A chassis electronic and electrical architecture of a vehicle and an environmental perception method for the chassis electronic and electrical architecture

Abstract

This application discloses a vehicle chassis electronic and electrical architecture and an environmental perception method for the chassis electronic and electrical architecture, relating to the field of autonomous driving technology. The chassis electronic and electrical architecture includes: a central computing platform, an Ethernet switch, a smart antenna layer, a sensor layer, and a regional controller layer; the central computing platform is connected to the Ethernet switch; the Ethernet switch is connected to the regional controller layer; the front regional controller is connected to the first type of perception sensor module; the middle regional controller is connected to the second type of perception sensor module; and the rear regional controller is connected to the third type of perception sensor module; the central computing platform includes a data initialization thread, an algorithm thread, and a control thread; the regional controller layer obtains various control instructions through the Ethernet switch; and various control instructions and initial sensor data are transmitted to each other through the smart antenna layer, thereby optimizing the design of the vehicle chassis electronic and electrical architecture.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a vehicle chassis electronic and electrical architecture and an environmental perception method for the chassis electronic and electrical architecture. Background Art

[0002] In the fields of intelligent driving or outdoor mobile robots, the development of environmental perception technology is crucial. In existing technologies, common environmental perception systems usually rely on multiple sensors, such as cameras, lidar, and IMU, which are connected to a central computing platform via wiring harnesses.

[0003] However, with the development of intelligent driving technology, higher requirements are placed on the real-time transmission of sensor data, the reliability of the system, and the complexity of wiring. Therefore, the current technical problems mainly include: First: the complexity and reliability of the wiring harness. The traditional wired connection method makes the system wiring complicated, which not only increases the difficulty of installation and maintenance, but also reduces the reliability of the system. Problems such as broken wiring harnesses or poor contact can easily lead to data transmission interruptions, affecting system performance. Second, data transmission efficiency. With the increase in the number of sensors, the existing data transmission methods have gradually shown bottlenecks in bandwidth and real-time performance, and cannot meet the needs of efficient multi-sensor data fusion and processing. Third, the hardware integration is low. In the existing chassis electronic and electrical architecture, various sensors and controllers are installed in a scattered manner, lacking a unified management and coordination mechanism, resulting in low system integration and difficulty in achieving collaborative work and unified scheduling.

[0004] Based on the above problems, how to optimize the chassis electronic and electrical architecture so that it can avoid low operating efficiency when performing complex environmental perception and data fusion and ultimately improve the environmental perception capability of the chassis electronic and electrical architecture is an issue that needs to be solved urgently. Summary of the Invention

[0005] The purpose of this application is to provide a vehicle chassis electronic and electrical architecture and an environmental perception method for the chassis electronic and electrical architecture, which can solve the problem of low operating efficiency caused by low integration of the chassis electronic and electrical architecture in complex environments, and thus low environmental perception ability of the chassis electronic and electrical architecture.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a chassis electronic and electrical architecture of a vehicle, comprising: a central computing platform, an Ethernet switch, a smart antenna layer, a sensor layer and a regional controller layer; the central computing platform is connected to the Ethernet switch; the sensor layer comprises a first type of perception sensing module, a second type of perception sensing module and a third type of perception sensing module; the sensor layer is used to output initial sensor data; the Ethernet switch is connected to the regional controller layer; the regional controller layer is used to filter, compress and analyze the initial sensor data, generate sensor data, and send it to the central computing platform through the Ethernet switch; the regional controller layer comprises a front regional controller, a middle regional controller and a rear regional controller; the front regional controller is connected to the first type of perception sensing module; the middle regional controller is connected to the second type of perception sensing module; the rear regional controller is connected to the third type of perception sensing module; the central computing platform The platform includes a data initialization thread, an algorithm thread and a control thread. The data initialization thread is used to preprocess and calibrate the sensor data to obtain initial data; the algorithm thread is used to perform environmental semantic segmentation, dynamic feature extraction, and synchronous positioning and mapping on the initial data in real time to generate an environmental map, which is used to locate the vehicle; the control thread is used to generate various control instructions based on the environmental map and the sensor data; the regional controller layer obtains the various control instructions through the Ethernet switch; the various control instructions and the initial sensor data are transmitted to each other through the smart antenna layer; the various control instructions are used to control the working modes of various sensors in the sensor layer, so that the chassis electronic and electrical architecture of the vehicle can obtain newly perceived initial sensor data in real time by controlling the working modes of various sensors in the sensor layer, and send the newly perceived initial sensor data to the central computing platform based on the smart antenna layer to output new control instructions.

[0008] In a second aspect, a method for environmental perception of a vehicle chassis electronic and electrical architecture is provided, comprising:

[0009] Based on the smart antenna layer, the initial sensor data obtained from the sensor layer is optimized to obtain optimized initial sensor data, and the optimized initial sensor data is sent to the regional control layer; the optimized initial sensor data is filtered, compressed and analyzed by the regional control layer to generate sensor data, and the sensor data is transmitted to the central computing platform through the Ethernet switch; the sensor data is calculated by the central computing platform to generate various control instructions, and the various control instructions are sent to the regional controller layer through the Ethernet switch; based on the various control instructions, the working modes of various sensors in the sensor layer are controlled; by controlling the working modes of various sensors in the sensor layer, newly perceived initial sensor data is obtained in real time, and based on the smart antenna layer, the newly perceived initial sensor data is sent to the central computing platform to output new control instructions.

[0010] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0011] This application provides a vehicle chassis electronic and electrical architecture and an environmental perception method for the chassis electronic and electrical architecture. This application first integrates initial sensor data at the sensor layer to support intelligent driving perception functions. The regional controller layer then processes the initial sensor data via an Ethernet switch and transmits it to a central computing platform. The central computing platform then processes the initial sensor data in parallel through three threads: a data initialization thread, an algorithm thread, and a control thread. This improves the computing power and processing efficiency of the computing unit. The regional controller layer includes three parts: a front regional controller, a middle regional controller, and a rear regional controller. This allows for centralized installation and unified management of the controllers, optimizing the integration of the entire chassis electronic and electrical architecture. The control threads output various control commands that are sent to the regional controller layer. The regional controller layer and the perception and sensing module layer exchange various control commands and initial sensor data via a smart antenna layer, realizing a wireless chassis architecture. This further optimizes the electronic and electrical architecture design and reduces wiring harness usage and chassis layout workload. Furthermore, through the various control instructions output by the central computing platform, the regional controller layer controls the working modes of various sensors in the sensor layer to obtain the newly perceived initial sensor data in real time, and then output new control instructions, ultimately improving the computing efficiency of the chassis electronic and electrical architecture, and thus improving the environmental perception capabilities of the entire chassis electronic and electrical architecture.

[0012] In addition, this application connects the front area controller with the first type of perception sensing module, the middle area controller with the second type of perception sensing module, and the rear area controller with the third type of perception sensing module. This design effectively avoids the allocation of computing resources being concentrated on a single module, which in turn easily leads to computing bottlenecks and low operating efficiency. Ultimately, it improves the computing power and processing efficiency of the computing unit, ensuring the efficient operation and stable performance of the system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 This is a schematic diagram of the electronic and electrical architecture of the chassis of a vehicle provided in an embodiment of the present application.

[0015] Figure 2 This is a schematic diagram of the overall structure of the chassis electronic and electrical architecture of a vehicle provided in an embodiment of the present application.

[0016] Figure 3 This is a flow chart of a method for sensing the chassis electronic and electrical architecture environment of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] like Figure 1 and Figure 2 As shown, the present application provides a chassis electronic and electrical architecture for a vehicle, including:

[0020] Central computing platform 1, Ethernet switch 2, smart antenna layer 3, sensor layer 4 and regional controller layer 5; the central computing platform 1 is connected to the Ethernet switch 2; the sensor layer 4 includes a first type of perception sensor module 41, a second type of perception sensor module 42 and a third type of perception sensor module 43; the sensor layer 4 is used to output initial sensor data; the Ethernet switch 2 is connected to the regional controller layer 5; the regional controller layer 5 is used to filter, compress and analyze the initial sensor data, generate sensor data, and send it to the central computing platform 1 through the Ethernet switch 2; the regional controller layer 5 includes a front regional controller 51, a middle regional controller 52 and a rear regional controller 53; the front regional controller 51 is connected to the first type of perception sensor module 41; the middle regional controller 52 is connected to the second type of perception sensor module 42; the rear regional controller 53 is connected to the third type of perception sensor module 43; the central computing platform 1 Platform 1 includes a data initialization thread, an algorithm thread and a control thread. The data initialization thread is used to preprocess and calibrate the sensor data to obtain initial data; the algorithm thread is used to perform environmental semantic segmentation, dynamic feature extraction, and synchronous positioning and mapping on the initial data in real time to generate an environmental map, which is used to locate the vehicle; the control thread is used to generate various control instructions based on the environmental map and the sensor data; the regional controller layer 5 obtains the various control instructions through the Ethernet switch 2; the various control instructions and the initial sensor data are transmitted to each other through the smart antenna layer 3; the various control instructions are used to control the working modes of various sensors in the sensor layer 4, so that the chassis electronic and electrical architecture of the vehicle can obtain newly perceived initial sensor data in real time by controlling the working modes of various sensors in the sensor layer 4, and send the newly perceived initial sensor data to the central computing platform 1 based on the smart antenna layer 3 to output new control instructions.

[0021] Among them, the central computing platform 1 serves as the core of the intelligent environmental perception algorithm, and is responsible for the preprocessing and calibration of sensor information, data fusion, environmental perception and decision-making, and result output and control. This application is developed based on a vehicle chassis or an outdoor mobile robot chassis, involving a large amount of processing data, a large computing load, and complex control logic. The Jetson Orin processor is used as the main controller. The Jetson Orin processor is a high-performance, low-power 32-bit microprocessor automotive-grade MCU equipped with a 2048-core NVIDIA Ampere architecture GPU with 64 Tensor Cores. It is widely used in the automotive control industry.

[0022] Specifically, the central computing platform contains three main threads, each of which is responsible for different data processing processes and functions.

[0023] First, the data initialization thread (running on the CPU) is responsible for acquiring and processing initial sensor data from various sensors. This includes surround view image fusion, lidar point cloud processing, and inertial measurement unit (IMU) data integration. The surround view image fusion strategy utilizes multi-sensor data fusion technology to fuse multiple sensor data into a unified view of the environment. LiDAR point cloud data processing utilizes a time-varying weighting strategy to ensure data accuracy in various environments. The IMU is used to accurately measure the vehicle's attitude and acceleration.

[0024] Secondly, the algorithm thread (running on the GPU) is responsible for high-level data processing, including semantic segmentation of the environment, dynamic feature extraction, and simultaneous localization and mapping (SLAM). Semantic segmentation uses deep learning models to classify and identify different environmental elements. Dynamic feature extraction uses machine learning algorithms to identify moving objects and their trajectories. The SLAM algorithm generates a real-time map of the environment using sensor data and performs vehicle positioning. The algorithm thread leverages the high-performance computing capabilities of the GPU to enable real-time processing of complex algorithms.

[0025] Finally, the control thread processes the environmental view and sensor data obtained by the algorithm thread to generate various control signals. These control signals dynamically adjust the operating modes of various sensors in the sensor layer 4 based on environmental changes to optimize data collection. The control thread communicates with the vehicle's actuators through the physical interface of the regional controller layer 5 to ensure the precise execution of various control commands.

[0026] The three threads transmit the sensor data obtained from the regional controller layer 5 to the central computing platform 1 through the Ethernet switch, and the central computing platform 1 then performs further comprehensive analysis and decision support, forming a complete data processing and control closed loop to ensure the safe and efficient operation of the vehicle in complex environments.

[0027] The regional controller layer 5 consists of front, middle, and rear regional controllers, each of which is connected to the Ethernet switch 2 and supplied with 12V power by the vehicle power supply module 6. These controllers communicate with the Ethernet switch 2 via the MAC interface.

[0028] Specifically, the primary function of the regional controller layer 5 is to preprocess the initial sensor data in preparation for processing by the central computing platform 1, without duplicating the processing performed by the central computing platform. Specifically, the regional controller layer 5 filters, compresses, and performs preliminary analysis on the data to reduce data transmission volume, improve transmission efficiency, and provide simplified data for subsequent in-depth analysis.

[0029] The data processing process of the regional controller layer 5 is as follows:

[0030] aData filtering: Use a low-pass filtering algorithm to remove noise from the initial sensor data.

[0031] b Data compression: Apply data compression algorithms Huffman coding and downsampling to reduce the volume of initial sensor data.

[0032] c Preliminary analysis: Implement a simple edge detection algorithm to identify the outline of objects in the image and use basic kinematic equations to estimate the object velocity.

[0033] The zone controller layer 5 acquires initial sensor data from the connected sensor layer 4 (e.g., image processing, radar, IMU, etc.). Initial sensor data is first filtered to remove significant noise and erroneous data. The initial sensor data is then compressed, using effective encoding techniques to reduce the data volume. Based on this data, preliminary analysis is performed, such as identifying basic object outlines and estimating velocity.

[0034] The regional controller layer 5 ensures that the initial sensor data has been preliminarily processed before being transmitted to the central computing platform 1, reducing the computational burden on the central computing platform 1 and optimizing overall system performance. This includes cleaning and compressing the initial sensor data to reduce bandwidth requirements and performing preliminary analysis to provide concise and useful information for further processing by the central computing platform 1.

[0035] This processing is performed on the zone controller’s embedded processor, ensuring that the initial sensor data is optimized and simplified before being transmitted to the central computing platform, thereby improving the efficiency and performance of the entire chassis electronic and electrical architecture.

[0036] In some embodiments, the first type of perception sensing module 41 includes multiple cameras; the multiple cameras are used to collect environmental image data around the vehicle; the second type of perception sensing module 42 includes a lidar; the lidar is used to collect spatial environmental data of the vehicle; the third type of perception sensing module 43 includes an inertial measurement unit; the inertial measurement unit is used to measure and report the vehicle's posture and acceleration data; the initial sensor data includes the environmental image data, the spatial environment data, and the vehicle's posture and acceleration data.

[0037] Among them, the perception sensor in the first type of perception sensing module 41 can be 6 monocular cameras with a 360° circumference at the front, which are used to capture environmental image data; the second type of perception sensing module 42 has a laser radar to collect three-dimensional environmental data; the third type of perception sensing module 43 has an on-board IMU, which is used to obtain the vehicle's posture and acceleration data.

[0038] Specifically, the front zone controller is connected to six monocular cameras, primarily transmitting high-resolution visual image data. This image data is used for environmental perception, such as pedestrian detection, lane keeping, and traffic sign recognition. The central zone controller's lidar transmits high-precision three-dimensional spatial data, providing a detailed map of the surrounding environment and assisting with obstacle detection and navigation. The inertial measurement unit in the rear zone controller transmits data on vehicle acceleration and orientation, supporting vehicle stability and dynamic monitoring. This wirelessly transmitted data encompasses a wide range of categories, from basic mechanical control to advanced environmental perception and navigation, and is essential for the proper functioning of autonomous vehicles. The smart antenna layer 3 plays a crucial role in this process, not only ensuring real-time transmission of initial sensor data but also optimizing data transmission efficiency and security through its advanced signal processing capabilities.

[0039] In some embodiments, it further includes: an on-board power supply module 6; the on-board power supply module is used to convert high voltage into low voltage.

[0040] First, the central computing platform 1 uses a 24V power supply due to its high-performance computing needs to ensure stability and sufficient power. Second, the Ethernet switch 2 requires a 12V power supply and can be powered directly from the 12V output of the vehicle power supply module 6. The smart antenna layer 3, including the smart antenna controller and antenna array, also uses a 12V power supply.

[0041] For sensor layer 3, different sensors have different voltage requirements, but all operate between 5V and 24V. To simplify the design, a 12V power supply is selected. The zone controllers typically also operate between 5V and 12V and also use a 12V power supply.

[0042] Specifically, to ensure the safety and reliability of the entire architecture, a centralized on-board power supply module 6 is designed to convert high voltage to the low voltage required by each module. Fuses and relays are used to protect each branch circuit from overloads and short circuits. Furthermore, electromagnetic compatibility should be considered, ensuring that all power lines are well shielded and grounded, and including voltage and current monitoring functions to monitor system status in real time and perform fault diagnosis. This design not only ensures that each component receives proper power but also enhances the overall performance of the system.

[0043] In some embodiments, the second type of perception sensing module also includes a wire-controlled steering and braking control unit; the wire-controlled steering and braking control unit is used to generate a current operating status signal of the vehicle based on the spatial environment data; the current operating status signal includes a steering angle and a braking force; the third type of perception sensing module also includes a battery control unit and a motor control unit; the battery control unit is used to generate battery status information based on the posture and acceleration data of the vehicle; the battery status information includes battery charge and battery temperature; the motor control unit is used to generate motor operating data based on the posture and acceleration data of the vehicle; the initial sensor data also includes the current operating status signal, the battery status information and the motor operating data.

[0044] Among them, the functions of the wire-controlled steering unit and the brake control unit are to transmit signals about the current operating status of the vehicle, such as steering angle and braking force. These signals are used to perform precise vehicle control operations.

[0045] Among them, the function of the battery control unit is to transmit battery status information, such as power level and temperature, to ensure energy management of electric vehicles.

[0046] Among them, the function of the motor control unit is to transmit motor operation data for adjusting motor output to ensure optimal driving performance and energy efficiency.

[0047] The various control commands also include chassis control and power supply control. Chassis control is used to adjust the vehicle's direction and speed. Power supply control is used to ensure stable power supply to the system and prevent system failure due to insufficient power.

[0048] In practical applications, chassis control is generated based on current operating status signals as well as vehicle posture and acceleration data. Model Predictive Control (MPC) algorithms are used to achieve precise chassis control. Power supply control is primarily based on battery status information and motor operating data. An Extended Kalman Filter (EKF) is used to estimate the battery's health status, and a Power Management Algorithm (PMA) is used to schedule battery and motor power.

[0049] In some embodiments, the smart antenna layer 3 includes a smart antenna controller and an antenna array; the smart antenna layer is used to optimize and process the initial sensor data of the sensor layer based on the smart antenna controller using beamforming technology, generate optimized initial sensor data, and transmit the optimized sensor data to the regional controller layer through the antenna array.

[0050] The smart antenna layer 3 is used to establish wireless communication connections. The smart antenna controller uses algorithms to adjust the operating status of each antenna in the antenna array to optimize signal coverage and transmission. Using beamforming technology, the controller guides the antenna array to transmit a focused signal beam, improving signal directionality and transmission range, thereby enhancing communication quality in complex environments.

[0051] The smart antenna controller primarily adjusts and optimizes the antenna array's signal reception and transmission capabilities. Smart antenna layer 3 utilizes digital signal processing algorithms to enhance signal reception quality and directional transmission capabilities. Regarding connectivity, the smart antenna controller connects directly to the antenna array via Ethernet, precisely controlling the direction of signal transmission and reception, significantly improving the efficiency and quality of wireless communications. The smart antenna controller utilizes sophisticated signal processing algorithms, adaptive beamforming, and pattern optimization to calculate optimal antenna parameters in real time. The smart antenna controller adjusts the individual elements of the antenna array based on environmental changes, achieving optimal signal transmission through electrical phase adjustment and power control. The antenna array is arranged in a specific geometric layout, enabling the system to flexibly respond to signal demands from different directions. This design not only improves signal reliability and quality, but also effectively reduces interference and enhances communication efficiency.

[0052] In some embodiments, the central computing platform 1 is connected to the Ethernet switch 2 via an Ethernet cable; the sensor layer 4, the regional controller layer 5 and the smart antenna layer 3 are all connected to the Ethernet switch 2 via Ethernet cables; the Ethernet switch 2 communicates with the regional controller layer 5 via a media access control interface.

[0053] In practice, the central computing platform 1 is connected directly to the Ethernet switch 2 via a CAT7 Ethernet cable. This cable supports higher bandwidth and can effectively handle high-speed data transmission. Manually configuring the IP settings during connection ensures stable communication between devices without relying on DHCP.

[0054] For switch configuration, use MGBE (Multi-Gigabit Ethernet) lines to connect an Ethernet switch to Jetson Orin for data transmission, and use MDIO / MDC lines for switch configuration. It is important to enable the switch and configure the port settings before powering on the system to ensure that the network settings are initialized correctly when the system boots.

[0055] Among them, for the Ethernet switch 2 and the regional controller layer 5, a wired Ethernet connection is used. The Cat 6a network cable can support a transmission speed of 10Gbps, which can not only ensure high-speed and stable data transmission, but also enhance the security and stability of the entire network system. It is crucial to ensure efficient and secure data communication between the regional controller and the Ethernet switch.

[0056] Among them, MAC (Medium Access Control Layer) is part of the data link layer in the OSI (Open Systems Interconnection Model) seven-layer model, which is responsible for managing and controlling how devices access shared transmission media. MAC is located in the lower half of the data link layer, and together with the LLC (Logical Link Control) layer, constitutes the data link layer. During the MAC connection process, sensor data processing involves several key steps. First, when sending sensor data, MAC encapsulates the data packets of the upper layer protocol into frames, including adding MAC header information such as source address, destination address and control information. When receiving sensor data, MAC decapsulates the frame, extracts the sensor data and hands it over to the upper layer protocol for processing. Secondly, MAC manages the access of network devices to the shared communication medium through the CSMA / CD or CSMA / CA mechanism to prevent data conflicts. The Ethernet physical layer (PHY) and MAC communicate through MDI to ensure the stability and accuracy of sensor data transmission on the physical medium. In Figure 2 In the connection shown, data is sent from the MAC interface of the central computing platform and transmitted to the PHY layer via the MDI interface. The PHY layer modulates the sensor data into electrical signals and transmits them via the ETH+ and ETH- connectors. The PHY layer on the receiving end transmits the sensor data back to the MAC via the MDI interface, completing the reception and decapsulation of the sensor data. The MAC is also responsible for frame error detection (such as CRC check) to ensure the integrity of the sensor data. When an error is detected, it discards the erroneous frame and requests the sensor data to be resent. The MAC manages the sensor data transmission rate and sends pause frames through flow control mechanisms (such as IEEE 802.3x flow control) to control the data flow rate on the sending end. Through these steps, the MAC and PHY layers work together to ensure the reliable transmission and processing of sensor data in the network.

[0057] Reference Figure 3 , provides an environment perception method for a chassis electronic and electrical architecture of a vehicle, comprising:

[0058] Step 101: Based on the smart antenna layer, the initial sensor data obtained from the sensor layer is optimized to obtain optimized initial sensor data, and the optimized initial sensor data is sent to the regional control layer.

[0059] Step 102: filtering, compressing, and analyzing the optimized initial sensor data through the regional control layer to generate sensor data, and transmitting the sensor data to the central computing platform through the Ethernet switch;

[0060] Step 103: Calculating the sensor data through the central computing platform to generate various control instructions, and sending the various control instructions to the regional controller layer through the Ethernet switch;

[0061] Step 104: Controlling the operating modes of various sensors of the sensor layer based on the various control instructions;

[0062] Step 105: By controlling the working modes of various sensors in the sensor layer, the newly perceived initial sensor data is acquired in real time, and based on the smart antenna layer, the newly perceived initial sensor data is sent to the central computing platform to output new control instructions.

[0063] In some embodiments, step 103 specifically includes:

[0064] Step 201: Preprocess and calibrate the sensor data to obtain initial data through the data initialization thread of the central computing platform.

[0065] Step 202: Performing environmental semantic segmentation, dynamic feature extraction, and synchronous positioning and mapping on the initial data in real time through the algorithm thread of the central computing platform to generate an environmental map.

[0066] Step 203: Generate the various control instructions based on the control thread of the central computing platform, the environment map and the sensor data.

[0067] In some embodiments, step 201 specifically includes:

[0068] Step 301: Using a surround view image fusion strategy to fuse the sensor data to generate an environment view.

[0069] Step 302: Process the sensor data using a time-varying weight strategy to obtain lidar point cloud data; the initial data includes the environment view, the lidar point cloud data, and the vehicle's posture and acceleration data; wherein the vehicle's posture and acceleration data are obtained based on measurements of the inertial measurement unit of the sensor layer.

[0070] Step 202 specifically includes:

[0071] Step 401: Perform environment semantic segmentation on the environment view to obtain a view classification result.

[0072] Step 402: Dynamically extract features from the view classification results, the lidar point cloud data, and the vehicle's posture and acceleration data using a machine learning algorithm, and obtain the environment map based on a simultaneous positioning and mapping algorithm.

[0073] This application implements a wireless beam chassis architecture through a central computing platform 1, a regional controller layer 5, and a smart antenna layer 3, integrating various sensors such as cameras, lidars, and inertial measurement units (IMUs) to provide support for intelligent driving perception functions. The Ethernet interfaces of various sensors, regional controller layer 5, and smart antenna layer 3 in this application are all connected to the Ethernet switch 2 of the central computing platform 1 through the vehicle Ethernet bus. Sensors in different areas of the chassis are powered by the vehicle power supply according to the principle of proximity. The regional controller layer 5 controls the corresponding sensors according to different working condition instructions, and transmits the initial sensor data to the central computing platform 1 through the vehicle Ethernet through the smart antenna layer 3. The central computing platform 1 receives sensor data and provides services for intelligent driving based on the environmental perception algorithm. At the same time, it separates the data initialization thread, algorithm thread, and control thread to realize parallel processing of the three parts, which can improve the computing power and processing efficiency of the computing unit.

[0074] This application optimizes the electronic and electrical architecture design, reduces the use of wiring harnesses and the workload of chassis layout, and further improves the computing power and processing efficiency of the computing unit through innovative parallel processing architecture design, ensuring the efficient operation and stable performance of the system in complex environments.

[0075] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0076] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0077] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0078] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0079] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRdM), magnetic random access memory (MRdM), ferroelectric random access memory (FRdM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RdM) or external cache memory, etc. By way of illustration and not limitation, RdM may be in various forms, such as static random access memory (SRdM) or dynamic random access memory (DRdM).

[0080] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0081] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A chassis electronic and electrical architecture for a vehicle, characterized in that: include: Central computing platform, Ethernet switch, smart antenna layer, sensor layer and regional controller layer; The central computing platform is connected to the Ethernet switch; The sensor layer includes a first type of sensing sensor module, a second type of sensing sensor module and a third type of sensing sensor module; the sensor layer is used to output initial sensor data; The Ethernet switch is connected to the regional controller layer; the regional controller layer is used to filter, compress and analyze the initial sensor data to generate sensor data and send it to the central computing platform through the Ethernet switch; the regional controller layer includes a front regional controller, a middle regional controller and a rear regional controller; the front regional controller is connected to the first type of perception sensor module; the middle regional controller is connected to the second type of perception sensor module; and the rear regional controller is connected to the third type of perception sensor module; The central computing platform includes a data initialization thread, an algorithm thread, and a control thread, wherein the data initialization thread is used to preprocess and calibrate the sensor data to obtain initial data; The algorithm thread is used to perform real-time environmental semantic segmentation, dynamic feature extraction, and synchronous positioning and mapping on the initial data to generate an environmental map, which is used to locate the vehicle; the control thread is used to generate various control instructions based on the environmental map and the sensor data; The regional controller layer obtains the various control instructions through the Ethernet switch; The various control instructions and the initial sensor data are transmitted to each other through the smart antenna layer; the various control instructions are used to control the working modes of various sensors in the sensor layer, so that the chassis electronic and electrical architecture of the vehicle can obtain the newly perceived initial sensor data in real time by controlling the working modes of various sensors in the sensor layer, and send the newly perceived initial sensor data to the central computing platform based on the smart antenna layer to output new control instructions.

2. The vehicle chassis electrical and electronic architecture according to claim 1, characterized in that: The first type of perception sensing module includes multiple cameras; the multiple cameras are used to collect environmental image data around the vehicle; the second type of perception sensing module includes a laser radar; the laser radar is used to collect spatial environmental data of the vehicle; the third type of perception sensing module includes an inertial measurement unit; the inertial measurement unit is used to measure and report vehicle posture and acceleration data; The initial sensor data includes the environmental image data, the spatial environment data, and the posture and acceleration data of the vehicle.

3. The vehicle chassis electrical and electronic architecture according to claim 2, characterized in that: The second type of perception sensor module further includes a wire-controlled steering and braking control unit; the wire-controlled steering and braking control unit is configured to generate a current operating state signal of the vehicle based on the spatial environment data; the current operating state signal includes a steering angle and a braking force; The third type of perception sensor module also includes a battery control unit and a motor control unit; The battery control unit is used to generate battery status information based on the posture and acceleration data of the vehicle; the battery status information includes battery power and battery temperature; The motor control unit is used to generate motor operation data based on the posture and acceleration data of the vehicle; The initial sensor data further includes the current operating state signal, the battery state information, and the motor operating data.

4. The vehicle chassis electrical and electronic architecture according to claim 1, characterized in that: The smart antenna layer includes a smart antenna controller and an antenna array; The smart antenna layer is used to optimize and process the initial sensor data of the sensor layer based on the smart antenna controller using beamforming technology, generate optimized initial sensor data, and transmit the optimized sensor data to the regional controller layer through the antenna array.

5. The vehicle chassis electrical and electronic architecture according to claim 1, characterized in that: The central computing platform is connected to the Ethernet switch via an Ethernet cable; The sensor layer, the zone controller layer and the smart antenna layer are all connected to the Ethernet switch via Ethernet cables; The Ethernet switch communicates with the zone controller layer via a media access control interface.

6. The vehicle chassis electrical and electronic architecture according to claim 1, characterized in that: It also includes: an on-board power supply module; the on-board power supply module is used to convert high voltage into low voltage.

7. A method for environmental perception of a vehicle chassis electronic and electrical architecture, characterized in that: include: Based on the smart antenna layer, the initial sensor data obtained from the sensor layer is optimized to obtain optimized initial sensor data, and the optimized initial sensor data is sent to the regional control layer; filtering, compressing and analyzing the optimized initial sensor data through the regional control layer to generate sensor data, and transmitting the sensor data to the central computing platform through the Ethernet switch; Calculating the sensor data through the central computing platform to generate various control instructions, and sending the various control instructions to the regional controller layer through the Ethernet switch; Based on the control instructions, control the working modes of various sensors in the sensor layer; By controlling the working modes of various sensors in the sensor layer, the newly perceived initial sensor data is acquired in real time, and based on the smart antenna layer, the newly perceived initial sensor data is sent to the central computing platform to output new control instructions.

8. The environment perception method of chassis electronic and electrical architecture according to claim 7, characterized in that: The sensor data is calculated by the central computing platform to generate various control instructions, including: Preprocessing and calibrating the sensor data to obtain initial data through a data initialization thread of the central computing platform; Through the algorithm thread of the central computing platform, the initial data is subjected to real-time environment semantic segmentation, dynamic feature extraction, and synchronous positioning and mapping to generate an environment map; The control instructions are generated based on the control thread of the central computing platform, the environment map and the sensor data.

9. The environment perception method of chassis electronic and electrical architecture according to claim 8, characterized in that: Preprocessing and calibrating the sensor data to obtain initial data through the data initialization thread of the central computing platform specifically includes: Using a surround view image fusion strategy to fuse the sensor data to generate an environment view; Processing the sensor data using a time-varying weight strategy to obtain lidar point cloud data; The initial data includes the environment view, the lidar point cloud data, and the vehicle's posture and acceleration data; wherein the vehicle's posture and acceleration data are measured based on the inertial measurement unit of the sensor layer.

10. The environment perception method of chassis electronic and electrical architecture according to claim 9, characterized in that: Through the algorithm thread of the central computing platform, the initial data is subjected to real-time environment semantic segmentation, dynamic feature extraction, and synchronous positioning and mapping to generate an environment map, specifically including: Performing environment semantic segmentation on the environment view to obtain a view classification result; The view classification results, the lidar point cloud data, and the vehicle's posture and acceleration data are dynamically extracted using a machine learning algorithm, and the environment map is obtained based on a simultaneous positioning and mapping algorithm.

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