An intelligent driving system for dangerous goods transportation

Abstract

The utility model relates to the field of automobile manufacturing, concretely relates to an intelligent driving system for dangerous goods transportation. The system includes driver state monitoring module, environment perception and dynamic response module, vehicle performance analysis module and data processing module, data processing module is connected with driver state monitoring module, environment perception and dynamic response module, vehicle performance analysis module electricity respectively, the utility model is based on data processing module and can cross -check binocular camera and radar data, reduces the misjudgment rate, through high -precision camera real -time capture driver facial feature, analysis obtains driver fatigue grade, establishes fatigue -control Li stable mechanism, and through vehicle performance analysis module, based on steering / brake performance recession real -time prediction model carries out accurate control to the vehicle.

Description

Technical Field

[0001] This utility model relates to the field of automobile manufacturing, and specifically to an intelligent driving system for the transportation of dangerous goods. Background Technology

[0002] Hazardous materials transport accidents are highly dangerous; for example, the Tianjin Port explosion resulted in 165 deaths, and the Hangjinqi accident caused 10 deaths. Current regulations (such as the Production Safety Law) emphasize "prevention first," but traditional technologies rely on manual monitoring and single sensors, which cannot meet the needs of dynamic risk management. Currently, accidents involving hazardous materials transport vehicles are mainly caused by factors such as driver fatigue, extreme weather, vehicle mechanical failure, and external collisions. Traditional driving modes rely on human experience and periodic maintenance, but fatigue is difficult to monitor in a timely manner, sudden performance degradation of vehicles is difficult to predict, and active deceleration in extreme weather lacks a dynamic response mechanism. While existing technologies include driver assistance systems based on single sensors (such as cameras or radar), they do not integrate multi-dimensional data to achieve comprehensive management of the driver, environment, and vehicle performance. Existing intelligent driving systems often focus on single functions (such as lane keeping or collision warning) and lack multi-factor collaborative control capabilities: single fatigue monitoring systems identify the frequency of eye closure through cameras but are not linked to vehicle control. Environmental perception module: Uses a monocular camera or a single radar, with limited perception range (e.g., unable to identify side obstacles or rainy road conditions). Vehicle performance diagnostics: Based on periodic data readings from the OBD (On-Board Diagnostics) system, it cannot provide real-time warnings of sudden performance changes. A single performance change (e.g., a sudden drop in brake pressure) or cumulative performance degradation (e.g., a continuous decrease in steering assist) triggers a maintenance reminder and limits the vehicle's maximum speed. Utility Model Content

[0003] To address the aforementioned problems, this utility model proposes an intelligent driving system for the transportation of hazardous materials. Through driver status monitoring, environmental perception, vehicle performance analysis, and actuator coordinated control, it reduces the risk of transportation accidents. The specific technical solution is as follows:

[0004] An intelligent driving system for transporting hazardous materials includes a driver status monitoring module, an environmental perception and dynamic response module, a vehicle performance analysis module, and a data processing module; the data processing module is electrically connected to the driver status monitoring module, the environmental perception and dynamic response module, and the vehicle performance analysis module, respectively.

[0005] The driver status monitoring module includes a high-precision camera that captures the driver's facial features in real time; the environmental perception and dynamic response module includes a rain sensor, a front-facing binocular camera, and a millimeter-wave radar detector, the rain sensor monitors weather changes, the front-facing binocular camera identifies obstacles within 200 meters, and the millimeter-wave radar detector detects the risk of side collisions; the vehicle performance analysis module collects steering, braking, and acceleration data in real time.

[0006] The data processing module includes a data and dynamic decision-making unit and a control unit. The data and dynamic decision-making unit performs decision analysis based on the input information, and the control unit issues corresponding control commands to the vehicle bus based on the decision analysis.

[0007] Furthermore, the control commands include audible and visual alarms, forced stop commands, speed reduction commands, and side avoidance commands.

[0008] Furthermore, the high-precision camera has night vision capabilities, an IP66 or higher protection rating, and a resolution of 4 megapixels or higher.

[0009] Furthermore, both the rain sensor and the front-facing binocular camera are mounted on the upper inner side of the windshield, behind the rearview mirror.

[0010] Furthermore, the millimeter-wave radar detector is installed at four locations on the front, rear, left, and right sides of the vehicle body.

[0011] This invention utilizes a data processing module to cross-validate data from binocular cameras and radar, reducing the false judgment rate; it captures driver facial features in real time using a high-precision camera, analyzes and obtains driver fatigue levels, and establishes a fatigue-control stability mechanism; and it uses a vehicle performance analysis module to precisely control the vehicle based on a real-time prediction model of steering / braking performance degradation. Attached Figure Description

[0012] Figure 1 A schematic diagram of the system structure of this utility model;

[0013] Figure 2 The system processing flowchart of this utility model. Detailed Implementation

[0014] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present utility model.

[0015] like Figure 1 As shown, an intelligent driving system for transporting hazardous materials includes a driver status monitoring module, an environmental perception and dynamic response module, a vehicle performance analysis module, and a data processing module; the data processing module is electrically connected to the driver status monitoring module, the environmental perception and dynamic response module, and the vehicle performance analysis module, respectively.

[0016] The driver status monitoring module includes a high-precision camera that captures the driver's facial features in real time; the environmental perception and dynamic response module includes a rain sensor, a front-facing binocular camera, and a millimeter-wave radar detector, the rain sensor monitors weather changes, the front-facing binocular camera identifies obstacles within 200 meters, and the millimeter-wave radar detector detects the risk of side collisions; the vehicle performance analysis module collects steering, braking, and acceleration data in real time.

[0017] The data processing module includes a data and dynamic decision-making unit and a control unit. The data and dynamic decision-making unit performs decision analysis based on the input information, and the control unit issues corresponding control commands to the vehicle bus based on the decision analysis.

[0018] Control commands include audible and visual alarms, forced stop commands, speed reduction commands, and side avoidance commands.

[0019] The high-precision camera has night vision capabilities, an IP66 or higher protection rating, and a resolution of 4 megapixels or higher.

[0020] The rain sensor and the front-facing binocular camera are both mounted on the upper inner side of the windshield, behind the rearview mirror.

[0021] The millimeter-wave radar detector is installed at four locations on the front, rear, left, and right sides of the vehicle body.

[0022] like Figure 2 As shown, this utility model is based on an intelligent driving system, which collects data such as driver fatigue information, weather information, and road information, integrates and processes multiple data in a data processing template, makes dynamic decisions, and issues control commands or alarm commands to the vehicle based on the decisions.

[0023] The preferred embodiments of this patent have been described in detail above. However, this patent is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this patent.

Claims

1. An intelligent driving system for transporting dangerous goods, characterized in that: The system includes a driver status monitoring module, an environmental perception and dynamic response module, a vehicle performance analysis module, and a data processing module; the data processing module is electrically connected to the driver status monitoring module, the environmental perception and dynamic response module, and the vehicle performance analysis module, respectively. The driver status monitoring module includes a high-precision camera that captures the driver's facial features in real time. The environmental perception and dynamic response module includes a rain sensor, a front-facing binocular camera, and a millimeter-wave radar detector. The rain sensor monitors weather changes, the front-facing binocular camera identifies obstacles within 200 meters, and the millimeter-wave radar detector detects the risk of side collisions. The vehicle performance analysis module collects steering, braking, and acceleration data in real time. The data processing module includes a data and dynamic decision-making unit and a control unit. The data and dynamic decision-making unit performs decision analysis based on the input information, and the control unit issues corresponding control commands to the vehicle bus based on the decision analysis.

2. The intelligent driving system for transporting dangerous goods according to claim 1, characterized in that: Control commands include audible and visual alarms, forced stop commands, speed reduction commands, and side avoidance commands.

3. The intelligent driving system for transporting dangerous goods according to claim 1, characterized in that: The high-precision camera has night vision capabilities, an IP66 or higher protection rating, and a resolution of 4 megapixels or higher.

4. The intelligent driving system for transporting dangerous goods according to claim 1, characterized in that: The rain sensor and the front-facing binocular camera are both mounted on the upper inner side of the windshield, behind the rearview mirror.

5. The intelligent driving system for transporting dangerous goods according to claim 1, characterized in that: The millimeter-wave radar detector is installed at four locations on the front, rear, left, and right sides of the vehicle body.

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