Automobile intelligent rear window interaction system and method based on AI and vehicle-road cooperation

Abstract

The invention discloses an automobile intelligent rear window interaction system and method based on AI and vehicle-road cooperation, relates to the technical field of intelligent traffic, and constructs a human-vehicle-road cooperation active safety interaction network by fusing transparent Micro-LED display, multi-mode AI interaction and urban traffic big data. The system comprises an embedded transparent display module, a V2X communication unit, an AI decision center and a dynamic space encoder. According to the scheme, a traditional single information prompting mode is broken through, according to actual measurement, the rear-end collision accident rate can be reduced by 35%, the road passing efficiency can be improved by 22%, meanwhile, vehicle-road cloud cooperation and social interaction are supported, and traffic safety and communication efficiency are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to an AI and vehicle-road cooperation based intelligent rear window interaction system and method for vehicle. BACKGROUND

[0002] The existing rear window glass of a vehicle only has basic functions such as perspective and heating, and cannot meet the inter-vehicle communication needs: there is a lack of effective and safe prompt between vehicles (such as emergency braking warning), the driver's emotions are difficult to convey (such as courtesy intention), and the vehicle cannot be integrated into the intelligent transportation network (such as emergency vehicle avoidance and road condition sharing). This leads to frequent rear-end accidents, low road traffic efficiency, and road rage caused by poor communication.

[0003] The present application aims to break through the above limitations, realize the upgrade of the rear window from "passive light transmission" to "active interaction" through the deep integration of transparent display technology and AI interaction, and build an intelligent transportation ecosystem of man-vehicle-road cooperation. SUMMARY

[0004] The present application aims to provide an AI and vehicle-road cooperation based intelligent rear window interaction system and method for vehicle.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: An AI and vehicle-road cooperation based intelligent rear window interaction system and method for vehicle, specifically comprising the following steps: S1, embedding a bendable transparent Micro-LED film into laminated glass, with a pixel density of 100 PPI and supporting full-color display; S2, using pixel shielding technology to solve the problem of direct sunlight, and maintaining an effective brightness of 2000 nit (contrast ratio > 10:1) under an illumination of 100,000 lux; S3, micro-channel packaging process (laser etching 50 μm deep channels) and refractive index matching glue (n = 1.52) filling, taking into account the light transmittance (> 82%) and glass strength.

[0006] Multi-modal interaction system 1. Voice recognition: supporting offline emergency instructions (such as "SOS"), multi-language switching and sensitive word filtering (accuracy 99.2%); 2. Vehicle body signal integration: obtaining real-time vehicle conditions such as steering and brake force through direct connection to CAN bus; 3. Environmental perception: integrating rainfall, light, and millimeter wave radar (detecting the distance of the rear vehicle) data to realize dynamic warning.

[0007] AI decision-making center 1. Based on the federated learning framework, aggregating multi-vehicle driving data to optimize the model; 2. Reinforcement learning dynamically adjusts display strategy to target "safety + efficiency + user satisfaction"; 3. LSTM model predicts driving behavior (such as brake advance), improves timeliness of prompts.

[0008] V2X communication and vehicle-road cooperation 1. Support DSRC / 5G dual-mode communication to realize information interaction between vehicles and traffic facilities (such as intelligent street lamps); 2. In emergency scenarios, link the front vehicles to form a virtual channel, and shorten the response time by 20%.

[0009] The beneficial effects of the present application are: The automobile intelligent rear window interaction system based on AI and vehicle-road cooperation can effectively reduce the rear-end accident rate by 35%, the high-speed rear-end warning is advanced by 2.3 seconds, and the collision risk is effectively avoided. The road traffic efficiency is improved by 22%, the efficiency of the merging area is improved by 15%, the response time in emergency scenarios is shortened by 20%. The light transmittance is > 82%, the display is clear under strong light, the power consumption is reduced by 40%, and the response is fast. Support OTA upgrade, compatible with related standards and smart city traffic brain, the model can be continuously optimized through federated learning. Multi-modal interaction reduces communication problems, ensures communication safety, and builds a human-vehicle-road cooperative safety network.

[0010] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, and can be implemented in accordance with the content of the specification, the following is a detailed description of the preferred embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 The Micro-LED interlayer structure shown in the present application is shown in the figure; Figure 2 The automobile rear window preparation process flowchart shown in the present application is shown in the figure; Figure 3 The automobile rear window display area dynamic partitioning schematic diagram shown in the present application is shown in the figure; Figure 4 The algorithm processing flowchart of the automobile rear window interaction system shown in the present application is shown in the figure; Figure 5 The V2X communication protocol stack architecture diagram of the automobile rear window interaction system shown in the present application is shown in the figure Figure 6 The message conversion logic state machine schematic diagram of the automobile rear window interaction system shown in the present application is shown in the figure.

[0012] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, and can be implemented in accordance with the content of the specification, the following is a detailed description of the preferred embodiments of the present application with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] The technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0014] Reference Figures 1-6 Example 1 Hardware Preparation 1. Glass substrate processing: 4.76 mm chemically strengthened soda-lime glass is selected, and a 355 nm ultraviolet laser is used to process micro-channels (pitch 200 μm, depth 50 μm); 2. Chip transfer: 10 μm x 20 μm Micro-LED chips are implanted with an accuracy of ±1.5 μm, and the driving circuit is integrated on the edge of the glass; 3. Encapsulation and lamination: fill the refractive index matching glue (n = 1.52), and hot-press composite with the outer glass (180℃→120℃ step-down temperature); 4. Calibration: six-axis robot adjusts the module position (repositioning accuracy 0.02 mm), and integrates the ball calibration pixel color coordinates (ΔE < 3).

[0015] Example 2 (Software and Interaction Process) 1. Driving data collection: collect the operation data of 1000 drivers under 20 road conditions, and extract the features through convolution attention network; 2. Real-time decision example: Input: the radar detects that the rear car is 3.2m away and the acceleration is >0.5g; Processing: AI determines it as "high risk approach", and the dynamic space encoder activates the central warning area; Output: display red flashing frame + text "rear car approaching fast!", and send warning to the rear car through V2X at the same time.

[0016] 3. Vehicle-road cooperation example: detect the approach of an ambulance (within 500m), the system receives the traffic control instruction, the rear window displays "ambulance giving way→", and the front 5 vehicles are displayed in turn to form a virtual channel.

[0017] Technical effects Display performance: transmittance >82%, power consumption reduced by 40% compared with traditional scheme (12W in full brightness mode), response time <5ms; Safety improvement: rear-end accident rate reduced by 35%, high-speed rear-end warning time advanced by 2.3 seconds (NHTSA standard); Traffic efficiency: road traffic efficiency is improved by 22%, and the traffic efficiency of the merging area is improved by 15% (due to the reduction of forced overtaking); Extensibility: support OTA upgrade (dialect recognition, semantic library update), compatible with SAE J3067 standard and smart city traffic brain.

[0018] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the description.

[0019] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An intelligent rear window interaction system for automobiles based on AI and vehicle-road collaboration, characterized in that: include: S1, transparent Micro-LED display layer, using laser micromachining to form 50μm deep micro-grooves on the glass substrate, with chip transfer accuracy of ±1.5μm and transmittance >82%; S2, multimodal input module, integrating voice recognition, vehicle body CAN bus signal and V2X communication interface; S3, the AI ​​processing unit, performs driving data prediction based on the LSTM model, generates cultural adaptability information based on real-time traffic big data, and optimizes display strategies using reinforcement learning. The reward function is R = α・Safety + β・Efficiency + γ・User_Satisfaction; S4, dynamic spatial encoder, automatically adjusts the display area and content priority according to the distance to the following vehicle.

2. The information generation method of the automobile intelligent rear window interaction system based on AI and vehicle-road collaboration as claimed in claim 1, characterized in that: The following steps are involved: S1. Analyze historical driving data through the LSTM model to predict the braking prompt advance time; S2. Combine real-time traffic big data to generate adaptive prompt text; S4. Use reinforcement learning framework to optimize display strategy and dynamically adjust display content, format and duration according to weather and traffic density.

3. The V2X communication method of the automobile intelligent rear window interaction system based on AI and vehicle-road collaboration as claimed in claim 1, characterized in that: include: S1. The message packet contains the emotion tag; S2, implements an anonymous MAC address rotation mechanism and supports DSRC and 5G dual-mode communications; S4: Can forward road anomaly information within 5km ahead.

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