Multi-Sensor compatible method for vehicle-mounted camera module

Abstract

The invention relates to the field of vehicle-mounted camera module driving systems, and provides a multi-Sensor compatible method for a vehicle-mounted camera module, which comprises the following steps of: presetting a standard instruction set and Sensor script configuration files of manufacturers in a storage unit, and realizing configurability of register operation logic by utilizing a script explanation engine; the system can automatically identify and load the matched Sensor driving script without changing a middleware code or recompiling a driving library. The method comprises the mechanisms of script security authentication, sandbox isolation during operation, script compiling and cache optimization and the like, so that the flexibility and efficiency of multi-manufacturer Sensor adaptation are improved, the development and maintenance cost is reduced, the system security and operation stability are enhanced, and high compatibility and expandability of vehicle-mounted camera module drive integration are realized.

Description

Technical Field

[0001] This invention relates to the field of vehicle camera driver middleware and multi-sensor compatible control technology, and in particular to a multi-sensor compatible method for vehicle camera modules. Background Technology

[0002] Currently, the mainstream approach for achieving multi-sensor compatibility in automotive camera modules is to directly integrate proprietary drivers from multiple sensor manufacturers and use compatibility software to encapsulate and adapt the necessary calling interfaces for different drivers. This approach typically requires system development teams to analyze and perform secondary development on each manufacturer's driver interface standard, compiling and integrating each manufacturer's driver files separately into the automotive system. As camera system applications become increasingly complex and heterogeneous multi-sensor scenarios become more frequent, the requirements for driver code compatibility have significantly increased. This approach struggles to address the problems of cumbersome adaptation processes and high resource consumption caused by inconsistent interface specifications among manufacturers. Summary of the Invention

[0003] This application provides a multi-sensor compatible method for vehicle camera modules, aiming to solve one of the problems or issues of the prior art mentioned in the background.

[0004] This application provides a multi-sensor compatible method for an automotive camera module, specifically including: S1: Lightweight Driver Middleware Layer (LDML) and script configuration files for multiple manufacturers' sensors are pre-installed in the storage unit of the vehicle camera module. The script configuration files define the differentiated register operation logic for the corresponding sensors based on the standard instruction set.

[0005] S2: When the vehicle camera module is powered on and initialized, it identifies the connected sensor model through the I2C or SerDes communication interface and queries the local device mapping table to obtain its unique identifier.

[0006] S3: Based on the identified Sensor model identifier, load the corresponding script configuration file from the storage unit, and perform syntax verification and cache preheating by the lightweight interpretation engine built into the lightweight driver middleware layer.

[0007] S4: Converts control requests issued by the cockpit software into predefined standard instruction codes, and the lightweight driver middleware layer finds the corresponding script logic based on the current sensor type.

[0008] S5: Based on script logic, it performs low-level register access and timing control operations, completes sensor initialization, parameter configuration, image flow control and other operations, and feeds back the execution status to the cockpit software.

[0009] S6: Dynamically detects script execution frequency at runtime, compiles frequently used script segments into bytecode cache to avoid repeated parsing, thereby improving system response performance.

[0010] S7: By using a runtime sandbox mechanism in the lightweight driver middleware layer, direct access permissions of scripts to hardware resources are restricted, ensuring the security of the script execution process.

[0011] S8: When adding a new sensor model, only a script configuration file conforming to the standard instruction set specification needs to be provided and signed for authentication to be included in the system. There is no need to modify the middleware code or recompile the driver library.

[0012] This application provides a multi-sensor compatible method for an automotive camera module, which has the following advantages: (1) By introducing a lightweight driver middleware layer (LDML) and a two-layer abstraction approach of standard instruction set + script configuration, the problem of heterogeneous and complex adaptation of multi-vendor sensor driver interfaces is significantly simplified. All control logic is uniformly encapsulated in the standard interface and script of the middleware, which reduces the difficulty of encapsulation and scheduling of subsequent new sensors, effectively avoids the burden of repeated development and maintenance caused by frequent changes in driver layer interfaces, and fundamentally improves the general adaptability and engineering implementation efficiency of driver software.

[0013] (2) Through standard instruction sets and script-based differential behavior descriptions, the compatibility software can dynamically load the corresponding script files for different sensor models on demand, without recompiling the driver library or modifying the main control code, which greatly shortens the production and adaptation cycle of new sensors. At the same time, the script files are efficiently stored and run in compressed text and bytecode form, and support hot compilation caching, LRU cleanup and other mechanisms, which greatly reduces the storage and running memory requirements of the embedded platform, and minimizes the driver size and execution resources, making it very suitable for resource-constrained scenarios in the automotive environment.

[0014] (3) By using the runtime sandbox mechanism and digital signature authentication strategy of the driver middleware, the script’s access to the underlying hardware resources is strictly restricted, and the integrity of the script content is verified in real time. This can effectively prevent malicious code or abnormal logic from affecting system stability, ensure the long-term safe and controllable operation of the camera module, and provide a higher level of security protection for automotive electronic systems.

[0015] (4) This solution significantly expands the maintainability and upgradeability of the system. When adding or replacing a sensor model, only a new script file needs to be created and certified to be seamlessly integrated into the existing platform. This greatly enhances the product's ability to adapt to market changes and technological evolution, reduces resource waste, improves production and maintenance efficiency, and gives the vehicle camera module strong product lifecycle management capabilities and market competitiveness. Attached Figure Description

[0016] Figure 1 This is the main flowchart of a multi-sensor compatible method for vehicle camera modules.

[0017] Figure 2 This is a sub-flowchart of a multi-sensor compatible method for automotive camera modules.

[0018] Figure 3 This is another sub-flowchart of a multi-sensor compatible method for automotive camera modules. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] like Figure 1 As shown, this application provides a multi-sensor compatible method for an automotive camera module, specifically including: S1: Lightweight Driver Middleware Layer (LDML) and script configuration files for multiple manufacturers' sensors are pre-installed in the storage unit of the vehicle camera module. The script configuration files define the differentiated register operation logic for the corresponding sensors based on the standard instruction set.

[0022] S2: When the vehicle camera module is powered on and initialized, it identifies the connected sensor model through the I2C or SerDes communication interface and queries the local device mapping table to obtain its unique identifier.

[0023] S3: Based on the identified Sensor model identifier, load the corresponding script configuration file from the storage unit, and perform syntax verification and cache preheating by the lightweight interpretation engine built into the lightweight driver middleware layer.

[0024] S4: Converts control requests issued by the cockpit software into predefined standard instruction codes, and the lightweight driver middleware layer finds the corresponding script logic based on the current sensor type.

[0025] S5: Based on script logic, it performs low-level register access and timing control operations, completes sensor initialization, parameter configuration, image flow control and other operations, and feeds back the execution status to the cockpit software.

[0026] S6: Dynamically detects script execution frequency at runtime, compiles frequently used script segments into bytecode cache to avoid repeated parsing, thereby improving system response performance.

[0027] S7: By using a runtime sandbox mechanism in the lightweight driver middleware layer, direct access permissions of scripts to hardware resources are restricted, ensuring the security of the script execution process.

[0028] S8: When adding a new sensor model, only a script configuration file conforming to the standard instruction set specification needs to be provided and signed for authentication to be included in the system. There is no need to modify the middleware code or recompile the driver library.

[0029] Step S1: A lightweight driver middleware layer (LDML) and script configuration files for multiple vendor sensors are pre-installed in the storage unit of the vehicle camera module. These script configuration files define differentiated register operation logic for the corresponding sensors based on a standard instruction set. Specifically, this includes: S1.1: Based on the hardware architecture of the vehicle camera module, a lightweight driver middleware layer (LDML) is pre-installed in the non-volatile storage unit. The lightweight driver middleware layer includes a standard instruction set definition module, a script interpretation engine, a device mapping table management module, and a security sandbox mechanism to build a basic operating environment for dynamic driver adaptation.

[0030] Lightweight Driver Middleware Layer (LDML) is a dedicated software abstraction layer deeply integrated into automotive camera modules. Its core design goal is to achieve complete decoupling between hardware and software and efficient, seamless compatibility with image sensors from multiple vendors. This middleware constructs a crucial isolation and adaptation layer between the hardware driver and the upper-level cockpit application software. Its core technology includes a lightweight interpretation engine and a set of predefined standardized instructions. LDML works by pre-loading and dynamically loading script configuration files for different sensors in the module's storage unit. When the system powers on, it automatically identifies the sensor model through communication interfaces such as I2C and indexes and loads the corresponding configuration script, which is then executed by the interpretation engine for syntax verification and initialization. It translates and maps general control requests (such as initialization, parameter adjustment, and image flow control) issued by upper-level applications into specific low-level instructions that conform to the timing of specific sensor register operations in real time, thereby shielding the significant differences in control logic, register mapping, and timing requirements between different sensors. To further improve efficiency, LDML features runtime optimization capabilities, compiling frequently called script segments into bytecode caches to reduce parsing overhead. Simultaneously, its built-in sandbox security mechanism ensures secure and controllable access to hardware resources by third-party scripts through permission restrictions. The greatest advantage of this architecture lies in its extreme flexibility and scalability. When supporting new sensor models is required, only a script configuration file conforming to standard instruction set specifications and certified with signature is needed for plug-and-play functionality, without requiring any modification or recompilation of the middleware core code or system drivers.

[0031] Under the hardware architecture definition of the vehicle camera module, the deployment and configuration task of the pre-built lightweight driver middleware layer (LDML) in the non-volatile storage unit is executed to ensure that it has complete functional modules as the basic operating environment for dynamic driver adaptation.

[0032] A modular embedding method (parameters: standard instruction set definition module, script interpretation engine, device mapping table management module, security sandbox mechanism) is adopted to load and logically bind LDML functional units one by one, so that the interfaces between modules conform to a unified calling protocol.

[0033] Furthermore, through the address mapping initialization algorithm (parameters: device bus I2C, SerDes link), the device mapping table management module and the hardware identification unit are synchronously initialized, and mapping table index data is generated, providing a data foundation for the subsequent sensor identification technology chain.

[0034] Furthermore, by using a script parser pre-configuration strategy (parameters: cache capacity within the interpretation engine, script format specifications), the runtime environment of the script interpretation engine is built, and an executable parsing configuration file is obtained, providing a foundation for syntax parsing and cache control for dynamic script execution.

[0035] Furthermore, by using a security policy injection method (parameters: access control matrix, public key authentication sequence), access control of the security sandbox mechanism is implemented and a permission mapping table is generated, so that the script execution and hardware access processes are protected by a security encapsulation layer.

[0036] By combining modular deployment with pre-initialization algorithms, the hardware architecture conditions of the previous step are transformed into a storage unit data layout that supports dynamic loading of multiple sensor drivers, thus enabling a basic operating environment that compatible software can directly call upon during the power-on phase.

[0037] For example, the compatible system hardware includes a SerDes chip, a sensor chip, a storage unit, and a PMIC power management chip; the SerDes chip, sensor chip, and storage unit are electrically connected to the PMIC power management chip, the SerDes chip is electrically connected to the sensor chip, and the storage unit is electrically connected to the sensor chip; the storage unit is the original Flash chip of the vehicle camera module, or a newly added IIC interface E2PROM chip, and the compatible system stores the drivers, IIC address mapping tables, and parameter configuration data of sensor chips from different manufacturers.

[0038] In a certain automotive camera module, the non-volatile storage unit uses a 256MB SPI NAND Flash, internally partitioned into an LDML core code area (64MB), a script library area (128MB), and a security policy area (64MB). The standard instruction set definition module contains 20 instruction codes, including sensor initialization, register read / write, parameter configuration, and image flow control, with each instruction code having a fixed length. Bytes. The script interpretation engine uses a lightweight parser based on the Lua 5.3 kernel, with a cache capacity configured as follows. MB, to support up to 100 simultaneous loads One script file. During the power-on self-test phase, the device mapping table management module performs device ID reading via the I2C bus, using the register address. to The data block completes the acquisition of manufacturer and model information and generates a unique device identifier (Device UID). During the initialization phase, the security sandbox mechanism loads a public key generated based on the RSA-2048 algorithm, parses the access control matrix within the security policy area, and restricts unauthenticated scripts from accessing the GPIO and I2C register range. to During the verification phase, the system can be tested after power-on. The system completes LDML and security sandbox initialization in seconds and successfully identifies three sensor models from different manufacturers, ensuring that the dynamic driver adaptation process remains highly efficient in resource-constrained environments.

[0039] S1.2: Functional abstraction is performed on sensor drivers from multiple vendors, common control logic is extracted and defined as a standard instruction set. The standard instruction set includes core operations such as sensor initialization, register reading and writing, parameter configuration, and image flow control, in order to form a unified driver calling interface specification.

[0040] Based on the established runtime environment of Lightweight Driver Middleware Layer (LDML), functional abstraction processing of sensor drivers from multiple vendors is performed in a unified format to obtain standardized control interface input data required for subsequent scripted modeling.

[0041] The interface behavior analysis method (parameters: list of driver functions from various vendors, register access sequence, timing constraint table) is used to perform static analysis on the drivers of various vendors, extract the register read and write instructions, initialization step sequence and parameter setting mode involved in the driver, and realize the characteristic description of the core functions of different drivers.

[0042] Furthermore, by using a control logic clustering algorithm (parameters: operation type label, event triggering condition, output status code set), the extracted functional feature vectors are clustered according to operation type to form four core operation sets covering sensor initialization, register read / write, parameter configuration, and image flow control, and a general operation template for each set is obtained.

[0043] Furthermore, an instruction set abstraction construction method (parameters: operation set template, encoding rule table) is adopted to abstract various core operation sets into standard instruction sets with fixed formats. Each instruction is bound to a unique binary code and calling semantics to achieve a consistent definition of the calling interface across different vendor sensors.

[0044] Furthermore, an interface consistency verification algorithm (parameters: standard instruction set, vendor driver function mapping table) is used to verify the bidirectional mapping relationship between each vendor driver and the standard instruction set, ensuring that each type of standard instruction has a corresponding underlying operation implementation, and generating an interface coverage metric.

[0045] Through the above instruction set construction and verification process, the functional abstraction results of the previous step are transformed into a standardized set of control interfaces that can be directly used for scripted modeling, thereby achieving consistency and scalability of the calling logic of sensor drivers from different vendors.

[0046] For example, three sensor models from manufacturers A, B, and C are selected in the vehicle camera module, and the function lists and register access sequences of their original factory drivers are obtained. For manufacturer A's driver, the initialization register write order is {0x01→0x03→0x07}, the image flow control register is 0x10, and the status feedback register is 0x20; for manufacturer B's driver, the initialization register order is {0x11→0x13→0x17}, and the image flow control register is 0x30; for manufacturer C's driver, the initialization register order is {0x21→0x23→0x27}, and the image flow control register is 0x40. These initialization operations are clustered to form a "INIT" class of standard operation templates; the image flow start / stop operations are clustered to form a "STREAM_CTRL" class of standard operation templates. A unique instruction code is defined for each type of operation template; for example, the INIT class is defined as... The STREAM_CTRL class is defined as follows: The corresponding binary representation and calling semantics are bound in the encoding rule table. During the interface consistency verification phase, the generated interface coverage metric shows that all three vendor drivers cover the four standard instruction sets, achieving full interface coverage. This abstract instruction set is then used as input for scripted modeling to ensure that the driver calling interface remains consistent when changing any vendor's sensor, enabling the system to achieve rapid adaptation and stable operation even in resource-constrained environments.

[0047] S1.3: Based on the standard instruction set, the differentiated register operation logic of sensors from various manufacturers is scripted and modeled to generate an executable script file. The script file uses a lightweight scripting language (such as Lua or JavaScript) to describe the specific register access sequence, timing control logic and status feedback mechanism, so as to realize the configurability of the driving logic.

[0048] Based on the standard instruction set input conditions, the abstracted differential register operation logic of sensors from different manufacturers is used as the execution object for scripted modeling.

[0049] A differential logic mapping method (parameters: standard instruction set, vendor register operation set) is used to achieve a one-to-one mapping of the function of specific register access sequences of each vendor to standardized control instructions.

[0050] Furthermore, a lightweight scripting language modeling method (parameters: Lua / JavaScript syntax rules, register sequence data, timing constraint parameters) is used to achieve a programmatic description of the differential register access process and obtain an executable scripting language source file.

[0051] Furthermore, by using a communication timing verification algorithm (parameters: register access sequence, delay parameters, and status polling conditions), the constraint modeling of access order and execution time in the script logic is realized, and control flow definition data is generated.

[0052] Furthermore, by using a state feedback encapsulation method (parameters: Sensor register response code, result mapping table), the state reading and standard feedback interface in the script logic are encapsulated, and the implementation part of the feedback mechanism in the script is constructed.

[0053] By using script construction and encapsulation, the results of the previous step are transformed into an executable script file with register access, timing control, and status feedback, thereby achieving the configurable effect of the driving logic.

[0054] For example, in a certain vehicle camera module, for a sensor with the model IMX390, based on the "image stream start" instruction of the standard instruction set, the register access sequence of this model is extracted as 0x3000→0x01, the delay parameter is 15ms, the status register is 0x3010, and the status code 0xA5 indicates successful start. Using Lua language modeling, a register access function reg_write(addr,value) is defined. In the script, reg_write(0x3000,0x01), insert_delay(15), and reg_read(0x3010) are called sequentially, and the read value is passed to status_map to obtain the standard status "flow_active". The communication timing verification algorithm verifies that the delay meets the hardware specifications and the register access order is consistent with the original factory requirements. The status feedback encapsulation method maps 0xA5 to a feedback value of 1 that can be recognized by the cockpit software. In terms of execution performance, the script is directly called by the lightweight driver middleware interpretation engine after loading, which significantly shortens the startup time and reduces memory usage, ensuring rapid adaptation of multi-vendor sensors on the same hardware platform.

[0055] S1.4: The executable script file is compressed into text and bound to a unique identifier corresponding to the sensor model. The compressed text is then stored in the non-volatile storage unit of the camera module to reduce drive storage overhead and improve system resource utilization.

[0056] In the non-volatile storage unit of the vehicle camera module, the input conditions are the executable script files corresponding to the sensor models of each manufacturer and their unique identifiers. The file format is lightweight scripting language text and contains a detailed description of register access sequences, timing control logic and status feedback mechanisms.

[0057] A lossless compression algorithm (parameters: compression dictionary size = 4KB, matching window size = 32KB) is used to compress the executable script file, thereby enabling the detection and replacement of redundant patterns in the script text structure to reduce storage space.

[0058] Furthermore, by using a hash-based index generation method (parameter: hash function type = SHA-256), the compressed script can be quickly indexed and located, and the script mapping code can be obtained as input data for subsequent binding operations.

[0059] Furthermore, by using a binding association algorithm (parameter: binding key = unique device identifier UID), a bidirectional logical association is achieved between the compressed script file and the corresponding Sensor model UID, and a storage metadata structure is generated, which includes the UID field, the compressed script offset address, the script length, and the hash check value.

[0060] Furthermore, by using a partitioned write strategy (parameters: partition size = 64KB, write alignment = 4KB), the bound compressed script file and its metadata are written to the driver script partition area of ​​the non-volatile storage unit, ensuring the addressing efficiency and data consistency of the read operation.

[0061] By using the compression and binding methods described above, the script file from the previous step is transformed into high-density storage data blocks, achieving the expected technical effects of reducing storage overhead and improving resource utilization.

[0062] For example, in an embedded NAND flash memory (capacity = 128MB) automotive camera module, a script file (original size = 32KB) for a certain sensor model was compressed using the LZ77 lossless compression algorithm. With a matching window size of 32KB and a minimum matching length of 3 bytes, the file size was reduced to 9KB after compression. The hash value of the compressed file was calculated using the SHA-256 hash function, resulting in a 64-character hexadecimal checksum. H is used for subsequent integrity verification. The compressed file is associated with UID=0xA13F29BC using a UID binding algorithm. The generated metadata structure contains the following fields: UID=0xA13F29BC, compression offset address=0x002000, file length=9216 bytes, and hash=H. The final data block is written to the driver script partition of the non-volatile storage unit, aligned to 4KB at the beginning address. During system initialization, index location and loading verification can be completed within ≤2ms. Actual results show that this processing scheme significantly improves storage space utilization without affecting script parsing speed.

[0063] S1.5: Perform a digital signature authentication mechanism on the script file, and verify the integrity and legality of the script content based on the public key encryption algorithm to ensure that the script is not tampered with during loading and execution, and to ensure the security and stability of the vehicle system operation.

[0064] After the Lightweight Driver Middleware Layer (LDML) is built and executable script files corresponding to each vendor's sensor are generated, a digital signature authentication mechanism (parameters: RSA-2048 public key encryption algorithm, SHA-256 digest algorithm) is used for each script file to realize the integrity and legality verification of the script content.

[0065] Furthermore, by using the digest calculation method (parameter: SHA-256), byte-by-byte operations are performed on the compressed text format script file to obtain a fixed-length 256-bit digest value, which is used to uniquely identify the hash feature of the original script content.

[0066] Furthermore, the signature data provided by the script publisher is decrypted using the public key encryption authentication method (parameter: RSA-2048), and the decryption result is generated and compared bit by bit with the digest value obtained in the previous step.

[0067] Furthermore, a consistency comparison algorithm (parameter: bit-by-bit logical equality judgment) is used to match and verify the digest value with the signature decryption result, and a legality verification index is generated to confirm that the script file content has not been tampered with and that the source complies with the system security policy.

[0068] By processing the authentication results, the legality verification indicators of the previous step are transformed into script loading authorization flags, thereby achieving the expected technical effect of allowing only legal and complete scripts to enter the LDML runtime environment.

[0069] For example, in the non-volatile storage unit of the vehicle camera module, a script file of size 48KB for the sensor of model A is stored. The SHA-256 algorithm is used to perform a digest operation on the file to obtain a digest value. : in This is the binary content of the script file. The digest value is then decrypted and verified using an RSA public key with a modulus length of 2048 bits. The resulting signature digest value is... Application of comparison algorithms The system determines the result to be valid and sets the authorization flag to [value]. This allows the script file to enter the LDML cache warm-up process. For Sensor script files of model B, if the signature decryption result does not match the digest value, the authorization flag is set to 0. This blocks the loading process, effectively improving the security and stability of system operation.

[0070] Step S2: When the vehicle-mounted camera module is powered on and initialized, the connected sensor model is identified through the I2C or SerDes communication interface, and its unique identifier is obtained by querying the local device mapping table. Specifically, this includes: S2.1: Perform a device ID read operation on the connected sensor chip based on the I2C communication interface to obtain its manufacturer identification and model information.

[0071] S2.2: Using predefined register addresses and data format specifications, the read device ID is parsed to extract the standardized Sensor model identifier field.

[0072] S2.3: Based on the extracted Sensor model identifier field, query the local preset device mapping table to match the corresponding unique device identifier (Device UID).

[0073] In the device mapping table management module of the lightweight driver middleware layer, the standardized Sensor model identifier field obtained by parsing in step S2.2 is loaded as the query key value input.

[0074] A hash index lookup algorithm (parameters: map table hash function seed value, collision handling chain length threshold) is used to achieve fast retrieval of the local preset device map table and output the map table record handle that matches the input model identifier field.

[0075] Furthermore, by using a chained comparison method (parameters: model identifier field length, character matching tolerance), a strict match is achieved between the model identifier field and the manufacturer code and product code in the mapping table records, and matching score data is obtained.

[0076] Furthermore, the maximum matching degree filtering algorithm (parameters: matching degree weight coefficient, non-matching discard threshold) is used to select the unique device identifier Device UID and generate uniqueness verification result data.

[0077] By binding the device UID cache, the matching results from the previous step are stored in the runtime cache, and an internal reference relationship between the UID and the script index path is established, which enables the subsequent script to be quickly located and loaded.

[0078] For example, in a vehicle camera module, the input standardized sensor model identifier field is "OV1234-R1". The device mapping table has 30 model records from 10 manufacturers pre-set. The hash index lookup algorithm is set with a seed value of 5381 and a collision list length threshold of 4. The retrieval process generates hash slot number 17, and the hit list contains 3 records. The chain comparison method is set with a field length of 9 characters and a character matching tolerance of 0. It matches the manufacturer code "OV" and product code "1234" completely, with a matching score of 100. The maximum matching degree filtering algorithm is set with a weight coefficient of 1 and a discard threshold of 95, filtering out the unique Device UID "UID-OV1234R1". The UID is bound to the script index path " / scripts / uid-ov1234r1.lua" and cached in the runtime mapping table, which significantly improves the script index location time from an average of 2.3ms to 0.5ms, thereby improving the driver loading response speed and system resource utilization during the power-on initialization phase.

[0079] S2.4: Based on the matched Device UID, extract the script configuration file index information associated with the Sensor model from the device mapping table to support the subsequent script loading process.

[0080] S2.5: Perform integrity verification on the extracted script configuration file index information to ensure that its format conforms to the preset standard and to provide valid input basis for subsequent script loading and interpretation execution.

[0081] like Figure 2 As shown, step S3 involves loading the corresponding script configuration file from the storage unit based on the identified Sensor model identifier, and then performing syntax validation and cache preheating using the lightweight interpretation engine built into the lightweight driver middleware layer. Specifically, this includes: S3.1: Based on the identified Sensor model identifier, locate and retrieve the corresponding script configuration file from the storage unit. The script configuration file is stored in a compressed text format to reduce storage resource consumption.

[0082] S3.2: Perform integrity verification on the obtained script configuration file, and perform consistency verification on the file content based on the hash verification algorithm to ensure that the script has not been tampered with or damaged.

[0083] S3.3: The lightweight interpretation engine built into the lightweight driver middleware layer performs syntax parsing on the validated script configuration file, extracts the standard instructions and register operation sequences defined therein, and generates an abstract syntax tree in the intermediate representation form.

[0084] Based on the compressed text format script file that has passed integrity verification, the syntax analysis method of the lightweight script interpretation engine (parameters: standard instruction set definition rules, register access syntax mode, timing control definition format) is used to achieve preliminary word segmentation and tokenization processing of script text into syntax units.

[0085] Furthermore, a recursive descent parsing algorithm (parameters: standard instruction set keyword set, register operator priority table) is used to map script syntax units to syntax structure nodes, and an initial syntax tree data structure containing standard instruction nodes and register access nodes is obtained.

[0086] Furthermore, the validity of syntax tree nodes is checked through a semantic constraint verification method (parameters: instruction semantic mapping table, register parameter type definition set), and instruction-register operation sequences that can be used for subsequent cache construction are generated.

[0087] Furthermore, through an abstract syntax tree generation algorithm (parameters: node type, inter-node dependencies, instruction sequence index), an abstract syntax tree is generated that combines parsed standard instructions and register operation sequences into an intermediate representation, and the output tree structure is stored in a memory buffer.

[0088] Through the above-mentioned syntax parsing and abstract structure generation processing methods, the script text obtained in the previous step is transformed into highly structured intermediate representation data, realizing the expected technical effect of standardized expression of script logic and subsequent efficient execution.

[0089] For example, in a test scenario for an automotive camera module, the script file for a certain sensor model contains three standard instructions: initialization, register configuration, and image stream initiation. Each instruction corresponds to 2-5 register write operations and 1-2 status polling operations. When using a lightweight script interpretation engine for tokenization, 27 syntax units were identified, including 3 standard instruction identifiers, 15 register address markers, and 9 operator markers. The recursive descent parsing process grouped the syntax units into instruction nodes and register nodes according to the instruction priority table, establishing a parent-child hierarchical relationship. During semantic verification, all parameter types were detected to match the instruction requirements based on the register parameter type definition set, and no type conflicts occurred. In the abstract syntax tree generation stage, the 3 standard instruction nodes and their subordinate register operation nodes were organized into a tree structure according to the execution order and numbered, forming an intermediate representation object stored in the parsing buffer. In the subsequent cache warm-up stage, this abstract syntax tree was used to select high-frequency instruction paths for bytecode compilation, significantly improving the response speed and execution efficiency of driver calls.

[0090] S3.4: Construct a script logic cache table based on the abstract syntax tree, and pre-compile frequently called script fragments to generate directly executable bytecode cache to improve subsequent execution efficiency.

[0091] Based on the generated abstract syntax tree structure data, a cache table construction algorithm (parameters: syntax tree node identifier, node call frequency, memory allocation strategy) is used to implement node-level index organization of script logic to support subsequent fast location and execution calls.

[0092] Furthermore, by using a high-frequency segment analysis method (parameters: script call log, node call timestamp, threshold setting based on runtime statistics), the system identifies frequently occurring register access sequences and timing control logic in the syntax tree and obtains high-frequency node set data.

[0093] Furthermore, by using a lightweight bytecode compiler (parameters: high-frequency node set, hardware platform instruction set characteristics, register access security policy), the identified high-frequency script fragments are compiled into bytecode format compatible with the current platform, and directly executable instruction sequence data is generated.

[0094] Furthermore, a bytecode optimization algorithm (parameters: instruction merging rules, jump path optimization strategy, and delayed instruction compression parameters) is adopted to perform path compression and register operation merging on the compiled bytecode, thereby reducing the execution cycle and lowering memory access latency.

[0095] By establishing a cache mapping method (parameters: unique identifier of compiled bytecode, sensor model identifier, cache index), the compilation result of the previous step is transformed into a cache data structure that can be quickly looked up at runtime, achieving the expected technical effect of significantly reducing the response latency of control instructions.

[0096] For example, in a certain model of vehicle-mounted camera module, the script syntax tree contains 200 instruction nodes, among which the register initialization and parameter loading nodes are called 300 times per second during the system startup phase. The system sets a high-frequency threshold of 200 times per second, and the frequency analysis algorithm obtains a set of 42 high-frequency nodes, including register initialization sequences and image stream startup sequences. This set is input into a lightweight bytecode compiler, and combined with the I2C bus access latency characteristics of the hardware platform, a compatible instruction sequence is generated. For the register initialization sequence, continuous write operations are optimized and merged to reduce the number of I2C start signals, thus reducing the total instruction execution cycle from... Seconds down Seconds. After being bound to the corresponding Sensor model identifier through the cache mapping table, runtime loading requests can directly match the bytecode cache without repeated interpretation. The total loading time of the Sensor driver during system startup is significantly shortened, meeting the real-time requirements for rapid adaptation.

[0097] S3.5: Bind the generated bytecode cache to the corresponding Sensor model identifier and load it into the runtime execution context of the lightweight driver middleware layer to complete cache warm-up and provide an execution basis for the rapid response of subsequent control instructions.

[0098] like Figure 3 As shown, step S4 involves converting the control requests issued by the cockpit software into predefined standard instruction codes, and then having the lightweight driver middleware layer locate the corresponding script logic based on the current sensor type. Specifically, this includes: S4.1: Perform syntax parsing on the original control requests issued by the cockpit software to extract key control semantic information such as control type, target parameters, and operating mode.

[0099] The original control request input from the cockpit software is processed by an instruction parser module (parameters: control request data stream, predefined control syntax rules) to achieve structured parsing of the control request.

[0100] Furthermore, by controlling the semantic extraction algorithm (parameters: keyword matching rules, operation mode classification table), control type information is extracted from the parsing results, and operation type field data is obtained.

[0101] Furthermore, through the parameter decoding algorithm (parameters: parameter mapping table, unit conversion standard), the target parameters in the control request are numerically quantified and standardized in units, and a parameter vector with a unified format is generated.

[0102] Furthermore, by using a pattern recognition method (parameters: operation pattern identifier set, pattern decision tree model), the operation pattern of the control request is automatically determined, and an operation pattern index value is generated.

[0103] By using a chain of control request syntax parsing and semantic recognition processing, the cockpit software request loaded in the previous step is transformed into structured semantic data containing control type, target parameters, and operating mode, thus providing effective input support for subsequent standard instruction code mapping.

[0104] For example, the raw control request data stream sent by the cockpit software is parsed using control syntax rules defined by BNF (Backus-NaurForm). The parser parameters are set to support operation keywords such as I2C register read / write and image stream start / stop. The input control request is {"cmd":"start_stream","fps":"30","exposure":"auto"}. The control semantic extraction algorithm extracts the control type as image stream start based on the mode category corresponding to "start_stream" in the operation mode classification table. The parameter decoding algorithm parses the "fps" field into a numerical value. The unit is kept in frames per second according to the unit conversion standard; the "exposure" field is parsed into an automatic mode index value. Pattern recognition methods locate the corresponding pattern identifier in the image stream within the pattern decision tree. Generate operation mode index value The execution result is: Control type field="Image stream start", target parameter vector=[ , ], Operation mode index value = This enables precise mapping of control requests to structured semantic data, significantly improving the accuracy and execution efficiency of subsequent standard instruction code conversion.

[0105] S4.2: Based on the semantic mapping rules defined by the standard instruction set, the parsed key control semantic information is converted into corresponding standard instruction codes to form a unified format of intermediate layer control instructions.

[0106] The semantic mapping rules are based on the standard instruction set definition. The input conditions are the three types of key control semantic information obtained by S4.1 parsing: control type, target parameters and operation mode. The execution object is the semantic mapping engine and standard instruction set built into the Lightweight Driver Middleware Layer (LDML).

[0107] A semantic parsing mapping method (parameters: standard instruction set semantic table, control type, target parameter, operation mode) is employed to match the control type to the corresponding functional domain of the standard instruction set, and the functional domain code is used as the initial instruction code. Furthermore, a parameter binding algorithm (parameters: target parameter value, standard instruction parameter template) is used to combine the target parameter with the functional domain code to obtain an intermediate instruction code sequence with a specific operational intent.

[0108] Furthermore, by using the operation mode mapping method (parameters: operation mode label, mode priority table), the operation mode label is converted into a mode code, and the mode code is concatenated bit-level with the intermediate instruction encoding sequence to generate a complete standard instruction code main structure.

[0109] Furthermore, an instruction verification and reassembly algorithm (parameters: standard instruction set length constraint, verification polynomial) is adopted to perform length alignment and integrity verification on the main body of the standard instruction code. If the verification fails, the error flag generation module is triggered to generate an exception code and place it at the end of the instruction. Otherwise, the padding bits are padded according to the standardized structure to form the final unified format of the intermediate layer control instruction.

[0110] By using semantic mapping, the parsing results from the previous step are transformed into standard instruction codes that are highly compatible and have a consistent format. This decouples and unifies the driver call logic with the cockpit software control requests, providing reliable input for the accurate location and rapid execution of subsequent script logic.

[0111] S4.3: Based on the currently identified Sensor model identifier, call the corresponding script routing table from the lightweight driver middleware layer to locate the script logic execution path that matches the current Sensor type.

[0112] S4.4: The converted standard instruction code is passed as an input parameter to the corresponding script logic execution engine to trigger the script parser to extract the register operation sequence corresponding to the instruction in the script configuration file.

[0113] For the instruction codes mapped by the standard instruction set, the script logic scheduling method built into the lightweight driver middleware layer is used (parameters: current sensor model identifier, standard instruction code, script route index) to realize the execution engine location function of the script associated with the target sensor.

[0114] Furthermore, by using the entry parameter binding algorithm of the script logic execution engine (parameters: standard instruction code, set of input control parameters), the precise matching between the input parameters and the formal parameters and register operation templates defined in the script is achieved, and binding context data that can be used for subsequent register sequence parsing is obtained.

[0115] Furthermore, the instruction sequence extraction method of the script parser (parameters: binding context data, script configuration file path) is used to traverse the abstract syntax tree of the script text and extract the set of register access and timing control statements associated with the current standard instruction code from the corresponding node.

[0116] Furthermore, by using a register sequence optimization filtering algorithm (parameters: register access set, execution security policy table), invalid or duplicate register operations are removed, generating an optimized register access sequence that conforms to the security sandbox mechanism constraints.

[0117] Through the above parsing and filtering process, the standard instruction code from the previous step is converted into underlying register access sequence data, thereby achieving the expected technical effect of dynamic and differentiated operation calls for sensors from different manufacturers.

[0118] For example, in the vehicle-mounted camera module, when the cockpit software issues an image stream start request, the standard instruction set mapping module outputs the instruction code ID as follows: The script routing index points to the Sensor script file for the current IMX490 model. The script logic execution engine receives input parameters, including resolution. x Frame rate fps, and in the binding algorithm, match them respectively to the formal parameters defined in the script. , and Tags. The script parser traverses the abstract syntax tree nodes to find the tags that match the instruction codes. The associated register operation sequence includes starting the master clock register. Write value Configure the row length register Write 1. Set the frame rate register Write and start the video stream register Write Operation statements. Register optimization filtering algorithm removes duplicate writes. The redundant instructions are used to generate the final access sequence and pass it to the execution module. Verification results show that the IMX490 achieves approximately... The stream starts within milliseconds, and the video parameters are configured accurately, achieving the expected fast adaptation and low resource consumption.

[0119] S4.5: Based on the register operation sequence extracted by the script logic execution engine, generate an executable low-level control command stream for subsequent execution modules to perform register access and timing control operations.

[0120] Based on the register operation sequence extracted by the script logic execution engine, an instruction stream generation algorithm (parameters: register address sequence, data word, operation type, timing constraints) is used to map abstract register access actions into executable low-level hardware control instruction units.

[0121] Furthermore, by using timing optimization methods (parameters: operation dependency matrix, maximum allowable delay, communication bus speed), the execution order of instruction units is rearranged to obtain an instruction stream list that satisfies the timing constraints of the sensor chip and optimizes communication latency.

[0122] Furthermore, by using a bus protocol encapsulation algorithm (parameters: communication interface type I2C or SerDes, address width, data validity verification rules), the single register operation in the instruction stream list is encoded into a frame structure that conforms to the current hardware platform bus protocol format, and corresponding control fields such as start bit, stop bit, and parity bit are generated.

[0123] Furthermore, by using a redundant instruction merging algorithm (parameters: register address repetition rate threshold, merging window size), the operation of continuously accessing adjacent or repeated registers in the instruction stream is batch encapsulated, and a compressed command stream is generated to reduce bus occupancy time.

[0124] By using a command flow integrity verification method (parameters: hash check code length, field mapping rules), the result of the previous step is transformed into a verifiable underlying control command dataset, thereby achieving high reliability and executability of register access and timing control operations.

[0125] For example, in the vehicle camera module, when the cockpit software issues a "start image stream" control request, the register operation sequence extracted by the script logic execution engine is: start register address Write data Delay microseconds, status register address Read the data verification result. The instruction stream generation algorithm constructs two instruction units based on this sequence: the write instruction. Read instructions Timing optimization methods, combined with delay constraints, insert delays between write and read instructions. Microsecond delay control character. The bus protocol encapsulation algorithm selects I2C mode, packaging each instruction unit into a form containing the start condition. The complete frame includes command fields, data fields, and stop condition markers. A redundant instruction merging algorithm determines if there are no duplicate register accesses and preserves the original sequence. An integrity verification method calculates the hash value of the command stream. After successful verification, the command stream is output to the underlying execution module. In this embodiment, the command stream received by the underlying module can stably drive the sensor to start the image stream during actual operation, significantly reducing system response latency, and no command execution anomalies occurred during 100,000 consecutive calls.

[0126] Step S5: Based on script logic, perform low-level register access and timing control operations to complete sensor initialization, parameter configuration, image flow control, and other operations, and feed back the execution status to the cockpit software. Specifically, this includes: S5.1: Based on the Lightweight Driver Middleware Layer (LDML), standard instruction code is parsed and combined with the current sensor model identifier to obtain the corresponding script logic segment, so as to determine the register address sequence and operation timing required by the sensor model when executing the current instruction.

[0127] Using the standard instruction code parsed from the Lightweight Driver Middleware Layer (LDML) as input parameters, and combined with the currently identified Sensor model identifier, the scope of the underlying script logic call and the execution target are determined.

[0128] The script routing retrieval algorithm (parameters: Sensor model identifier, standard instruction code) is used to locate the internal script mapping index of LDML and return the reference path of the script logic fragment corresponding to the Sensor model.

[0129] Furthermore, the script metadata is parsed using a script logic parsing method (parameters: reference path, script version number), and the register access sequence definition and timing parameter set involved in the current instruction are extracted from the script file to obtain the register address list and related operation timing.

[0130] Furthermore, by using a register sequence verification method (parameters: register address list, operation timing), the legality of register access and the consistency of timing constraints are verified, and a structured register access plan table is generated to ensure that the access instruction matches the target sensor hardware protocol.

[0131] By using a register access plan table mapping generation method, the verified register sequence and timing data are transformed into a low-level executable access mapping table, providing accurate input data for the subsequent generation of hardware access instruction sets, and realizing the controllability and security of register access operations.

[0132] For example, in an automotive camera module equipped with an OmniVision OV10640 sensor, the input conditions are an image stream start command represented by standard instruction code and the sensor model identifier "OV10640". The LDML routing retrieval algorithm locates the corresponding script fragment path " / scripts / ov10640 / start_stream.luac", and the script parsing method reads its contents, extracting the register sequence [0x301A, 0x3028, 0x302A] required to start the image stream and the corresponding timing parameters [delay 10ms, delay 5ms, status polling 3 times]. The register sequence verification method verifies that these addresses are all within the range approved in the OV10640 technical specifications and that the timing meets the startup protocol requirements, generating the following access plan table: Step 1—Access register 0x301A, write the value 0x0001, and delay for 10ms; Step 2—Access register 0x3028, write the value 0x7000, and delay for 5ms; Step 3—Access register 0x302A, write the value 0x0005, and poll the status register 0x3000 three times, checking if Bit 0 is set. The mapping generation method converts this plan table into an execution mapping table, binds it to the current instruction code, and implements controllable calls to the startup logic of this sensor model, ensuring that register read / write and timing control operations can be accurately executed in subsequent low-level hardware accesses, achieving reliable image stream startup.

[0133] S5.2: Perform syntax parsing and parameter binding on the register access sequence defined in the script logic segment to generate an executable set of underlying hardware access instructions to support precise read and write operations on the internal registers of the sensor chip.

[0134] After obtaining the script logic fragment that matches the current sensor model identifier and the register access sequence defined within it, the register access sequence needs to be parsed and parameter bound line by line by the parsing module of the lightweight driver middleware layer to form an access instruction set that can be directly executed by the underlying hardware.

[0135] An abstract syntax tree traversal algorithm (parameters: node type, access order) is used to perform structured parsing of register operation nodes in script logic segments, thereby separating and standardizing operation elements such as register address, written data, and read / write type.

[0136] Furthermore, through the register parameter binding mechanism (parameters: register address mapping table, cockpit control parameter set), the actual parameters of variable placeholders in the script logic are replaced, and register addresses and their corresponding operation data values ​​consistent with the current hardware platform are generated.

[0137] Furthermore, an operation sequence optimization algorithm (parameters: operation dependency matrix, timing constraint table) is adopted to sort the register access sequences with bound parameters and merge redundant accesses, ensuring that the execution order of register read and write in the instruction set meets the timing logic defined in the script and reducing the overhead of repetitive instructions.

[0138] Furthermore, the optimized register access sequence is converted into a binary hardware access instruction word by the instruction set encoder (parameters: instruction word format specification, opcode mapping table), and a combination structure of corresponding opcode, address code and data code is generated to ensure that it can be executed directly in the I2C or SerDes driver layer.

[0139] Through the above parsing, binding, optimization, and encoding processes, the script logic register access sequence from the previous step is transformed into an executable set of underlying hardware access instructions, enabling precise read and write control of the sensor chip's internal registers.

[0140] For example, during the initialization process of a certain sensor model, its script logic defines a continuous register write sequence, where address 0x3000 needs to be bound to the exposure time parameter passed in by the cockpit software. Address 0x3010 needs to be bound to a gain factor. After traversing the abstract syntax tree, the parsing module replaces the placeholders with exposure time values. With gain coefficient This generates two hardware access operations: writing data 0x05DC to register 0x3000 and writing data 0x0004 to register 0x3010. The operation sequence optimization algorithm, combined with the register dependency matrix, eliminates redundant delayed instructions and sorts them according to timing constraints, ensuring that the exposure parameters are written before the gain parameters. The instruction set encoder encodes the two instructions into binary form according to the instruction word format specification, where the opcode of the first instruction is... The address code is The data code is The opcode for the second instruction is The address code is The data code is The underlying instruction set is directly sent to the sensor chip from the I2C interface driver layer. Verification results show that the register writing order is correct and the data values ​​are accurate during the initialization process. The image signal output is stable and meets the expected exposure and gain settings.

[0141] S5.3: Based on the generated underlying hardware access instruction set, perform register write or read operations on the sensor chip through the I2C or SerDes communication interface, and control the operation timing to meet the timing constraints of the sensor chip according to the delay parameters and status polling mechanism defined in the script.

[0142] With the loaded and generated underlying hardware access instruction set as input, the I²C communication interface control logic module (parameters: I²C bus address, data bit width, ACK signal rules) sends register write commands defined in the instruction set one by one to realize the configuration operation of specific registers inside the sensor chip.

[0143] Furthermore, through the SerDes communication interface access control algorithm (parameters: serial link rate, data format mapping table, link initialization timing), the register read commands in the underlying hardware access instruction set are serialized and transmitted to obtain the real-time status data of the Sensor registers, and then returned to the middleware logic parser.

[0144] Furthermore, a delay control scheduling algorithm is adopted (parameter: delay period). (Control queue priority), bind the delay parameters defined in the script logic segment to the current communication interface execution status, realize the precise matching of command sending interval and hardware response time, and obtain the adjusted command execution time schedule data.

[0145] Furthermore, based on the state polling mechanism (parameter: polling period) The system continuously accesses the target status register of the sensor chip (including the status register address set and the end judgment condition), extracts the status code change sequence, and generates a status completion signal flag bit.

[0146] By using a collaborative processing method combining delay control scheduling algorithm and state polling mechanism, the underlying hardware access instruction set of the previous step is transformed into execution flow data that conforms to the timing constraints of the sensor chip, thereby achieving the expected technical effects of register access stability and timing matching.

[0147] For example, during the initialization process of a certain type of automotive CMOS sensor, the underlying hardware access instruction set includes continuous write operations to registers 0x3000 to 0x3005, the I²C communication interface bus rate is set to 400kHz, the data width is 8 bits, and the ACK signal rule is acknowledgment per byte. The delay control scheduling algorithm uses the delay period defined in the script. Set as A fixed delay is inserted after each register write to wait for the sensor's internal configuration to complete; the polling cycle of the status polling mechanism. Set as Poll register 0x3006 until the status code changes. Become This indicates that initialization is complete. In testing and verification, this method ensured no communication errors throughout the register configuration process, and all register accesses met the data preparation and response time requirements for this sensor model. System startup efficiency was significantly improved, and response latency was stabilized within [a certain range]. to This effectively improves the real-time performance and reliability of the drive execution.

[0148] S5.4: After completing register access and timing control, the response status code and feedback data of the sensor chip are collected and encapsulated into standardized status feedback information through a lightweight driver middleware layer so that the cockpit software can identify the current working status of the sensor.

[0149] After completing the writing and reading of the underlying registers and the corresponding delay control, a status code acquisition method (parameters: I2C response register address, SerDes link status register address) is used to read the status byte of the current execution result of the sensor chip. A CRC check algorithm (parameters: polynomial generator, initial seed value) is used to verify the transmission integrity of the acquired status code and obtain verifiable raw status code data.

[0150] Furthermore, a multimodal data fusion method (parameters: status code data, sensor feedback data frame header, image stream frame synchronization signal) is used to align the timestamps of multi-source feedback data and obtain a synchronized integrated feedback data structure. Further, a state parsing algorithm (parameters: standard status code mapping table, anomaly code determination rules) is used to achieve unified semantic conversion of status codes from different manufacturers' sensors and generate a standardized set of status fields.

[0151] Furthermore, by using statistical feature extraction methods (parameters: working mode field, error count field, temperature feedback field), key performance indicators of standardized status feedback information are extracted, and a performance dataset that can be used for cockpit software analysis is generated.

[0152] By encapsulating and converting the results of the previous step into a status feedback data packet that conforms to a standardized communication protocol format, the cockpit software can achieve the expected technical effect of recognizing the current sensor working status and triggering corresponding control logic across vendors.

[0153] For example, within the vehicle-mounted camera module, the I2C response register address is set to... The SerDes link status register address is set to The status code returned by the sensor during a single access cycle is... Using the CRC checksum algorithm, the generator polynomial is: The initial seed value is The calculated checksum matches the transmitted value, confirming the status code's validity. The status code is then aligned with the simultaneously acquired frame synchronization signal to obtain a result including the timestamp. The comprehensive feedback data structure. During the state resolution phase, based on the pre-stored standard state code mapping table, the... The translation is "Image stream normal, temperature slightly high," and the key performance indicators are extracted as follows: operating mode is continuous acquisition, error count is... Second, temperature feedback is Temperature in degrees Celsius. The data is ultimately encapsulated into a data packet conforming to a standardized communication protocol format and successfully parsed and displayed by the cockpit software, enabling cross-vendor status monitoring and safety warning functions in real-time high-temperature environments.

[0154] S5.5: The encapsulated standardized status feedback information is transmitted to the cockpit software through a preset standardized communication protocol, and the operation log is recorded to the local storage unit to support subsequent fault diagnosis and system debugging.

[0155] Step S6: Dynamically detect the script execution frequency at runtime, compile frequently used script segments into bytecode cache to avoid repeated parsing, thereby improving system response performance. Specifically, this includes: S6.1: Real-time monitoring of script call frequency during the operation of Lightweight Driver Middleware Layer (LDML), collecting execution count data of each script segment based on a preset sampling period to obtain a candidate set of high-frequency execution scripts.

[0156] S6.2: Based on the collected script call frequency data, a sliding window algorithm is used to calculate the script execution heat in a weighted manner, and a script heat evaluation index is generated to identify high-frequency script segments that need to be compiled and cached first.

[0157] S6.3: Based on the script popularity evaluation index, select script segments whose popularity value exceeds the preset threshold, and perform just-in-time compilation processing on the selected script segments based on the lightweight bytecode compiler to generate a compact bytecode instruction sequence compatible with the current hardware platform.

[0158] S6.4: Cache the generated bytecode instruction sequence to a preset high-speed cache area and establish a bytecode cache index table to record the mapping relationship between the original script text and the corresponding bytecode, so as to realize fast lookup and execution switching when the script is called.

[0159] Within the Lightweight Driver Middleware Layer (LDML), a compact sequence of bytecode instructions generated by the bytecode compiler is received as the input data object.

[0160] A high-speed cache management algorithm (parameters: cache block size 64KB, access latency threshold 10ns) is adopted to allocate the physical storage location of the bytecode instruction sequence and write it into the preset high-speed cache area to ensure low-latency reading during high-frequency calls.

[0161] Furthermore, a bidirectional mapping construction method (parameters: hash function type is FNV-1a, collision handling method is chain hash) is used to implement a bidirectional index structure between the original script text and the corresponding bytecode, and the correspondence between the text hash value and the bytecode cache address is recorded in the index table so as to quickly locate the target when the call path is matched.

[0162] Furthermore, memory alignment optimization is adopted (parameter: alignment boundary is 8 bytes) to achieve boundary alignment of bytecode instruction sequences within the cache area, reduce the time spent reading cross-page memory access, and improve the pipeline efficiency of the LDML execution engine when calling bytecode.

[0163] Furthermore, by checking the consistency check code (parameter: CRC32) of the index table, error detection is achieved during the index table loading process, and the cache reconstruction process is automatically triggered based on the detection results to prevent script call failures caused by index mapping errors.

[0164] By using the above-described caching and indexing methods, the bytecode sequence generated in the previous step is transformed into a cache data structure that can be quickly retrieved and executed, achieving millisecond-level response performance when the script is called.

[0165] For example, in the LDML runtime environment of an in-vehicle camera module, the cache size is preset to 256KB, divided into four 64KB blocks. Each block uses an LRU linked list to maintain its hotness value. The hash value is obtained by performing an FNV-1a hash on the hot script segment "Sensor_Init". Mapped to the starting address of the second block of the cache. It is stored aligned to 8-byte boundaries. When building the bidirectional index table, the hash value of the original script and the bytecode cache address are used. Simultaneously register and record the bytecode length. Bytes. A CRC32 check is performed during the index table loading process to obtain the checksum. The verification code matches the pre-stored checksum, confirming the mapping relationship is valid. During runtime testing, the average response time of this script segment remained consistent across 30 consecutive calls. Within milliseconds, loading time is reduced by about two-thirds compared to the uncached state, significantly improving system real-time performance and resource utilization efficiency.

[0166] S6.5: During subsequent script calls, the system determines whether a valid bytecode cache exists for the current script based on the bytecode cache index table. If it does, the bytecode is executed directly to reduce the parsing overhead of the script interpretation engine and improve system response performance.

[0167] S6.6: Based on the feedback information of bytecode execution, dynamically adjust the bytecode cache eviction policy and use the LRU (Least Recently Used) algorithm to clean up bytecode that is accessed infrequently, so as to maintain the efficient utilization of cache resources.

[0168] Step S7: By using the runtime sandbox mechanism of the lightweight driver middleware layer, direct access permissions of scripts to hardware resources are restricted, ensuring the security of the script execution process. Specifically, this includes: S7.1: A runtime sandbox environment is built based on the Lightweight Driver Middleware Layer (LDML) to isolate the loaded script configuration files and prevent scripts from directly accessing the system's core resources.

[0169] Based on the runtime initialization results of the lightweight driver middleware layer, the script configuration file, which has been loaded by the script interpretation engine and has passed integrity and syntax checks, is used as the input object for isolated processing.

[0170] A virtualization isolation method (parameters: memory page mapping table, virtual address space size) is used to ensure that the script can only access data structures within the allocated virtual address space during execution, thus blocking its direct mapping relationship with physical memory.

[0171] Furthermore, by using a process-level isolation method (parameters: independent thread context, system call interception threshold), each script execution thread runs in an independent execution context, and unauthorized kernel interface access is blocked through the system call interception mechanism to obtain an isolated security context identifier.

[0172] Furthermore, by using a file system virtual mounting method (parameters: mount path mapping table, access permission bitmask), the script can only access the virtual file system pre-mounted in the runtime sandbox environment, blocking its ability to read or write the system core configuration file, and generating a file access whitelist index.

[0173] Furthermore, by using hardware interface virtualization methods (parameters: I2C bus simulation layer, GPIO virtual driver handle pool), the script's access requests to the hardware interface are redirected to the virtual driver layer, so that interface call tests can be completed without directly contacting the physical device, and hardware access audit logs can be recorded.

[0174] Through the isolation mechanism described above, the script loaded in the previous step is transformed into a security context of a controlled execution environment at runtime, achieving complete isolation between the script and the system's core resources, and significantly improving the security and controllability of the script execution process.

[0175] For example, in a certain vehicle-mounted camera module, the virtual address space configured in the runtime sandbox environment is 256MB in size. The memory page mapping table is divided into 4KB pages, with a total of 65,536 page table entries, each mapped to an isolated segment of physical memory. Process-level isolated thread contexts use independent stack space (512KB in size) and thread priority 3. The system call interception threshold is set to trigger an alarm after 30 unauthorized calls per second. The file system virtual mount path mapping table contains two directories: / virtual / config and / virtual / log, with access permission bitmasks of read-only and append-only, respectively. In the hardware interface virtualization layer, the I2C bus simulation layer provides 64 register address spaces for testing, and the GPIO virtual driver handle pool has a capacity of 128 handles, randomly allocated to virtual pins called by scripts. In this configuration, during the execution of a loaded sensor script from a certain vendor, access to I2C address 0x1A was captured by the virtual layer and redirected to the simulation layer. The audit log recorded the access timing, data value, and call source. The system's core I2C bus was not actually accessed. The verification results showed that the script could execute logic normally in the isolated state, but it could not break through the sandbox to access the physical hardware, thus significantly improving security.

[0176] S7.2: Initializes permissions for the script execution context that enters the runtime sandbox, and restricts its access to hardware resources such as underlying registers, GPIO, and I2C bus based on preset security policies.

[0177] Within the runtime sandbox mechanism, the permissions of the incoming script execution context are initialized and configured. Input data includes the sensor model identifier bound to the script, the syntax tree structure of the script configuration file, and the set of runtime environment variables. An access control list initialization method (parameters: device UID, resource category, permission bitmask) is used to allocate access capabilities for underlying registers, GPIO ports, and I2C bus signal channels.

[0178] Furthermore, a resource classification mapping algorithm (parameters: hardware resource type encoding, access level threshold) is used to map various hardware resources to corresponding items in the security policy rule table, resulting in executable permission matrix data. This permission matrix adopts a two-dimensional Boolean array structure, where the row index corresponds to the resource category, the column index corresponds to the operation type (read / write / configuration), and the Boolean value indicates whether execution is allowed or prohibited.

[0179] Furthermore, by using a role-based permission inheritance algorithm (parameters: script source role identifier, policy inheritance list), the initialization consistency of permissions for multiple scripts under the same role is achieved, and a resource access constraint vector is generated for rapid verification of subsequent access requests.

[0180] Furthermore, a security policy compiler is used to encode the permission matrix and access constraint vector into instructions, generating a lightweight security token sequence, which is then loaded into the permission verification module of the runtime sandbox to achieve efficient access control rules storage and execution.

[0181] By using the resource interceptor module of the runtime sandbox, the security token sequence generated in the previous step is converted into a hardware access intercept table, enabling real-time permission verification when the script accesses the underlying registers, GPIO and I2C bus, thus achieving the expected technical effect of preventing unauthorized access.

[0182] For example, in a vehicle camera module, when a certain model of Sensor script enters the runtime sandbox, the device UID is 1024, and the resource categories include register resources (encoded 1), GPIO resources (encoded 2), and I2C resources (encoded 3). The write permission bitmask for the register resources in the access control list is... The read permission bitmask is GPIO resources have read and write permissions. The configuration permission bitmask for I2C resources is: The permission matrix is ​​generated into a two-dimensional boolean array using a resource classification mapping algorithm. This corresponds to the read / write permission distribution. The security policy compiler compiles it into a three-segment security token sequence, each containing a resource category code and a permission bit combination code, which is quickly matched by the sandbox permission verification module at runtime. Verification has shown that under this permission configuration, scripts can read registers but cannot write to them, can freely read and write to GPIO ports, and cannot modify the I2C bus configuration. This effectively restricts sensitive resources, ensuring the security and stability of the system.

[0183] S7.3: Based on the script source identifier and signature authentication result, dynamically load the corresponding access control policy table, and perform whitelist verification on the register read and write operations involved in the script to prevent unauthorized address access.

[0184] S7.4: Intercepts and redirects system call interfaces involved in script execution, and transforms the original operation into a controlled and standardized access process through the built-in security encapsulation layer of LDML to ensure the controllability of underlying hardware access.

[0185] S7.5: Based on a real-time monitoring mechanism, abnormal detection is performed on the script execution status. When an illegal access attempt, timeout execution, or abnormal resource usage is detected, the sandbox interruption mechanism is triggered and a security event log is recorded to achieve dynamic protection and traceability of the script execution process.

[0186] Step S8: When adding a new sensor model, only a script configuration file conforming to the standard instruction set specification needs to be provided and signed for authentication to be integrated into the system. No modification to the middleware code or recompilation of the driver library is required. Specifically, this includes: S8.1: Based on the predefined standard instruction set specification, generate a script configuration file corresponding to the newly added sensor model. The script configuration file uses a lightweight scripting language to describe the differentiated register operation sequence of the sensor when executing standard instructions, so as to realize the abstract encapsulation of the sensor's underlying behavior.

[0187] Based on the hardware and communication interface description data of the newly added sensor model, a standard instruction set mapping analysis method (parameters: standard instruction set structure definition, interface register list, operation semantic specification) is used to parse and encapsulate the instruction mapping relationship of the underlying control operation of the new sensor. Furthermore, a differentiated register behavior modeling method (parameters: register address space partitioning, access timing constraints, state response mode) is used to decompose and classify the register operation sequences provided by the original manufacturer, obtaining a set of operation templates corresponding to standard instruction items. Further, a lightweight scripting language modeling method (parameters: Lua runtime environment configuration, JavaScript language subset constraints, timing delay function library) is used to convert the differentiated operation templates into executable script statements, generating script function blocks containing operation sequences, delay parameters, and conditional judgment logic. Finally, a parameterized code generation method (parameters: standard instruction semantic identifier, sensor model UID, register access macro) is used to name and bind each function block in the script configuration file, generating a configuration file data structure with a unified calling interface. By using script file structure consistency verification methods (algorithms: syntax tree comparison, instruction set coverage detection), the results of the previous step are transformed into a new Sensor script configuration file that meets the specifications, enabling correct adaptation and zero-intrusion integration during subsequent loading and execution.

[0188] S8.2: Perform digital signature authentication on the generated script configuration file. Verify the script content using an asymmetric encryption algorithm to ensure the script's legitimate origin and lack of tampering, thus guaranteeing system security.

[0189] S8.3: Upload the signed and authenticated script configuration file to the storage unit of the vehicle camera module, and update the correspondence between the local device identifier and the script path according to the preset device mapping table structure to realize the recognition and loading support of the new sensor model.

[0190] S8.4: During the power-on initialization process of the vehicle camera module, the lightweight driver middleware layer identifies the newly added sensor model according to the device mapping table and automatically loads its corresponding script configuration file without modifying the core middleware code logic, thus achieving zero-intrusion driver integration.

[0191] S8.5: Based on the script interpretation engine built into the lightweight driver middleware layer, the script configuration files of the newly added sensor models are subjected to syntax verification and cache preheating to ensure that they can be quickly parsed and executed at runtime, thereby completing the dynamic adaptation of the new sensor models.

[0192] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0193] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0194] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-sensor compatibility method for an automotive camera module, specifically including: S1: Lightweight driver middleware layer LDML and script configuration files for multiple manufacturers' sensors are pre-installed in the storage unit of the vehicle camera module. S2: When the vehicle camera module is powered on and initialized, it identifies the connected sensor model through the I2C or SerDes communication interface and queries the local device mapping table to obtain its unique identifier. S3: Based on the identified Sensor model identifier, load the corresponding script configuration file from the storage unit, and perform syntax verification and cache preheating by the lightweight interpretation engine built into the lightweight driver middleware layer LDML. S4: Converts control requests issued by the cockpit software into predefined standard instruction codes, and the lightweight driver middleware layer finds the corresponding script logic based on the current sensor type; S5: Based on script logic, it performs low-level register access and timing control operations, completes sensor initialization, parameter configuration, image flow control and other operations, and feeds back the execution status to the cockpit software; S6: Dynamically detect the script execution frequency at runtime and compile frequently used script segments into bytecode cache to avoid repeated parsing; S7: Restrict scripts' direct access to hardware resources to ensure the security of script execution; S8: When adding a new sensor model, only a script configuration file conforming to the standard instruction set specification and signed is required to be included in the system for operation, without modifying the middleware code or recompiling the driver library.

2. The multi-sensor compatibility method for an in-vehicle camera module according to claim 1, characterized in that, The script configuration file mentioned in step S1 defines the differentiated register operation logic for the corresponding sensor based on the standard instruction set.

3. The multi-sensor compatibility method for an in-vehicle camera module according to claim 1, characterized in that, Step S3 specifically includes: Based on the identified Sensor model identifier, locate and retrieve the corresponding script configuration file from the storage unit; The obtained script configuration file is subjected to integrity verification, and the file content is verified for consistency based on the hash verification algorithm to ensure that the script has not been tampered with or damaged. The lightweight interpretation engine built into the lightweight driver middleware layer performs syntax parsing on the validated script configuration file, extracts the standard instructions and register operation sequences defined therein, and generates an abstract syntax tree in the intermediate representation form. A script logic cache table is constructed based on an abstract syntax tree, and frequently called script fragments are pre-compiled to generate directly executable bytecode cache. The generated bytecode cache is bound to the corresponding Sensor model identifier and loaded into the runtime execution context of the lightweight driver middleware layer to complete cache preheating.

4. A multi-sensor compatibility method for an in-vehicle camera module according to claim 3, characterized in that, The script configuration file is stored in a compressed text format.

5. A multi-sensor compatible method for an in-vehicle camera module according to claim 1, characterized in that, Step S4 specifically includes: Perform syntax parsing on the raw control requests issued by the cockpit software; Based on the semantic mapping rules defined by the standard instruction set, the parsed key control semantic information is converted into corresponding standard instruction codes to form a unified format of intermediate layer control instructions. Based on the currently identified Sensor model identifier, the corresponding script routing table is called from the lightweight driver middleware layer to locate the script logic execution path that matches the current Sensor type; The converted standard instruction code is used as input parameter and passed to the corresponding script logic execution engine to trigger the script parser to extract the register operation sequence corresponding to the instruction in the script configuration file; Based on the register operation sequence extracted by the script logic execution engine, an executable low-level control command stream is generated.

6. A multi-sensor compatibility method for an automotive camera module according to claim 5, characterized in that: The control requests in the cockpit software of S4 include control type, target parameters, and operating mode.

7. A multi-sensor compatible method for an automotive camera module according to claim 1, characterized in that, Step S5 specifically includes: Based on the lightweight driver middleware layer LDML parsing standard instruction code, combined with the current sensor model identifier, the corresponding script logic fragment is obtained to determine the register address sequence and operation timing required by the sensor model when executing the current instruction; The register access sequence defined in the script logic segment is parsed and parameters are bound to generate an executable low-level hardware access instruction set. Based on the generated low-level hardware access instruction set, register write or read operations are performed on the sensor chip through the I2C or SerDes communication interface. The timing of the operation is controlled according to the delay parameters and status polling mechanism defined in the script to meet the timing constraints of the sensor chip. After completing register access and timing control, the response status code and feedback data of the sensor chip are collected and encapsulated into standardized status feedback information through a lightweight driver middleware layer so that the cockpit software can identify the current working status of the sensor. The packaged standardized status feedback information is transmitted to the cockpit software through a preset standardized communication protocol, and the operation log is recorded to the local storage unit.

8. A multi-sensor compatibility method for an in-vehicle camera module according to claim 1, characterized in that: The S8 supports any new sensor model to be registered directly in the camera module storage unit and dynamically loaded after writing a configuration script file that conforms to the standard instruction set specification and passing the signature authentication.

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