Safety chip based on unmanned driving and information interaction method thereof

Abstract

The invention discloses a safety chip based on unmanned driving and an information interaction method thereof, and particularly relates to the technical field of information interaction, comprising the steps that the safety chip of an unmanned vehicle verifies the identity of an infrastructure through a cloud trust platform, and the cloud distributes temporary session keys for the unmanned vehicle and the infrastructure; the cloud is used as a central node to complete information interaction between the unmanned vehicle and the infrastructure, the communication condition between the safety chip of the unmanned vehicle and the infrastructure is monitored in real time, the communication performance between the unmanned vehicle and the infrastructure is quantified, and the driving data of the vehicle is uploaded to the cloud through the safety chip. According to the method and the system, the traffic condition of the intersection is uploaded to the cloud through the infrastructure, and the driving condition of the unmanned vehicle at the front intersection is quantified, so that traffic accidents caused by information delay or loss can be avoided, and the condition of unsafe traffic can be avoided through information interaction among the unmanned vehicle, the infrastructure and the cloud.

Description

Technical Field

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

[0002] With the rapid development of driverless technology, more and more driverless vehicles are being put into use on actual roads. Driverless vehicles need to exchange information with road infrastructure (such as traffic lights, traffic monitoring cameras, road sensors, etc.) and other vehicles in real time to ensure safe and efficient driving. However, due to factors such as bandwidth, signal coverage and vehicle mobility, existing communication systems are often unable to guarantee timely data transmission and accuracy when driving at high speeds, in complex road conditions or when communication signals are unstable, making the performance of driverless vehicles in complex traffic environments less accurate, which may affect the rationality and safety of driving decisions. In addition, driverless vehicles need to consider driving safety issues and usually do not make flexible temporary decisions. In particular, too many driverless vehicles in complex or dynamic traffic environments will reduce traffic efficiency.

[0003] In order to solve the above two defects, a technical solution is now provided. 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 unmanned driving and an information interaction method thereof to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for interacting with a safety chip based on unmanned driving, comprising 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 a central node to complete the information exchange between the driverless vehicle and the infrastructure. S2: Determine the communication capability information and security conversion information of the unmanned vehicle by monitoring the communication between the safety chip of the unmanned vehicle and the infrastructure in real time, and quantify the communication performance between the unmanned vehicle and the infrastructure; S3: The vehicle's driving data is uploaded to the cloud through the security chip to obtain the driving information of each unmanned vehicle at the intersection ahead, and the traffic conditions of the intersection are uploaded to the cloud through the infrastructure to obtain the road condition information of each unmanned vehicle at the intersection ahead; S4: Through comprehensive analysis of the driving information and road condition information of the unmanned vehicle, the driving conditions of the unmanned vehicle at the intersection ahead are quantified, and a warning signal is generated in a timely manner.

[0006] In a preferred embodiment, determining the communication capability information of the unmanned vehicle includes: The communication capability information of the unmanned vehicle is represented by the communication position containing the total coefficient; The acquisition logic of the communication position including the total coefficient is as follows: each time the unmanned vehicle establishes a communication connection with the infrastructure, the system records the GPS coordinates and the coordinates of the infrastructure at that moment, and uses the Haversing formula to calculate the distance between the two to obtain the communication connection distance between the unmanned vehicle and the infrastructure, and marks the communication connection distance between the unmanned vehicle and the infrastructure as: , m=1, 2, 3, ..., M, M is a positive integer, and m is the number of each position of the intersection ahead; The success rate of the unmanned vehicle communicating with the infrastructure at each location is obtained, and the success rate of the unmanned vehicle communicating with the infrastructure at each location is marked as: ,in, , is the number of successful communications at the mth position, is the total number of connection attempts; According to the historical records of the infrastructure at the intersection ahead, the historical average success rate of the communication between the unmanned vehicle and the infrastructure at various locations at the intersection ahead is obtained, and the historical average success rate of the communication between the unmanned vehicle and the infrastructure at various locations at the intersection ahead is marked as: ; Calculate the total coefficient of the communication position, the calculation formula is: ;in, It is the full coefficient of the communication position.

[0007] In a preferred embodiment, determining the safety conversion information of the unmanned vehicle includes: The safety conversion information of the unmanned vehicle is represented by a time delay fluctuation coefficient; The logic for obtaining the time delay fluctuation coefficient is: obtaining the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location, and marking the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location as: ; The average and standard deviation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location are calculated, and the average and standard deviation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location are marked as: and ,in, , ; Calculate the coefficient of variation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location, using the following formula: ;in, The coefficient of variation of the average time required for the autonomous vehicle to successfully communicate with the infrastructure at various locations; Calculate the time delay fluctuation coefficient, the calculation formula is: ;in, is the time delay fluctuation coefficient.

[0008] In a preferred embodiment, quantifying the communication performance between the unmanned vehicle and the infrastructure includes: Through comprehensive analysis of the communication capability information and safety conversion information of the unmanned vehicle, the communication position total coefficient and the time delay fluctuation coefficient are weighted and calculated to build a communication evaluation model and generate a communication evaluation coefficient. The calculation formula of the communication evaluation coefficient is: ;in, is the communication evaluation coefficient, , are the proportional coefficients of the communication position total coefficient and the time delay fluctuation coefficient, respectively. , They are both greater than 0; A communication evaluation coefficient threshold is set, and the communication evaluation coefficient of the unmanned vehicle is compared with the communication evaluation coefficient threshold. If the communication evaluation coefficient of the unmanned vehicle is less than the communication evaluation coefficient threshold, an alarm signal is generated; if the communication evaluation coefficient of the unmanned vehicle is greater than the communication evaluation coefficient threshold, no alarm signal is generated.

[0009] In a preferred embodiment, obtaining driving information of each unmanned vehicle at the front intersection includes: The driving information of each unmanned vehicle at the front intersection is represented by the vehicle driving data deviation coefficient; The logic for obtaining the vehicle driving data deviation coefficient is as follows: the driving data of the vehicle after entering the intersection is determined through the safety chip of the unmanned vehicle, and the driving data includes the driving distance between the unmanned vehicle and other vehicles, and the driving distance between the unmanned vehicle and other vehicles is marked 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 intersection infrastructure; Set the unmanned vehicle distance threshold and mark the unmanned vehicle distance threshold as: , obtain the distance during the driving process where the distance to other vehicles is less than the distance threshold of the unmanned vehicle, and re-mark the distance during the driving process where the distance to other vehicles is less than the distance threshold of the unmanned 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 unmanned vehicle; Calculate the vehicle driving data deviation coefficient, the calculation formula is: ;in, is the vehicle driving data deviation coefficient.

[0010] In a preferred embodiment, obtaining the road condition information of each unmanned vehicle at the front intersection includes: The road condition information of each unmanned vehicle at the intersection ahead is represented by the road condition hazard concealment coefficient; The logic for obtaining the road condition hazard concealment coefficient is as follows: the infrastructure uploads the traffic conditions at the intersection to the cloud, and the traffic conditions include the response time of the unmanned vehicle, the response distance of the unmanned vehicle, and the number of changes of the traffic signal; Set the standard value of the response time of the unmanned vehicle, the standard value of the response distance of the unmanned vehicle, and the standard value of the number of traffic signal changes, and mark the standard value of the response time of the unmanned vehicle as: , the standard value of the response distance of the unmanned vehicle is marked as: , mark the standard value of the number of traffic signal changes as: , obtain the response time deviation of the unmanned vehicle, the response distance deviation of the unmanned vehicle, and the number of changes of the traffic signal, and mark the response time deviation of the unmanned vehicle, the response distance deviation of the unmanned vehicle, and the number of changes of the traffic signal as: , , ; Calculate the road condition danger concealment coefficient, the calculation formula is: ;in, It is the road hazard concealment coefficient.

[0011] In a preferred embodiment, the driving condition of the unmanned vehicle at the intersection ahead is quantified and a warning signal is generated in time, including: Through comprehensive analysis of the driving information and road condition information of the unmanned vehicle, the vehicle driving data deviation coefficient and the road condition hazard 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: ;in, is the driving evaluation coefficient of the driverless vehicle, , are the proportional coefficients of vehicle driving data deviation coefficient and road condition hazard concealment coefficient, , They are both greater than 0; The driving evaluation coefficients of each unmanned vehicle on the road ahead are obtained, and the driving evaluation coefficients of each unmanned vehicle on the road ahead are marked as: , where k=1, 2, 3, ..., K, K is a positive integer, and k is the number of the unmanned vehicle on the road ahead; Set the driving evaluation coefficient threshold and mark the driving evaluation coefficient threshold as: , calculate the feedback risk coefficient, the calculation formula is: ;in, is the feedback risk factor; A feedback risk coefficient threshold is set, and the feedback risk coefficient of the intersection ahead is compared with the feedback risk coefficient threshold. If the feedback risk coefficient is greater than the feedback risk coefficient threshold, a warning signal is generated; if the feedback risk coefficient is less than the feedback risk coefficient threshold, no warning signal is generated.

[0012] In a preferred embodiment, a safety chip based on unmanned driving includes an identity authentication module, a data acquisition module, a safety communication monitoring module, and a driving evaluation module; The identity authentication module is used to verify the identity of the infrastructure and the unmanned vehicle through the cloud trust platform to ensure the legitimacy of both parties in communication. Through the public key infrastructure technology, digital certificates and signature mechanisms are used to ensure the mutual trust of identity between the vehicle and the infrastructure. Data collection module, used to collect vehicle driving data deviation coefficient, road condition hazard concealment coefficient, communication location total coefficient and time delay fluctuation coefficient; A safety communication monitoring module is used to build a communication evaluation model, set a communication evaluation coefficient threshold, and compare the communication evaluation coefficient of the unmanned vehicle with the communication evaluation coefficient threshold; The driving evaluation module is used for the driving evaluation model, sets a feedback risk factor threshold, and compares the feedback risk factor of the front intersection with the feedback risk factor threshold.

[0013] Technical effects and advantages of the present invention: The present invention determines the communication capability information and security conversion information of the unmanned vehicle by monitoring the communication status between the safety chip of the unmanned vehicle and the infrastructure in real time, quantifies the unmanned vehicles capable of cloud information interaction, obtains the driving information and road condition information of each unmanned vehicle at the intersection ahead according to the unmanned vehicles capable of cloud information interaction, and determines the real-time complexity of the road condition ahead. The present invention helps to determine the communication stability between the unmanned vehicle and the infrastructure at the intersection ahead by real-time monitoring of the communication capability and the status of the safety chip, avoids traffic accidents caused by information delay or loss, and helps the unmanned vehicle to share the real-time traffic conditions at the intersection ahead through information interaction between the unmanned vehicle, the infrastructure and the cloud, provides decision support for the path planning of the rear vehicles, and avoids traffic congestion or other unsafe situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 A schematic diagram of a flow chart of a method for interacting with a safety chip based on unmanned driving according to the present invention; Figure 2 This is a schematic diagram of the structure of a safety chip based on unmanned driving in the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1 Figure 1 The present invention is a flowchart of a method for interacting with a safety chip based on unmanned driving, which specifically 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 a central node to complete the information exchange between the driverless vehicle and the infrastructure. S2: Determine the communication capability information and security conversion information of the unmanned vehicle by monitoring the communication between the safety chip of the unmanned vehicle and the infrastructure in real time, and quantify the communication performance between the unmanned vehicle and the infrastructure; S3: The vehicle's driving data is uploaded to the cloud through the security chip to obtain the driving information of each unmanned vehicle at the intersection ahead, and the traffic conditions of the intersection are uploaded to the cloud through the infrastructure to obtain the road condition information of each unmanned vehicle at the intersection ahead; S4: Through comprehensive analysis of the driving information and road condition information of the unmanned vehicle, the driving conditions of the unmanned vehicle at the intersection ahead are quantified, and a warning signal is generated in a timely manner.

[0017] The security chip is connected to the vehicle control end and is responsible for collecting, encrypting and decrypting vehicle status information (such as location, speed, driving mode, etc.), receiving data from the infrastructure, and transmitting it to the vehicle execution end; The security chip is connected to the cloud trust platform, and global identity authentication and data verification are performed through the cloud platform to ensure that both the vehicle and the infrastructure are trusted nodes, while also achieving dynamic key distribution and data verification; Before the vehicle is started and connected to the infrastructure, a two-factor authentication process needs to be completed, including mutual identity verification between the security chip and the vehicle control end, ensuring that both parties are trusted devices through the digital certificate and signature inside the vehicle, and using the hardware-bound unique identifier (such as the chip serial number) and digital signature for authentication; The security chip verifies the identity of the infrastructure through the cloud trust platform, and the cloud distributes temporary session keys to the vehicle and infrastructure for subsequent information interaction.

[0018] The information exchange between the vehicle and the infrastructure is carried out under the protection of dual authentication. The vehicle control terminal sends the status information to the security chip, which encrypts it and uploads it to the cloud through the infrastructure. The cloud verifies the integrity of the information and the credibility of the source, and then feeds the verification results back to the infrastructure. The infrastructure encrypts the processed data and sends it to the security chip, which decrypts it, verifies the data source, and sends the result to the vehicle control end for execution.

[0019] 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. 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. 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.

[0020] 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; 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.

[0021] 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.

[0022] 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.

[0023] The acquisition logic of the communication position including the total coefficient is as follows: each time the unmanned vehicle establishes a communication connection with the infrastructure, the system records the GPS coordinates (vehicle position) and the coordinates of the infrastructure at that moment, and uses the Haversing formula to calculate the distance between the two to obtain the communication connection distance between the unmanned vehicle and the infrastructure, and marks the communication connection distance between the unmanned vehicle and the infrastructure as: , m=1, 2, 3, ..., M, M is a positive integer, and m is the number of each position of the intersection ahead; It should be noted that, generally, the closer the communication connection distance between the driverless vehicle and the infrastructure, the higher the probability of successful communication between the driverless vehicle and the infrastructure. The various positions of the intersection ahead are set by professional staff.

[0024] The success rate of the unmanned vehicle communicating with the infrastructure at each location is obtained, and the success rate of the unmanned vehicle communicating with the infrastructure at each location is marked as: ,in, , is the number of successful communications at the mth position, is the total number of connection attempts; According to the historical records of the infrastructure at the intersection ahead, the historical average success rate of the communication between the unmanned vehicle and the infrastructure at various locations at the intersection ahead is obtained, and the historical average success rate of the communication between the unmanned vehicle and the infrastructure at various locations at the intersection ahead is marked as: ; Calculate the total coefficient of the communication position, the calculation formula is: ;in, It is the full coefficient of the communication position.

[0025] It can be seen from the formula that the larger the communication location total coefficient, the stronger the communication capability between the unmanned vehicle and the infrastructure, which means that the data transmitted by the unmanned vehicle is likely to be successfully received by the infrastructure.

[0026] The logic for obtaining the time delay fluctuation coefficient is: obtaining the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location, and marking the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location as: ; The average and standard deviation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location are calculated, and the average and standard deviation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location are marked as: and ,in, , ; Calculate the coefficient of variation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location, using the following formula: ;in, The coefficient of variation of the average time required for the autonomous vehicle to successfully communicate with the infrastructure at various locations; Calculate the time delay fluctuation coefficient, the calculation formula is: ;in, is the time delay fluctuation coefficient.

[0027] It can be seen from the formula that the larger the time delay fluctuation coefficient, the different communication qualities of the unmanned vehicle in different locations, which means that the unmanned vehicle may encounter different signal strengths or interference in different geographical locations, and the wireless communication environment is susceptible to interference, such as mobile communication towers, Wi-Fi networks, satellite signals, etc.

[0028] Through comprehensive analysis of the communication capability information and safety conversion information of the unmanned vehicle, the communication position total coefficient and the time delay fluctuation coefficient are weighted and calculated to build a communication evaluation model and generate a communication evaluation coefficient. The calculation formula of the communication evaluation coefficient is: ;in, is the communication evaluation coefficient, , are the proportional coefficients of the communication position total coefficient and the time delay fluctuation coefficient, respectively. , Both are greater than 0.

[0029] A communication evaluation coefficient threshold is set, and the communication evaluation coefficient of the unmanned vehicle is compared with the communication evaluation coefficient threshold. If the communication evaluation coefficient of the unmanned vehicle is less than the communication evaluation coefficient threshold, an alarm signal is generated, indicating that the unmanned vehicle cannot operate in a safe and stable communication environment, and the driving data generated by the unmanned vehicle may be unreliable and cannot be uploaded to the cloud for analysis. If the communication evaluation coefficient of the unmanned vehicle is greater than the communication evaluation coefficient threshold, no alarm signal is generated, indicating that the unmanned vehicle can communicate with the infrastructure at the intersection ahead in a timely manner and exchange information.

[0030] Example 2 The above embodiment evaluates the information interaction behavior performance of the unmanned vehicle through the communication between the unmanned vehicle and the infrastructure in the process of moving from the rear intersection to the front intersection. This embodiment quantifies the road safety of the front intersection based on the unmanned vehicle that does not generate an alarm signal, and promptly feeds back the road safety to the unmanned vehicle behind.

[0031] According to the unmanned vehicles that do not generate alarm signals, the infrastructure of the intersection ahead communicates with the safety chip of the unmanned vehicle, and exchanges data with the infrastructure of the intersection ahead through wireless communication technology to determine the unmanned vehicle connected to the infrastructure of the intersection ahead. The cloud serves as a coordination hub for unmanned vehicles and infrastructure, and uploads the vehicle's driving data to the cloud through the safety chip to obtain driving information of each unmanned vehicle at the intersection ahead. The traffic conditions of the intersection are uploaded to the cloud through the infrastructure to obtain road condition information of each unmanned vehicle at the intersection ahead. The driving information of each unmanned vehicle at the intersection ahead is represented by a vehicle driving data deviation coefficient, and the road condition information of each unmanned vehicle at the intersection ahead is represented by a road condition hazard concealment coefficient.

[0032] The length of time that the driverless vehicle is connected to the infrastructure at the intersection ahead is recorded in real time, and the length of time that the driverless vehicle is connected to the infrastructure is marked as: T.

[0033] It should be noted that the driverless vehicle and the infrastructure exchange data through the Vehicle-to-Infrastructure (V2I) communication protocol. When the vehicle approaches the intersection, the infrastructure will begin to receive the vehicle's data (such as location, speed, direction, etc.). At the same time, the infrastructure will also send traffic light status, road condition information, etc. to the vehicle. When the vehicle enters the communication coverage of the intersection, the start time of the connection is recorded. When the vehicle leaves the communication coverage of the intersection or the communication is interrupted, the end time of the connection is recorded. If the vehicle does not leave the communication coverage of the intersection, the connection start time to the current time is recorded.

[0034] The logic for obtaining the vehicle driving data deviation coefficient is as follows: the driving data of the vehicle after entering the intersection is determined through the safety chip of the unmanned vehicle, and the driving data includes the driving distance between the unmanned vehicle and other vehicles, and the driving distance between the unmanned vehicle and other vehicles is marked 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 intersection infrastructure; It should be noted that driverless vehicles can identify the distance from other vehicles through sensors. Usually, driverless vehicles will maintain a certain distance from other vehicles. However, in complex situations such as dangerous road conditions and road congestion, driverless vehicles cannot maintain a safe distance from other vehicles.

[0035] Set the unmanned vehicle distance threshold and mark the unmanned vehicle distance threshold as: , obtain the distance during the driving process where the distance to other vehicles is less than the distance threshold of the unmanned vehicle, and re-mark the distance during the driving process where the distance to other vehicles is less than the distance threshold of the unmanned 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 unmanned vehicle; Calculate the vehicle driving data deviation coefficient, the calculation formula is: ;in, is the vehicle driving data deviation coefficient.

[0036] It can be seen from the formula that the larger the deviation coefficient of vehicle driving data is, the greater the potential risk of the driverless vehicle in driving at the intersection ahead and the possible complex road conditions.

[0037] The logic for obtaining the road condition hazard concealment coefficient is as follows: the infrastructure uploads the traffic conditions at the intersection to the cloud, and the traffic conditions include the response time of the unmanned vehicle, the response distance of the unmanned vehicle, and the number of changes of the traffic signal; Set the standard value of the response time of the unmanned vehicle, the standard value of the response distance of the unmanned vehicle, and the standard value of the number of traffic signal changes, and mark the standard value of the response time of the unmanned vehicle as: , the standard value of the response distance of the unmanned vehicle is marked as: , mark the standard value of the number of traffic signal changes as: , obtain the response time deviation of the unmanned vehicle, the response distance deviation of the unmanned vehicle, and the number of changes of the traffic signal, and mark the response time deviation of the unmanned vehicle, the response distance deviation of the unmanned vehicle, and the number of changes of the traffic signal as: , , ; Calculate the road condition danger concealment coefficient, the calculation formula is: ;in, It is the road hazard concealment coefficient.

[0038] It should be noted that the response time of an unmanned vehicle refers to the time interval from when the traffic signal changes to a driving instruction to when the unmanned vehicle starts, because the unmanned vehicle considers the driving safety of the vehicle and maintains a safe distance between vehicles. Therefore, the longer the response time of the unmanned vehicle, the more delay there is in the response of the unmanned vehicle, and the lower the driving efficiency of the vehicle. The response distance of an unmanned vehicle refers to the distance the unmanned vehicle travels from the time the traffic signal changes to a driving instruction to the time the traffic signal changes to a no-driving instruction. The longer the response distance of the unmanned vehicle, the more it will react with a certain delay, and the lower the driving efficiency of the vehicle. The number of traffic signal changes indicates the number of times the traffic signal changes after the driverless vehicle is connected to the infrastructure. The more traffic signal changes, the more likely the driverless vehicle is stuck in the road ahead. The reasons for the longer 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 complicated, 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 according to environmental changes in time, resulting in lower driving efficiency of the vehicle.

[0039] It can be seen from the formula that the greater the road hazard concealment coefficient, the lower the driving efficiency of the unmanned vehicle on the road ahead, indicating that the road ahead may have complex road conditions.

[0040] Through comprehensive analysis of the driving information and road condition information of the unmanned vehicle, the vehicle driving data deviation coefficient and the road condition hazard 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: ;in, is the driving evaluation coefficient of the driverless vehicle, , are the proportional coefficients of vehicle driving data deviation coefficient and road condition hazard concealment coefficient, respectively. , Both are greater than 0.

[0041] The driving evaluation coefficients of each unmanned vehicle on the road ahead are obtained, and the driving evaluation coefficients of each unmanned vehicle on the road ahead are marked as: , where k=1, 2, 3, ..., K, K is a positive integer, and k is the number of the unmanned vehicle on the road ahead; Set the driving evaluation coefficient threshold and mark the driving evaluation coefficient threshold as: , calculate the feedback risk coefficient, the calculation formula is: ;in, is the feedback risk factor.

[0042] It should be noted that the driving evaluation coefficient threshold is set by professional staff. If the driving evaluation coefficient of the unmanned vehicle is greater than the driving evaluation coefficient threshold, it means that the driving condition of the unmanned vehicle at the intersection ahead is poor. Conversely, if the driving evaluation coefficient of the unmanned vehicle is less than the driving evaluation coefficient threshold, it means that the driving condition of the unmanned vehicle at the intersection ahead is good.

[0043] A feedback danger coefficient threshold is set, and the feedback danger coefficient of the intersection ahead is compared with the feedback danger coefficient threshold. If the feedback danger coefficient is greater than the feedback danger coefficient threshold, a warning signal is generated. The warning signal is sent from the cloud to the unmanned vehicle that is about to enter the intersection ahead, notifying the unmanned vehicle that is about to enter the intersection ahead to change its driving route and avoid entering the intersection ahead. If the feedback danger coefficient is less than the feedback danger coefficient threshold, no warning signal is generated.

[0044] The present invention determines the communication capability information and security conversion information of the unmanned vehicle by monitoring the communication status between the safety chip of the unmanned vehicle and the infrastructure in real time, quantifies the unmanned vehicles capable of cloud information interaction, obtains the driving information and road condition information of each unmanned vehicle at the intersection ahead according to the unmanned vehicles capable of cloud information interaction, and determines the real-time complexity of the road condition ahead. The present invention helps to determine the communication stability between the unmanned vehicle and the infrastructure at the intersection ahead by real-time monitoring of the communication capability and the status of the safety chip, avoids traffic accidents caused by information delay or loss, and helps the unmanned vehicle to share the real-time traffic conditions at the intersection ahead through information interaction between the unmanned vehicle, the infrastructure and the cloud, provides decision support for the path planning of the rear vehicles, and avoids traffic congestion or other unsafe situations.

[0045] Example 3 Figure 2 It is a structural schematic diagram of a safety chip based on unmanned driving of the present invention, including an identity authentication module, a data acquisition module, a safety communication monitoring module, and a driving evaluation module; The identity authentication module is used to verify the identity of the infrastructure and the unmanned vehicle through the cloud trust platform to ensure the legitimacy of both parties in communication. Through the public key infrastructure technology, digital certificates and signature mechanisms are used to ensure the mutual trust of identity between the vehicle and the infrastructure. Data collection module, used to collect vehicle driving data deviation coefficient, road condition hazard concealment coefficient, communication location total coefficient and time delay fluctuation coefficient; A safety communication monitoring module is used to build a communication evaluation model, set a communication evaluation coefficient threshold, and compare the communication evaluation coefficient of the unmanned vehicle with the communication evaluation coefficient threshold; The driving evaluation module is used for the driving evaluation model, sets a feedback risk factor threshold, and compares the feedback risk factor of the front intersection with the feedback risk factor threshold.

[0046] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0047] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0048] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0049] Those of ordinary skill 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 to be beyond the scope of this application.

[0050] Those skilled in the art can 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.

[0051] In the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0052] 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 be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0053] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for interacting with safety chips based on unmanned driving, characterized in that: The specific steps include: 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 a central node to complete the information exchange between the driverless vehicle and the infrastructure. S2: Determine the communication capability information and security conversion information of the unmanned vehicle by monitoring the communication between the safety chip of the unmanned vehicle and the infrastructure in real time, and quantify the communication performance between the unmanned vehicle and the infrastructure; S3: The vehicle's driving data is uploaded to the cloud through the security chip to obtain the driving information of each unmanned vehicle at the intersection ahead, and the traffic conditions of the intersection are uploaded to the cloud through the infrastructure to obtain the road condition information of each unmanned vehicle at the intersection ahead; S4: Through comprehensive analysis of the driving information and road condition information of the unmanned vehicle, the driving conditions of the unmanned vehicle at the intersection ahead are quantified, and a warning signal is generated in a timely manner.

2. The method for information interaction of a safety chip based on unmanned driving according to claim 1, characterized in that: Determine the communication capabilities of the unmanned vehicle, including: The communication capability information of the unmanned vehicle is represented by the communication position containing the total coefficient; The acquisition logic of the communication position including the total coefficient is as follows: each time the unmanned vehicle establishes a communication connection with the infrastructure, the system records the GPS coordinates and the coordinates of the infrastructure at that moment, and uses the Haversing formula to calculate the distance between the two to obtain the communication connection distance between the unmanned vehicle and the infrastructure, and marks the communication connection distance between the unmanned vehicle and the infrastructure as: , m=1, 2, 3, ..., M, M is a positive integer, and m is the number of each position of the intersection ahead; The success rate of the unmanned vehicle communicating with the infrastructure at each location is obtained, and the success rate of the unmanned vehicle communicating with the infrastructure at each location is marked as: ,in, , is the number of successful communications at the mth position, is the total number of connection attempts; According to the historical records of the infrastructure at the intersection ahead, the historical average success rate of the communication between the unmanned vehicle and the infrastructure at various locations at the intersection ahead is obtained, and the historical average success rate of the communication between the unmanned vehicle and the infrastructure at various locations at the intersection ahead is marked as: ; Calculate the total coefficient of the communication position, the calculation formula is: ;in, It is the full coefficient of the communication position.

3. The method for information interaction of a safety chip based on unmanned driving according to claim 2 is characterized in that: Determine safe transition information for driverless vehicles, including: The safety conversion information of the unmanned vehicle is represented by a time delay fluctuation coefficient; The logic for obtaining the time delay fluctuation coefficient is: obtaining the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location, and marking the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location as: ; The average and standard deviation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location are calculated, and the average and standard deviation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location are marked as: and ,in, , ; Calculate the coefficient of variation of the average time required for the unmanned vehicle to successfully communicate with the infrastructure at each location, using the following formula: ;in, The coefficient of variation of the average time required for the autonomous vehicle to successfully communicate with the infrastructure at various locations; Calculate the time delay fluctuation coefficient, the calculation formula is: ;in, is the time delay fluctuation coefficient.

4. The method for information interaction of a safety chip based on unmanned driving according to claim 3 is characterized in that: Quantify the communication performance of autonomous vehicles with infrastructure, including: Through comprehensive analysis of the communication capability information and safety conversion information of the unmanned vehicle, the communication position total coefficient and the time delay fluctuation coefficient are weighted and calculated to build a communication evaluation model and generate a communication evaluation coefficient. The calculation formula of the communication evaluation coefficient is: ;in, is the communication evaluation coefficient, , are the proportional coefficients of the communication position total coefficient and the time delay fluctuation coefficient, respectively. , They are both greater than 0; A communication evaluation coefficient threshold is set, and the communication evaluation coefficient of the unmanned vehicle is compared with the communication evaluation coefficient threshold. If the communication evaluation coefficient of the unmanned vehicle is less than the communication evaluation coefficient threshold, an alarm signal is generated; if the communication evaluation coefficient of the unmanned vehicle is greater than the communication evaluation coefficient threshold, no alarm signal is generated.

5. The method for interacting with safety chips based on unmanned driving according to claim 4 is characterized in that: Obtain driving information of each driverless vehicle at the intersection ahead, including: The driving information of each unmanned vehicle at the front intersection is represented by the vehicle driving data deviation coefficient; The logic for obtaining the vehicle driving data deviation coefficient is as follows: the driving data of the vehicle after entering the intersection is determined through the safety chip of the unmanned vehicle, and the driving data includes the driving distance between the unmanned vehicle and other vehicles, and the driving distance between the unmanned vehicle and other vehicles is marked 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 intersection infrastructure; Set the unmanned vehicle distance threshold and mark the unmanned vehicle distance threshold as: , obtain the distance during the driving process where the distance to other vehicles is less than the distance threshold of the unmanned vehicle, and re-mark the distance during the driving process where the distance to other vehicles is less than the distance threshold of the unmanned 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 unmanned vehicle; Calculate the vehicle driving data deviation coefficient, the calculation formula is: ;in, is the vehicle driving data deviation coefficient.

6. The method for interacting with safety chips based on unmanned driving according to claim 5 is characterized in that: Obtain traffic information of each driverless vehicle at the intersection ahead, including: The road condition information of each unmanned vehicle at the intersection ahead is represented by the road condition hazard concealment coefficient; The logic for obtaining the road condition hazard concealment coefficient is as follows: the infrastructure uploads the traffic conditions at the intersection to the cloud, and the traffic conditions include the response time of the unmanned vehicle, the response distance of the unmanned vehicle, and the number of changes of the traffic signal; Set the standard value of the response time of the unmanned vehicle, the standard value of the response distance of the unmanned vehicle, and the standard value of the number of traffic signal changes, and mark the standard value of the response time of the unmanned vehicle as: , the standard value of the response distance of the unmanned vehicle is marked as: , mark the standard value of the number of traffic signal changes as: , obtain the response time deviation of the unmanned vehicle, the response distance deviation of the unmanned vehicle, and the number of changes of the traffic signal, and mark the response time deviation of the unmanned vehicle, the response distance deviation of the unmanned vehicle, and the number of changes of the traffic signal as: , , ; Calculate the road condition danger concealment coefficient, the calculation formula is: ;in, It is the road hazard concealment coefficient.

7. The method for information interaction of a safety chip based on unmanned driving according to claim 6 is characterized in that: Quantify the driving conditions of the driverless vehicle at the intersection ahead and generate early warning signals in a timely manner, including: Through comprehensive analysis of the driving information and road condition information of the unmanned vehicle, the vehicle driving data deviation coefficient and the road condition hazard 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: ;in, is the driving evaluation coefficient of the driverless vehicle, , are the proportional coefficients of vehicle driving data deviation coefficient and road condition hazard concealment coefficient, respectively. , They are both greater than 0; The driving evaluation coefficients of each unmanned vehicle on the road ahead are obtained, and the driving evaluation coefficients of each unmanned vehicle on the road ahead are marked as: , where k=1, 2, 3, ..., K, K is a positive integer, and k is the number of the unmanned vehicle on the road ahead; Set the driving evaluation coefficient threshold and mark the driving evaluation coefficient threshold as: , calculate the feedback risk coefficient, the calculation formula is: ;in, is the feedback risk factor; A feedback risk coefficient threshold is set, and the feedback risk coefficient of the intersection ahead is compared with the feedback risk coefficient threshold. If the feedback risk coefficient is greater than the feedback risk coefficient threshold, a warning signal is generated; if the feedback risk coefficient is less than the feedback risk coefficient threshold, no warning signal is generated.

8. A safety chip based on unmanned driving, used to implement the safety chip information interaction method based on unmanned driving according to any one of claims 1 to 7, characterized in that: It includes identity authentication module, data acquisition module, safety communication monitoring module and driving assessment module; The identity authentication module is used to verify the identity of the infrastructure and the unmanned vehicle through the cloud trust platform to ensure the legitimacy of both parties in communication. Through the public key infrastructure technology, digital certificates and signature mechanisms are used to ensure the mutual trust of identity between the vehicle and the infrastructure. Data collection module, used to collect vehicle driving data deviation coefficient, road condition hazard concealment coefficient, communication location total coefficient and time delay fluctuation coefficient; A safety communication monitoring module is used to build a communication evaluation model, set a communication evaluation coefficient threshold, and compare the communication evaluation coefficient of the unmanned vehicle with the communication evaluation coefficient threshold; The driving evaluation module is used for the driving evaluation model, sets a feedback risk factor threshold, and compares the feedback risk factor of the front intersection with the feedback risk factor threshold.

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