A method and system for comprehensive assessment of driving risk integrating independent climate fields

Abstract

This invention proposes a method for assessing driving risks by integrating an independent climate field. The method mainly consists of three parts: First, based on a three-field model of kinetic energy field, potential energy field, and behavioral field in human-vehicle-road collaboration, an independent climate field model is innovatively defined and constructed to characterize the additional driving risks caused by abrupt changes in the environmental medium. Then, by acquiring environmental information such as road surface adhesion coefficient and rainfall intensity, the risk equivalent mass of the climate field source is calculated, and the field strength distribution and attenuation function of the climate field are established based on spatial distance. Finally, the climate field is vector-superimposed with the original three fields to calculate the total driving risk field strength across the entire region. Under the premise of ensuring driving safety, the method achieves advanced warning and risk avoidance decisions for potential accident hazards based on the dynamic change trend of the total field strength. This invention does not change the internal structure of the original three-field model, but supplements the influence of meteorological and road factors on risk characterization through the principle of physical field superposition, effectively improving the safety of intelligent vehicles in complex and extreme environments.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and active vehicle safety control, and relates to a method for quantitative characterization of driving risks in complex weather conditions by integrating independent climate fields based on physical field theory and non-conservative mechanical constraints. Background Technology

[0002] With the rapid development of Intelligent Transportation Systems (ITS) and connected autonomous driving technologies, driving risk assessment models have become a core component in ensuring vehicle active safety and decision-making. Especially in urban roads and complex operating conditions, accurately quantifying potential threats during vehicle operation is a current research hotspot in traffic engineering and vehicle control.

[0003] Currently, the academic community, both domestically and internationally, primarily characterizes driving risks by constructing physical field models. These methods abstract the complex interactions of traffic participants into physical energy fields, using the spatial distribution of field strength to represent risk. The currently accepted comprehensive model is a unified three-field model based on the "human-vehicle-road" collaboration: the kinetic energy field determined by the moving target, the potential energy field determined by the geometric characteristics of the road environment, and the behavioral field determined by the individual behavioral characteristics of the driver.

[0004] The US PATH project (Partners for Advanced Transit and Highways) has explored safety constraints in vehicle platooning, demonstrating that cooperative perception can improve lane throughput. Meanwhile, in China, Professor Wang Jianqiang's team at Tsinghua University systematically proposed the "driving risk field" theory, defining the kinetic energy field. The potential energy field depends on the equivalent mass and state of motion of the target object. This depends on road boundary constraints. This theory successfully transforms complex relationships between traffic participants into computable, superimposed physical vectors, providing an important reference for path planning in autonomous driving.

[0005] However, in-depth research on existing driving risk field theories has revealed the following limitations that urgently need to be addressed when dealing with extreme environmental conditions. These limitations directly restrict the reliability of active safety control systems on variable friction-constrained road surfaces:

[0006] 1. Limitations of the dimensions used to define risk sources

[0007] Existing models primarily attribute vehicle risk to kinetic energy (speed and physical mass). However, in real-world conditions, such as low-adhesion surfaces caused by rain or oil spills, the loss of vehicle safety margin essentially stems from the degradation of braking performance. Current theories lack a mechanism to map "non-physical mass" environmental degradation (loss of road adhesion) into "risk equivalent energy." This prevents models from quantifying the mechanical nature of the environmental medium's suppression of vehicle handling limits, resulting in a lack of quantitative basis for risk assessment during sudden environmental changes.

[0008] 2. Limitations of Spatial Impact Characterization

[0009] Existing technologies often characterize environmental impacts in a "global" manner (i.e., modifying parameters across the entire road segment), lacking spatial modeling of "localized abrupt risks." For example, when water or oil accumulation exists only in localized areas of the road surface, existing field models cannot generate a radiation field with clearly defined physical attenuation characteristics at the risk source center. This prevents the system from achieving "feedforward perception" through the spatial distribution of risk gradients; vehicles must enter the damaged area to perceive the risk, thus losing the early warning window.

[0010] 3. Limitations of the superposition logic in field theory

[0011] Existing models treat environmental factors as "correction coefficients" for kinetic or potential energy fields. This approach logically couples the properties of the medium with the state of the target object. When multiple traffic participants are simultaneously in a complex climate zone, the computational complexity of the coupling between these fields increases exponentially, and the physical meaning becomes ambiguous. The lack of a "fourth field"—an independent climate field—that exists outside the geometric boundaries of people, vehicles, and roads prevents the model from maintaining logical simplicity and the linearity of superposition in multiphysics environments.

[0012] Therefore, this invention, while maintaining compatibility with the existing three-field theory, proposes a comprehensive vehicle risk assessment method integrating an independent climate field. This is achieved by introducing a climate risk equivalent based on the energy dissipation equivalence principle. And the climate field intensity distribution function based on Gauss's theorem. This invention enables the direct mapping of non-geometric environmental factors, such as abrupt changes in road surface adhesion characteristics, to spatial risk potential. The aim of this invention is to fill the theoretical gaps in existing models under non-conservative force constraints, providing dynamically consistent early warning support for autonomous driving in complex environments. Summary of the Invention

[0013] This invention aims to quantify the suppressive effect of environmental media on vehicle handling limits as a spatial risk potential by constructing an independent climate field. The specific implementation steps are as follows:

[0014] Step 1: Construct the climate field intensity distribution function :

[0015] This invention abstracts regions of localized environmental degradation as "dispersion sources" of risk energy. Based on the gravitational decay model in field theory, the formula for climate field intensity distribution is defined as follows:

[0016]

[0017] in, The gravitational constant of the climate field, Let r be the climate risk equivalent mass, and r be the distance from the measuring point to the center of the risk source. This is the spatial decay index. This design ensures the smooth evolution of risk perception in physical space.

[0018] Step 2: Deriving the climate risk equivalent (Based on the principle of energy equivalence)

[0019] To quantify the degree of environmental degradation, this invention maps the loss of road surface adhesion performance to risk equivalent mass. :

[0020]

[0021] in, To estimate the adhesion coefficient in real time, Environmental impact weighting factor This is the baseline value for dry road surfaces. When When reduced to extreme operating conditions, A sharp increase indicates that the vehicle has completely lost its braking ability;

[0022] Step 3: Calculation of global risk vector overlay:

[0023] Independent climate fields With the original kinetic energy field Potential energy field and behavioral field By performing vector superposition, the total driving risk field strength over the entire area is obtained. :

[0024]

[0025] This indicator can reflect the nonlinear risk gain caused by oil stains and water accumulation on the road surface in real time. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the global risk assessment process of the method of this invention. Detailed Implementation

[0028] The integrated independent climate field-based vehicle risk assessment system provided by this invention consists of an environmental perception module, a risk calculation module, and a decision control module. The specific implementation steps are as follows:

[0029] Step 1: Perception of road surface environmental characteristics and estimation of adhesion limit:

[0030] After the system is started, it uses an on-board sensing array (including a vision sensor, a rain sensor, and a wheel speed sensor) to monitor the condition of the surrounding environment in real time.

[0031] (1) Obtain the reference adhesion coefficient under ideal dry conditions based on the static channelization information of the intersection or road segment. (Usually set to 0.85);

[0032] (2) For sudden media degradation (such as water accumulation and oil spills), the vehicle yaw moment response deviation or drive wheel slip rate variation is used for inverse estimation to obtain the real-time estimated adhesion coefficient of the damaged area. For example, when oil spills were detected on the road surface, the measured... ;

[0033] Step 2: Constructing climate risk equivalents based on the principle of energy dissipation equivalence:

[0034] This invention maps the degree of medium degradation, which cannot be directly quantified, into a "generalized mass" of risk field sources with dynamic significance through the principle of energy equivalence. .

[0035] (1) According to the friction circle theory of vehicle dynamics, the decrease in road surface adhesion characteristics directly reduces the maximum longitudinal and lateral combined force that the tire can provide, resulting in the decay of the system's kinetic energy dissipation efficiency.

[0036] (2) Calculate the climate risk equivalent mass using the following formula. :

[0037] .

[0038] This step enables a linear mapping of non-physical meteorological factors to the risk energy scale, solving the technical problem of the difficulty in quantifying medium constraints mentioned in the background technology.

[0039] Step 3: Construct a spatial distribution model of climate field intensity based on field theory continuity:

[0040] To ensure the second-order differentiability and spatial continuity of risk assessment across the entire domain, this invention abstracts local media abrupt change regions as dissipative sources of risk energy.

[0041] Following the spatial gravitational attenuation model in field theory, the climate field strength distribution at a distance r from the risk center is calculated:

[0042]

[0043] in, The gravitational constant of the climate field, This is a spatial attenuation characteristic index. Through this model, climate risk attenuates radially outwards, allowing the system to sense potential risks through field strength gradients even before the vehicle physically contacts the damaged area.

[0044] Step 4: Overlaying of all risks and factors across the entire domain and feedforward-based risk hedging decision-making:

[0045] The system will calculate independent climate fields With the original kinetic energy field Potential energy field and behavioral field Perform vector coupling superposition.

[0046] (1) Calculate the total driving risk field strength across the entire area. :

[0047]

[0048] (2) The decision-making level bases decisions on the speeds of adjacent vehicles. and vehicle distance Combined with total risk field strength Define the danger index :

[0049] when and At that time, the vehicle was determined to be in an extremely dangerous state, and [the following was taken]. 1;

[0050] when and At that time, the vehicle was determined to be in a slightly dangerous state, and [the following was taken]. ;

[0051] when At that time, it was determined that the vehicle was in a safe condition, and [the vehicle was] taken [from a safe location]. .

[0052] (3) According to the joint feedback, if the total risk field strength exceeds the safety threshold, the decision control module will output control instructions in advance (such as active speed limit or path replanning) to achieve feedforward risk avoidance control for climate risks.

[0053] The above steps describe the implementation process of the present invention in detail, but the present invention is not limited to the specific details of the above embodiments. Anything within the scope of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A driving risk assessment method integrating independent climate fields, characterized in that, The method includes the following steps: Step one: build a basic driving risk field model, which includes kinetic field determined by moving targets , potential field determined by road environment factors , and behavior field determined by individual characteristics of drivers ; Step 2: Define an independent climate field , used to characterize the additional driving risks caused by environmental media; Step 3: Acquire real-time vehicle driving environment information through onboard sensors, including real-time estimated road surface adhesion coefficient. Rainfall intensity and characteristics of foreign objects on the road surface; Step 4: Calculate the field strength distribution of the climate field based on the environmental information; Step 5: Transfer the climate field With kinetic energy field Potential energy field Behavioral field Perform vector superposition to calculate the total driving risk field strength over the entire area. : Step Six: Based on the total risk field strength of the entire driving area Based on real-time data, it can perform driving risk warnings or proactive risk avoidance controls.

2. The method for assessing driving risks by integrating independent climate fields according to claim 1, characterized in that: Step two involves constructing a climate field model that follows the vector superposition principle and potential function theory in physical field theory. The climate field... The formula for the electric field distribution is: in, The gravitational constant of the climate field, Let r be the climate risk equivalent mass, and r be the distance from the target point to the center of the risk source. This is the spatial decay index.

3. The method for assessing driving risks by integrating independent climate fields according to claim 2, characterized in that: Step four, calculating the field strength distribution of the climate field, follows the flux analysis logic of Gauss's law. The climate risk equivalent mass... The calculation method is as follows: in, The road surface adhesion coefficient is estimated in real time. The maximum coefficient of adhesion for an ideal dry road surface. This is a preset environmental impact weighting factor.

4. The method for assessing driving risks by integrating independent climate fields according to claim 1, characterized in that: In step three, the road surface adhesion coefficient is estimated in real time. The methods for obtaining this information include inverse estimation using the vehicle yaw moment response deviation, or calculation using empirical formulas for road surface water film thickness.

5. The method for assessing driving risks by integrating independent climate fields according to claim 1, characterized in that: The total risk field strength of the entire driving area in step five It can reflect the nonlinear gain of risk caused by oil spills, water accumulation, or ice formation on the road surface.

6. A driving risk assessment system applying the method described in claims 1 to 5, characterized in that, The system includes: The environmental sensing module is used to sense the road surface adhesion status and meteorological parameters in real time. The risk calculation module is used to construct and overlay kinetic energy fields, potential energy fields, behavioral fields, and independent climate fields. The decision control module is used to output control commands based on the total risk field strength of the entire driving area.

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