situations. Brief Description of the Drawings

[0047] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0048] Figure 1 It is a schematic flow chart of a method for information interaction of a safety chip based on driverless driving according to the present invention;

[0049] Figure 2 It is a schematic structural diagram of a safety chip based on driverless driving according to the present invention. Detailed Embodiments

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Embodiment 1

[0052] Figure 1 It is a schematic flow chart of a method for information interaction of a safety chip based on driverless driving according to the present invention, and specifically includes the following steps:

[0053] S1: The security chip of the driverless vehicle verifies the identity of the infrastructure through the cloud trust platform. The cloud distributes temporary session keys for the driverless vehicle and the infrastructure, and uses the cloud as the central node to complete the information interaction between the driverless vehicle and the infrastructure.

[0054] S2: By monitoring the communication between the security chip of the driverless vehicle and the infrastructure in real time, determine the communication capability information and security conversion information of the driverless vehicle, and quantify the communication performance between the driverless vehicle and the infrastructure.

[0055] S3: The security chip uploads the driving data of the vehicle to the cloud to obtain the driving information of each driverless vehicle at the intersection ahead, and the infrastructure uploads the traffic conditions at the intersection to the cloud to obtain the road conditions information of each driverless vehicle at the intersection ahead.

[0056] S4: Through the comprehensive analysis of the driving information and road conditions information of the driverless vehicle, quantify the driving situation of the driverless vehicle at the intersection ahead, and generate a warning signal in a timely manner.

[0057] The security chip is connected to the vehicle control terminal, responsible for collecting, encrypting and decrypting vehicle status information (such as position, speed, driving mode, etc.), receiving data from the infrastructure, and transmitting it to the vehicle execution terminal.

[0058] The security chip is connected to the cloud trust platform, and conducts global identity authentication and data verification through the cloud platform to ensure that both the vehicle and the infrastructure are trusted nodes, and at the same time realizes dynamic key distribution and data verification.

[0059] Before the vehicle starts and connects to the infrastructure, a dual authentication process needs to be completed, including the security chip and the vehicle control terminal verifying each other's identities, ensuring that both parties are trusted devices through the digital certificates and signatures inside the vehicle, and using the unique identifier (such as the chip serial number) bound to the hardware for authentication with the digital signature.

[0060] The security chip verifies the identity of the infrastructure through the cloud trust platform, and the cloud distributes temporary session keys for the vehicle and the infrastructure for subsequent information interaction.

[0061] The information interaction between the vehicle and the infrastructure is carried out under the protection of dual authentication. Among them, the vehicle control terminal sends the status information to the security chip, and the security chip encrypts it and uploads it to the cloud through the infrastructure. After the cloud verifies the integrity of the information and the credibility of the source, it feeds back the verification result to the infrastructure.

[0062] The infrastructure encrypts the processed data and sends it to the security chip. The security chip decrypts it, verifies the data source, and sends the result to the vehicle control terminal for execution.

[0063] It should be noted that the communication between unmanned vehicles and the cloud may be affected by issues such as signal coverage and bandwidth limitations. Infrastructure (such as traffic lights and roadside units RSU) is usually deployed in fixed locations with stable connections and high-bandwidth network access capabilities. It can be used as a relay node. Vehicles upload data through the infrastructure, which can effectively avoid signal blind spots and enhance communication quality. Therefore, the infrastructure can share some of the communication pressure as a relay, and the vehicle only needs to establish short-distance communication with nearby infrastructure.

[0064] When the vehicle uploads status information to the infrastructure, the security chip encrypts it to prevent relay nodes (such as infrastructure) from reading or tampering with sensitive information. The infrastructure is only responsible for data forwarding and cannot access the encrypted content, thereby protecting the privacy of the vehicle.

[0065] The infrastructure needs to respond quickly (such as switching traffic lights) based on the data uploaded by the vehicles. While forwarding the data, the infrastructure can combine cloud feedback to make local decisions for real-time scenarios.

[0066] The cloud-based trust platform aggregates data from multiple infrastructure devices (such as traffic lights, cameras, and road sensors) to achieve global optimization of traffic. For example, the cloud can analyze the status of traffic lights and dynamically optimize traffic signal timing based on the vehicle's travel path;

[0067] As a central node, the cloud is responsible for synchronizing information between vehicles and infrastructure, ensuring the consistency of information such as traffic light status. For complex traffic scenarios, the cloud can coordinate the linkage of multiple infrastructure equipment.

[0068] By real-time monitoring of the communication between the safety chip of the unmanned vehicle and the infrastructure, the communication capability information and safety conversion information of the unmanned vehicle are determined, and the communication capability information of the unmanned vehicle is represented by the communication position full quantity coefficient, and the safety conversion information of the unmanned vehicle is represented by the time delay fluctuation coefficient.

[0069] It should be noted that the cloud system can receive most of the data from each vehicle, especially high-frequency and more important data (such as traffic light status, real-time road conditions, etc.). However, the bandwidth limitation of the network may cause the data to be unable to be fully transmitted, and other data (such as detailed sensor data, vehicle status information, etc.) may be delayed or lost, resulting in inaccurate decision-making. In addition, when there is a network attack, the attacker may interfere with or hijack the communication of the driverless vehicle through various means, thereby affecting data transmission and decision-making.

[0070] The acquisition logic of the communication location containing the full coefficient is as follows: Each time the driverless vehicle establishes a communication connection with the infrastructure, the system records the GPS coordinates (vehicle location) at that moment and the coordinates of the infrastructure, calculates the distance between the two using the Haversine formula to obtain the communication connection distance between the driverless vehicle and the infrastructure, and marks the communication connection distance between the driverless vehicle and the infrastructure as: , where m = 1, 2, 3, ……, M, M is a positive integer, and m is the number of each position at the intersection ahead;

[0071] It should be noted that generally, the closer the communication connection distance between the driverless vehicle and the infrastructure is, the higher the probability of successful communication between the driverless vehicle and the infrastructure. Each position at the intersection ahead is set by professionals in the field.

[0072] Obtain the success rate of communication between the driverless vehicle and the infrastructure at each position, and mark the success rate of communication between the driverless vehicle and the infrastructure at each position as: , where , is the number of successful communications at the m-th position, is the total number of attempted connections;

[0073] According to the historical records of the infrastructure at the intersection ahead, obtain the historical average success rate of communication between the driverless vehicle and the infrastructure at each position at the intersection ahead, and mark the historical average success rate of communication between the driverless vehicle and the infrastructure at each position at the intersection ahead as: ;

[0074] Calculate the communication location containing the full coefficient. The calculation formula is: ; where is the communication location containing the full coefficient.

[0075] It can be seen from the formula that the larger the communication location containing the full coefficient is, the stronger the communication ability between the driverless vehicle and the infrastructure is, indicating that the higher the possibility that the data transmitted by the driverless vehicle can be successfully received by the infrastructure.

[0076] The acquisition logic of the time delay fluctuation coefficient is as follows: Obtain the average duration required for the driverless vehicle to communicate successfully with the infrastructure at each position, and mark the average duration required for the driverless vehicle to communicate successfully with the infrastructure at each position as: ;

[0077] Calculate the average value and standard deviation of the average duration required for the driverless vehicle to communicate successfully with the infrastructure at each position, and mark the average value and standard deviation of the average duration required for the driverless vehicle to communicate successfully with the infrastructure at each position as: and , where , ;

[0078] Calculate the coefficient of variation of the average time required for a driverless vehicle to communicate successfully with infrastructure at each location. The calculation formula is: ; where is the coefficient of variation of the average time required for a driverless vehicle to communicate successfully with infrastructure at each location;

[0079] Calculate the time delay fluctuation coefficient. The calculation formula is: ; where is the time delay fluctuation coefficient.

[0080] As can be seen from the formula, the larger the time delay fluctuation coefficient, the more different the communication quality of the driverless vehicle at different locations, indicating that the driverless vehicle may encounter different signal strengths or interferences at different geographical locations, and the wireless communication environment is vulnerable to interference, such as mobile communication towers, Wi-Fi networks, satellite signals, etc.

[0081] Through the comprehensive analysis of the communication ability information and safety conversion information of the driverless vehicle, perform a weighted calculation on the communication location full coefficient and the time delay fluctuation coefficient to construct a communication evaluation model and generate a communication evaluation coefficient. The calculation formula of the communication evaluation coefficient is: ; where is the communication evaluation coefficient, , are the proportionality coefficients of the communication location full coefficient and the time delay fluctuation coefficient respectively, , are both greater than 0.

[0082] Set the communication evaluation coefficient threshold, and compare the communication evaluation coefficient of the driverless vehicle with the communication evaluation coefficient threshold. If the communication evaluation coefficient of the driverless vehicle is less than the communication evaluation coefficient threshold, an alarm signal is generated, indicating that the driverless vehicle cannot operate in a safe and stable communication environment, and the driving data generated by the driverless vehicle may be unreliable and cannot be uploaded to the cloud for analysis. If the communication evaluation coefficient of the driverless vehicle is greater than the communication evaluation coefficient threshold, no alarm signal is generated, indicating that the driverless vehicle can communicate with the infrastructure at the front intersection in a timely manner for information interaction.

[0083] Embodiment 2

[0084] The above embodiment evaluates the information interaction behavior performance of the driverless vehicle through the communication situation between the driverless vehicle and the infrastructure during the process of traveling from the rear intersection to the front intersection. This embodiment quantifies the road condition safety of the front intersection based on the driverless vehicle without generating an alarm signal and feeds it back to the driverless vehicle on the rear road condition in a timely manner.

[0085] For a driverless vehicle that has not generated an alarm signal, the infrastructure at the intersection ahead communicates with the safety chip of the driverless vehicle and exchanges data with the infrastructure at the intersection ahead through wireless communication technology to identify the driverless vehicles connected to the infrastructure at the intersection ahead. Using the cloud as a coordination hub for the driverless vehicles and the infrastructure, the driving data of the vehicle is uploaded to the cloud through the safety chip to obtain the driving information of each driverless vehicle at the intersection ahead, and the traffic conditions at the intersection are uploaded to the cloud through the infrastructure to obtain the road condition information of each driverless vehicle at the intersection ahead. The driving information of each driverless vehicle at the intersection ahead is represented by a vehicle driving data deviation coefficient, and the road condition information of each driverless vehicle at the intersection ahead is represented by a road condition danger concealment coefficient.

[0086] Record in real time the length of time that a driverless vehicle at the intersection ahead is connected to the infrastructure, and mark the length of time that the driverless vehicle is connected to the infrastructure as: T.

[0087] It should be noted that data exchange between the driverless vehicle and the infrastructure is carried out through the Vehicle-to-Infrastructure (V2I) communication protocol. When the vehicle approaches the intersection, the infrastructure starts to receive the vehicle's data (such as position, speed, direction, etc.). At the same time, the infrastructure also sends signal light status, road condition information, etc. to the vehicle. When the vehicle enters the communication coverage area of the intersection, the start time of the connection is recorded. When the vehicle exits the communication coverage area of the intersection or the communication is interrupted, the end time of the connection is recorded. If the vehicle has not exited the communication coverage area of the intersection, the time from the start time of the connection to the current time is recorded.

[0088] The acquisition logic of the vehicle driving data deviation coefficient is as follows: Determine the driving data of the vehicle after it enters the intersection through the safety chip of the driverless vehicle. The driving data includes the driving distance between the driverless vehicle and other vehicles, and mark the driving distance between the driverless vehicle and other vehicles as: , where n = 1, 2, 3,..., N, N is a positive integer, and n is the number of the distance between the driverless vehicle and other vehicles during the driving process after the driverless vehicle is connected to the infrastructure at the intersection;

[0089] It should be noted that the driverless vehicle can identify the distance from other vehicles through sensors. Usually, the driverless vehicle will maintain a certain distance from other vehicles. However, in complex situations such as dangerous road conditions and traffic congestion, the driverless vehicle cannot maintain a safe distance from other vehicles.

[0090] Set a driverless vehicle distance threshold and mark the driverless vehicle distance threshold as: , obtain the distance between the vehicle and other vehicles during driving that is less than the distance threshold of the driverless vehicle, and relabel the distance between the vehicle and other vehicles during driving that is less than the distance threshold of the driverless vehicle as: , where i = 1, 2, 3, ……, I, I is a positive integer, and i is the number of the distance less than the distance threshold of the driverless vehicle;

[0091] Calculate the vehicle driving data deviation coefficient, and the calculation formula is: ; where is the vehicle driving data deviation coefficient.

[0092] As can be seen from the formula, the larger the vehicle driving data deviation coefficient, the greater the potential risk of the driverless vehicle during driving at the intersection ahead, and there may be complex road conditions.

[0093] The acquisition logic of the road condition danger concealment coefficient is as follows: The infrastructure uploads the traffic conditions of the intersection to the cloud, and the traffic conditions include the response time of the driverless vehicle, the response distance of the driverless vehicle, and the number of changes in traffic signals;

[0094] Set the standard value of the response time of the driverless vehicle, the standard value of the response distance of the driverless vehicle, and the standard value of the number of changes in traffic signals, and mark the standard value of the response time of the driverless vehicle as: , mark the standard value of the response distance of the driverless vehicle as: , mark the standard value of the number of changes in traffic signals as: , obtain the response time deviation of the driverless vehicle, the response distance deviation of the driverless vehicle, and the deviation of the number of changes in traffic signals, and mark the response time deviation of the driverless vehicle, the response distance deviation of the driverless vehicle, and the deviation of the number of changes in traffic signals as: 、 、 ;

[0095] Calculate the road condition danger concealment coefficient, and the calculation formula is: ; where is the road condition danger concealment coefficient.

[0096] It should be noted that the response time of the driverless vehicle represents the time interval from when the traffic signal changes to the driving instruction to when the driverless vehicle starts. Because the driverless vehicle correspondingly considers the driving safety of the vehicle and maintains a safe distance between vehicles, the longer the response time of the driverless vehicle, the greater the delay in the driverless vehicle's reaction and the lower the driving efficiency of the vehicle;

[0097] The response distance of a driverless vehicle represents the driving distance of the driverless vehicle from when the traffic signal changes to a driving instruction until the traffic signal changes to a stop instruction. The longer the response distance of the driverless vehicle, the more it indicates that there is a certain delay when the driverless vehicle makes a reaction, and the lower the driving efficiency of the vehicle.

[0098] The number of traffic signal changes represents the number of times the traffic signal changes after the driverless vehicle is connected to the infrastructure. The more the number of traffic signal changes, the more it indicates that there is a situation of congestion in the road conditions ahead of the driverless vehicle.

[0099] The reasons for the increase in the response time of the driverless vehicle, the response distance of the driverless vehicle, and the number of traffic signal changes may be that the road conditions ahead are complex, there is a time delay in each decision of the driverless system, and the decision-making system of the driverless vehicle fails to adjust the driving strategy in a timely manner according to the environmental changes, resulting in a lower driving efficiency of the vehicle.

[0100] It can be seen from the formula that the larger the road condition danger concealment coefficient, the lower the driving efficiency of the vehicles in the road conditions ahead of the driverless vehicle, indicating that there may be complex road conditions ahead.

[0101] Through the comprehensive analysis of the driving information and road condition information of the driverless vehicle, the vehicle driving data deviation coefficient and the road condition danger concealment coefficient are weighted and calculated to construct a driving evaluation model and generate a driving evaluation coefficient. The calculation formula of the driving evaluation coefficient is: ; where is the driving evaluation coefficient of the driverless vehicle, 、 are the proportionality coefficients of the vehicle driving data deviation coefficient and the road condition danger concealment coefficient respectively, 、 are both greater than 0.

[0102] Obtain the driving evaluation coefficients of each driverless vehicle in the road conditions ahead, and mark the driving evaluation coefficients of each driverless vehicle in the road conditions ahead as: , where k = 1, 2, 3,..., K, K is a positive integer, and k is the number of the driverless vehicle in the road conditions ahead;

[0103] Set the driving evaluation coefficient threshold, and mark the driving evaluation coefficient threshold as: , calculate the feedback danger coefficient, and the calculation formula is: ; where is the feedback danger coefficient.

[0104] It should be noted that the driving evaluation coefficient threshold is set by the staff in the professional field. If the driving evaluation coefficient of the driverless vehicle is greater than the driving evaluation coefficient threshold, it indicates that the driving condition of the driverless vehicle at the upcoming intersection is poor. On the contrary, if the driving evaluation coefficient of the driverless vehicle is less than the driving evaluation coefficient threshold, it indicates that the driving condition of the driverless vehicle at the upcoming intersection is good.

[0105] Set the feedback danger coefficient threshold, and compare the feedback danger coefficient of the upcoming intersection with the feedback danger coefficient threshold. If the feedback danger coefficient is greater than the feedback danger coefficient threshold, a warning signal will be generated. The warning signal is sent by the cloud to the driverless vehicle about to enter the upcoming intersection, notifying the driverless vehicle about to enter the upcoming intersection to change its driving route and avoid entering the upcoming intersection. If the feedback danger coefficient is less than the feedback danger coefficient threshold, no warning signal will be generated.

[0106] It should be noted that

[0107] The present invention determines the communication ability information and security conversion information of the driverless vehicle by real-time monitoring of the communication between the security chip of the driverless vehicle and the infrastructure, quantifies the driverless vehicles capable of cloud information interaction, obtains the driving information and road condition information of each driverless vehicle at the upcoming intersection based on the driverless vehicles capable of cloud information interaction, and determines the real-time road condition complexity of the upcoming road conditions. The present invention helps to determine the communication stability between the driverless vehicle and the infrastructure at the upcoming intersection by real-time monitoring of the communication ability and the status of the security chip, avoiding traffic accidents caused by information delay or loss, and through the information interaction between the driverless vehicle, the infrastructure and the cloud, helping the driverless vehicle to share the real-time traffic conditions at the upcoming intersection, providing decision support for the path planning of the following vehicles, and avoiding traffic congestion or other unsafe situations.

[0108] Embodiment 3

[0109] Figure 2 It is a schematic structural diagram of a security chip based on driverless driving of the present invention, including an identity authentication module, a data acquisition module, a security communication monitoring module, and a driving evaluation module;

[0110] The identity authentication module is used to verify the identities of the infrastructure and the driverless vehicle through the cloud trust platform, ensuring the legality of both communication parties, and ensuring the identity mutual trust between the vehicle and the infrastructure through public key infrastructure technology, using digital certificates and signature mechanisms;

[0111] The data acquisition module is used to collect the vehicle driving data deviation coefficient, road condition danger concealment coefficient, and collect the communication location including the full amount coefficient and the time delay fluctuation coefficient;

[0112] A secure communication monitoring module is used to construct a communication evaluation model, set a threshold for the communication evaluation coefficient, and compare the communication evaluation coefficient of the driverless vehicle with the threshold of the communication evaluation coefficient.

[0113] A driving evaluation module is used to construct a driving evaluation model, set a threshold for the feedback risk coefficient, and compare the feedback risk coefficient of the front intersection with the threshold of the feedback risk coefficient.

[0114] All the above formulas are dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0116] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0117] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0118] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0119] In several embodiments provided in the present 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 illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0120] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0121] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application and should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for information interaction of a security chip based on driverless, characterized in that Specifically, it includes the following steps: S1: The security chip of the driverless vehicle verifies the identity of the infrastructure through the cloud trust platform. The cloud distributes temporary session keys to the driverless vehicle and the infrastructure, and uses the cloud as the central node to complete the information interaction between the driverless vehicle and the infrastructure; S2: By monitoring the communication situation between the security chip of the driverless vehicle and the infrastructure in real time, determine the communication ability information and security conversion information of the driverless vehicle, and quantify the communication performance between the driverless vehicle and the infrastructure; S3: Upload the driving data of the vehicle to the cloud through the security chip to obtain the driving information of each driverless vehicle at the intersection ahead, and upload the traffic conditions at the intersection to the cloud through the infrastructure to obtain the road conditions information of each driverless vehicle at the intersection ahead; S4: Through the comprehensive analysis of the driving information and road conditions information of the driverless vehicle, quantify the driving situation of the driverless vehicle at the intersection ahead, and generate a warning signal in a timely manner; Among them, the communication ability information of the driverless vehicle is represented by the communication position including the full coefficient, and the security conversion information of the driverless vehicle is represented by the time delay fluctuation coefficient. Through the comprehensive analysis of the communication ability information and security conversion information of the driverless vehicle, the communication position including the full coefficient and the time delay fluctuation coefficient are weighted and calculated to construct a communication evaluation model and generate a communication evaluation coefficient; The calculation formula for the communication evaluation coefficient is as follows: ; where is the communication evaluation coefficient, is the full quantity coefficient of the communication location, is the time delay fluctuation coefficient, and are the proportionality coefficients of the full quantity coefficient of the communication location and the time delay fluctuation coefficient respectively, and are both greater than 0; Set the communication evaluation coefficient threshold, compare the communication evaluation coefficient of the driverless vehicle with the communication evaluation coefficient threshold. If the communication evaluation coefficient of the driverless vehicle is less than the communication evaluation coefficient threshold, generate an alarm signal. If the communication evaluation coefficient of the driverless vehicle is greater than the communication evaluation coefficient threshold, no alarm signal is generated.

2. The information interaction method of a safety chip based on driverless according to claim 1, characterized in that The acquisition logic of the communication position including the full coefficient is: Each time the driverless vehicle establishes a communication connection with the infrastructure, the system records the GPS coordinates at the time of the communication connection and the coordinates of the infrastructure, calculates the distance between the two using the haversine formula, obtains the communication connection distance between the driverless vehicle and the infrastructure, and marks the communication connection distance between the driverless vehicle and the infrastructure as: , where m = 1, 2, 3, ……, M, M is a positive integer, and m is the number of each position at the intersection ahead; Obtain the success rate of communication between the driverless vehicle and the infrastructure at each location, and mark the success rate of communication between the driverless vehicle and the infrastructure at each location as: , where , is the number of successful communications at the m-th location, is the total number of attempted connections; Based on the historical records of the infrastructure at the intersection ahead, obtain the historical average success rate of communication between the driverless vehicle and the infrastructure at various positions at the intersection ahead, and mark the historical average success rate of communication between the driverless vehicle and the infrastructure at various positions at the intersection ahead as: ; The calculated communication location includes the full coefficient, and the calculation formula is: .

3. The method for information interaction of a security chip based on driverless according to claim 2, wherein, The acquisition logic of the time delay fluctuation coefficient is: Obtain the average duration required for a driverless vehicle to communicate successfully with the infrastructure at each location, and mark the average duration required for a driverless vehicle to communicate successfully with the infrastructure at each location as: ; Calculate the mean and standard deviation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location, and label the mean and standard deviation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location as: and , where , ; Calculate the coefficient of variation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location. The calculation formula is: ; where is the coefficient of variation of the average time required for a driverless vehicle to communicate successfully with the infrastructure at each location; Calculate the time delay fluctuation coefficient, and the calculation formula is as follows: .

4. A method for information interaction of a safety chip based on driverless driving according to claim 3, characterized in that Obtain the driving information of each driverless vehicle at the intersection ahead, including: Represent the driving information of each driverless vehicle at the intersection ahead by the vehicle driving data deviation coefficient; The acquisition logic of the vehicle driving data deviation coefficient is as follows: Determine the driving data of the vehicle after it enters the intersection through the safety chip of the driverless vehicle. The driving data includes the driving distance between the driverless vehicle and other vehicles. Mark the driving distance between the driverless vehicle and other vehicles as: , where n = 1, 2, 3, ……, N, N is a positive integer, and n is the serial number of the distance between the driverless vehicle and other vehicles during the driving process after the driverless vehicle is connected to the intersection infrastructure; Set the distance threshold for the driverless vehicle and label the distance threshold for the driverless vehicle as: Obtain the distance that is less than the distance threshold for the driverless vehicle from other vehicles during driving, and relabel the distance that is less than the distance threshold for the driverless vehicle from other vehicles during driving as: where i = 1, 2, 3, ……, I, I is a positive integer, and i is the number of the distance that is less than the distance threshold for the driverless vehicle; Calculate the deviation coefficient of vehicle driving data, and the calculation formula is: ; where is the deviation coefficient of vehicle driving data.

5. The information interaction method of a safety chip based on driverless according to claim 4, wherein, Obtain the road conditions information of each driverless vehicle at the intersection ahead, including: Represent the road conditions information of each driverless vehicle at the intersection ahead by the road condition danger concealment coefficient; The acquisition logic of the road condition danger concealment coefficient is: The infrastructure uploads the traffic conditions at the intersection to the cloud, and the traffic conditions include the response time of the driverless vehicle, the response distance of the driverless vehicle, and the number of changes in traffic signals; Set the standard values of the response time, response distance of the driverless vehicle, and the standard value of the number of changes of the traffic signal. Mark the standard value of the response time of the driverless vehicle as: , mark the standard value of the response distance of the driverless vehicle as: , mark the standard value of the number of changes of the traffic signal as: , obtain the response time deviation, response distance deviation of the driverless vehicle, and the deviation of the number of changes of the traffic signal. Mark the response time deviation, response distance deviation of the driverless vehicle, and the deviation of the number of changes of the traffic signal as: , , ; Calculate the road condition danger concealment coefficient, and the calculation formula is: ; where is the road condition danger concealment coefficient.

6. The information interaction method of a safety chip based on driverless according to claim 5, characterized in that, Quantify the driving situation of the driverless vehicle at the intersection ahead and generate a warning signal in a timely manner, including: Through the comprehensive analysis of the driving information of the driverless vehicle and the road condition information, the deviation coefficient of vehicle driving data and the hidden coefficient of road condition danger are weighted and calculated to construct a driving evaluation model and generate a driving evaluation coefficient. The calculation formula of the driving evaluation coefficient is: ; where is the driving evaluation coefficient of the driverless vehicle, , are the proportionality coefficients of the deviation coefficient of vehicle driving data and the hidden coefficient of road condition danger respectively, , are both greater than 0; Obtain the driving evaluation coefficients of each driverless vehicle for the road conditions ahead, and mark the driving evaluation coefficients of each driverless vehicle for the road conditions ahead as: , where k = 1, 2, 3, ……, K, K is a positive integer, and k is the number of the driverless vehicle existing in the road conditions ahead; Set the driving evaluation coefficient threshold, and mark the driving evaluation coefficient threshold as: , calculate the feedback danger coefficient, and the calculation formula is: ; where is the feedback danger coefficient; Set the feedback danger coefficient threshold, compare the feedback danger coefficient at the intersection ahead with the feedback danger coefficient threshold. If the feedback danger coefficient is greater than the feedback danger coefficient threshold, generate a warning signal. If the feedback danger coefficient is less than the feedback danger coefficient threshold, no warning signal is generated.

7. A safety chip based on driverless, which is used to implement the method for information interaction of a safety chip based on driverless according to any one of claims 1-6, characterized in that It includes an identity verification module, a data acquisition module, a secure communication monitoring module, and a driving evaluation module; An authentication module, which is used to verify the identities of the infrastructure and the driverless vehicle through a cloud trust platform to ensure the legality of both communication parties. Through public key infrastructure technology, digital certificates and signature mechanisms are used to ensure identity mutual trust between the vehicle and the infrastructure; A data collection module, which is used to collect the data deviation coefficient of vehicle driving, the road condition danger concealment coefficient, and collect the communication location including the full quantity coefficient and the time delay fluctuation coefficient; A secure communication monitoring module, which is used to construct a communication evaluation model, set a communication evaluation coefficient threshold, and compare the communication evaluation coefficient of the driverless vehicle with the communication evaluation coefficient threshold; A driving evaluation module, which is used for a driving evaluation model, sets a feedback danger coefficient threshold, and compares the feedback danger coefficient at the front intersection with the feedback danger coefficient threshold.

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