Artificial Intelligence-Based Vehicle Collision Warning Method and System

Abstract

The present invention provides an artificial intelligence-based vehicle collision warning method and system. First, a multi-source sensor data set of a target vehicle is obtained, including a sequence of vehicle operating status, environmental perception, and driving behavior data, each sequence consisting of standardized monitoring data. Then, dynamic spatiotemporal feature extraction is performed on the multi-source sensor data set to generate dynamic spatiotemporal coding features including features such as vehicle motion trends. Then, a pre-trained collision risk prediction model is called to perform a multi-dimensional risk association analysis to obtain a risk prediction result including a longitudinal collision risk level. Then, based on the risk prediction result, a real-time warning strategy set is constructed, including warning signal generation rules for different risk levels and a vehicle control instruction sequence. Finally, this strategy set is deployed to an on-board decision-making system, a control instruction mapping relationship with a vehicle actuator is established, and real-time risk intervention operations are triggered to achieve effective collision warning and risk intervention.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based vehicle collision warning method and system. Background Art

[0002] In the field of vehicle safety technology and auto insurance risk control, with the continuous growth of car ownership and the increasing complexity of the road traffic environment, vehicle collision accidents have become one of the main factors threatening road traffic safety and increasing the risk of auto insurance claims. Traditional vehicle collision warning systems mostly rely on single sensor data or simple threshold judgments, such as distance monitoring based solely on radar or camera data, or setting fixed collision warning thresholds based on vehicle speed and distance. Although such methods can provide warning functions to a certain extent, they have significant limitations: on the one hand, a single data source cannot fully reflect the dynamic changes in the vehicle's driving status and the surrounding environment, resulting in limited warning accuracy; on the other hand, fixed threshold judgments cannot adapt to different driving scenarios and differences in driver behavior, and are prone to false positives or missed positives, affecting the effectiveness of the warning and user experience.

[0003] Furthermore, relevant technologies often lack comprehensive consideration of multi-dimensional information, including vehicle motion trends, environmental interactions, and abnormal driving behavior, when addressing vehicle collision risks. For example, there is a lack of effective fusion analysis between vehicle operating status data (such as speed and acceleration) and environmental perception data (such as obstacle locations and road conditions), making it difficult to accurately predict potential collision risks. Furthermore, insufficient attention is paid to the impact of driver behavior patterns (such as sudden acceleration, braking, and frequent lane changes) on collision risk, making it difficult for warning systems to identify and intervene in high-risk driving behaviors in advance.

[0004] In terms of auto insurance risk control, existing technologies mainly rely on historical accident data and static risk assessment models, which are difficult to reflect the dynamic risk changes during vehicle driving in real time. This lagging risk assessment method not only cannot effectively prevent the occurrence of accidents, but may also lead to unreasonable auto insurance pricing, increased compensation risks and other problems. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a vehicle collision warning method based on artificial intelligence, the method comprising:

[0006] Acquire a multi-source sensor data set of a target vehicle, wherein the multi-source sensor data set includes a vehicle operating status data sequence, an environment perception data sequence, and a driving behavior data sequence, each data sequence consisting of standardized monitoring data of multiple continuous time windows;

[0007] Performing a dynamic spatiotemporal feature extraction operation on the multi-source sensor data set to generate dynamic spatiotemporal coding features for each time window, the dynamic spatiotemporal coding features including vehicle motion trend features, environmental interaction features, and behavior abnormality features;

[0008] Calling a pre-trained collision risk prediction model to perform multi-dimensional risk association analysis on the dynamic spatiotemporal coding features to generate a risk prediction result for the current time window, the risk prediction result including the longitudinal collision risk level, the lateral departure risk level, and the emergency braking trigger probability;

[0009] Building a real-time warning strategy set based on the risk prediction results, the real-time warning strategy set including warning signal generation rules and vehicle control instruction sequences corresponding to different risk levels;

[0010] The real-time warning strategy set is deployed to the vehicle decision-making system, and a control instruction mapping relationship with the vehicle actuator is established to trigger real-time risk intervention operations.

[0011] On the other hand, an embodiment of the present invention also provides an artificial intelligence-based vehicle collision warning system, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0012] Based on the above aspects, the embodiment of the present invention integrates multi-source sensor data sets such as vehicle operating status, environmental perception and driving behavior. On this basis, the dynamic spatiotemporal feature extraction operation not only captures key information such as vehicle movement trends and environmental interactions, but also introduces behavioral abnormality characteristics, effectively identifying potential human driving risks and enhancing the foresight and accuracy of risk identification. Furthermore, the pre-trained collision risk prediction model converts dynamic spatiotemporal coding features into risk prediction results through multi-dimensional risk association analysis, including longitudinal collision risk level, lateral deviation risk level and emergency braking trigger probability, realizing quantitative assessment and graded warning of collision risk, and providing scientific and quantitative decision-making basis for vehicle risk control. The real-time warning strategy set constructed based on the risk prediction results dynamically generates warning signals and control instructions according to the risk level, ensuring the timeliness of warnings and the effectiveness of intervention, and effectively reducing the probability of accidents and the degree of losses. Finally, the real-time warning strategy set is deployed to the on-board decision-making system, and a control command mapping relationship with the vehicle's actuator is established, realizing the connection from risk warning to active intervention, forming an active protection network for safe vehicle driving. It not only improves the safety of vehicle driving, reduces the risk of auto insurance claims, but also realizes intelligent and precise risk control measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the execution flow of the vehicle collision warning method based on artificial intelligence provided by an embodiment of the present invention.

[0014] Figure 2 Schematic diagram of exemplary hardware and software components of an artificial intelligence-based vehicle collision warning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of an artificial intelligence-based vehicle collision warning method provided by an embodiment of the present invention. The artificial intelligence-based vehicle collision warning method is introduced in detail below.

[0016] Step S110 , obtaining a multi-source sensor data set of the target vehicle, wherein the multi-source sensor data set includes a vehicle operating status data sequence, an environment perception data sequence, and a driving behavior data sequence, and each data sequence is composed of standardized monitoring data of multiple continuous time windows.

[0017] In this example, a target vehicle equipped with various sensors and intelligent systems is traveling on a road in an intelligent traffic environment. The road conditions are complex, with moderate traffic volume, various obstacles, and different road signs. The target vehicle is designed to use its onboard intelligent systems to monitor and analyze various data in real time to prevent potential collision risks and ensure driving safety.

[0018] For example, the target vehicle is equipped with multiple types of sensors to collect data from various aspects. Regarding the vehicle's operating status data sequence, the longitudinal acceleration sensor continuously collects longitudinal acceleration data, recording values every 0.1 seconds. For example, the longitudinal acceleration data sequence recorded over a certain period of time might be [1.2, 1.5, 1.3, 1.4, 1.6] (unit: m / s²). The lateral angular velocity sensor simultaneously collects lateral angular velocity data, also at 0.1-second intervals, producing a data sequence such as [0.2, 0.3, 0.25, 0.32, 0.28] (unit: rad / s²). The brake pressure sensor records brake pressure data, producing a brake pressure data sequence such as [20, 22, 21, 23, 22] (unit: kPa). After the data are collected, they can be processed according to the set normalization rules. For example, the longitudinal acceleration data can be mapped to the interval [0, 1] by subtracting the minimum value in the data sequence from each data and then dividing it by the difference between the maximum and minimum values. Assuming that the minimum longitudinal acceleration is 1.2 and the maximum is 1.6, the first data 1.2 is normalized to (1.2-1.2) / (1.6-1.2)=0, and 1.6 is normalized to (1.6-1.2) / (1.6-1.2)=1. The entire sequence is normalized in this way.

[0019] To acquire environmental perception data sequences, sensors such as millimeter-wave radars and cameras installed around the vehicle acquire real-time relative position data of obstacles around the target vehicle. The millimeter-wave radar measures the relative position of the obstacle and the vehicle every 0.2 seconds, generating a relative position data sequence. For example, the relative position data for an obstacle at several time points is [(20, 5), (18, 6), (16, 7), (14, 8)] (unit: meters, representing the lateral and longitudinal distances, respectively). Simultaneously, the lane recognition camera monitors the lane lines and collects a lane curvature data sequence, such as [0.01, 0.012, 0.011, 0.013, 0.012]. The ambient light sensor collects ambient light intensity data at 1-second intervals, such as [500, 520, 510, 530, 525] (unit: lux). The above data are also normalized. For example, for relative position data, the horizontal and vertical distances are mapped to the interval [0, 1] respectively, and processed by a method similar to the above-mentioned normalization of longitudinal acceleration data.

[0020] Driving behavior data sequences are collected using a steering wheel angle sensor, pedal position sensor, and driver gaze monitoring equipment. The steering wheel angle sensor records steering wheel angle changes in real time, generating an angle change data sequence at 0.1-second intervals, such as [10, 12, 11, 13, 12] (unit: degrees). The pedal position sensor monitors pedal position switching, generating a pedal position switch data sequence that records the duration of each pedal switch, such as [0.5, 1.2, 1.8, 2.5, 3.0] (unit: seconds, representing the time interval from the last switch to the current switch). The driver gaze monitoring equipment tracks the driver's gaze direction using a camera, generating a gaze direction data sequence that records the duration of each time the gaze deviates from the vehicle's front and the amount of lateral displacement of the vehicle, such as [(3, 0.1), (4, 0.12), (3.5, 0.11), (4.5, 0.13), (4, 0.12)] (unit: seconds and meters). The above data is also normalized. For example, the cornering data is mapped to the interval [0, 1]. This approach ultimately yields a multi-source sensor data set consisting of vehicle operating status data, environmental perception data, and driving behavior data. Each data sequence consists of standardized monitoring data from multiple consecutive time windows.

[0021] Step S120 , performing a dynamic spatiotemporal feature extraction operation on the multi-source sensor data set to generate dynamic spatiotemporal coding features for each time window, wherein the dynamic spatiotemporal coding features include vehicle motion trend features, environmental interaction features, and behavior abnormality features.

[0022] In this embodiment, for the vehicle operation status data sequence, the longitudinal acceleration data sequence, the lateral angular velocity data sequence and the brake pressure data sequence are first collected. For the longitudinal acceleration data sequence, a first time convolution network is constructed. The first time convolution network includes alternating dilated convolution layers and normalization layers. The dilation coefficient of the dilated convolution layer is set to 2 to capture multi-scale acceleration pattern features. For example, by performing a convolution operation on the longitudinal acceleration data through the dilated convolution layer, some periodic or trend features in the data can be discovered. Assume that after processing through the dilated convolution layer, some characteristic values such as [0.3, 0.4, 0.35, 0.42, 0.38] are obtained, and then the above characteristic values are mapped to a suitable range, such as the [0, 1] interval, through the normalization layer to obtain the acceleration time series features.

[0023] A second temporal convolutional network is constructed for the lateral angular velocity data sequence, consisting of a bidirectional convolutional layer and a gated activation unit. The bidirectional convolutional layer simultaneously convolves the data in both the forward and reverse directions, better extracting the temporal dependence of steering operations. For example, by processing the lateral angular velocity data sequence through the bidirectional convolutional layer, the order and changing patterns of steering operations can be discovered. The gated activation unit selects and activates the data based on its importance, and after processing, the steering temporal characteristics are obtained.

[0024] For the brake pressure data sequence, a third temporal convolutional network is constructed, consisting of a residual convolution module and an attention pooling layer. The residual convolution module retains key information in the data, preventing information loss during the convolution process. The attention pooling layer focuses on important features of braking behavior, identifying sudden changes in braking behavior and generating braking time series features.

[0025] The acceleration time series characteristics are differentially calculated to obtain the acceleration change rate characteristics. For example, if the acceleration time series characteristics are [0.3, 0.4, 0.35, 0.42, 0.38], then the acceleration change rate characteristics are calculated as [(0.4-0.3), (0.35-0.4), (0.42-0.35), (0.38-0.42)] = [0.1, -0.05, 0.07, -0.04]. The steering angular velocity fluctuation feature is obtained by performing a sliding window variance calculation on the steering time series features. Assuming the sliding window size is 3 and the steering time series features are [0.2, 0.3, 0.25, 0.32, 0.28], the variance of the first sliding window [0.2, 0.3, 0.25] is calculated as [(0.2-(0.2+0.3+0.25) / 3)²+(0.3-(0.2+0.3+0.25) / 3)²+(0.25-(0.2+0.3+0.25) / 3)²] / 3. Similarly, the variance of the entire sequence is calculated to obtain the steering angular velocity fluctuation feature. A gradient analysis is performed on the braking time series features to obtain the brake pressure gradient feature. Thus, by splicing the above features, the vehicle motion trend feature is obtained.

[0026] For the environmental perception data sequence, the relative position data sequence of obstacles around the target vehicle, the lane curvature data sequence, and the ambient light intensity data sequence are obtained. An obstacle trajectory prediction model is constructed, and the obstacle's motion state vector is initialized, assuming it contains 20 lateral position observations (unit: meters), 10 longitudinal velocity observations (unit: m / s²), and an acceleration estimate of 0.5 (unit: m / s²). A state transfer matrix and an observation noise covariance matrix are generated based on the vehicle coordinate system. The state transfer matrix is determined based on vehicle kinematics principles. The motion state vector is iteratively predicted using the state transfer matrix, assuming a time step of 0.2 seconds. After one iterative prediction, a predicted state vector and the corresponding prediction covariance matrix are output. The residual between the predicted state vector and the actual observation in the current time window is calculated. For example, if the predicted lateral position is 18 meters and the actual observation is 17 meters, the residual is 1 meter. The Mahalanobis distance of the residual vector is calculated using the prediction covariance matrix. When the Mahalanobis distance exceeds the dynamically adjusted threshold (assuming the threshold is 0.8 and the actual calculated Mahalanobis distance is 0.9), the noise parameters of the observation noise covariance matrix are corrected by a scaling factor of 1.2 to generate an updated observation noise covariance matrix. The Kalman filter's measurement update step is re-executed using the updated observation noise covariance matrix, outputting a revised predicted state vector and lateral displacement prediction error signature. This revised predicted state vector is input into the trajectory pattern matching network, where it undergoes a convolutional similarity comparison with typical motion patterns in the historical trajectory database to generate a longitudinal velocity covariance signature. The lateral displacement prediction error signature and longitudinal velocity covariance signature are then integrated to generate the obstacle's relative motion trajectory signature.

[0027] A pre-trained lane feature encoder is used to extract multi-scale features from the lane curvature data sequence, generating a lane curvature matching feature, which contains the dynamic matching coefficient between the current lane curvature and the vehicle's steering angle. A sliding window statistical process is performed on the ambient light intensity data sequence, assuming a sliding window size of 3, to generate a visibility impact coefficient feature, which contains the weight parameter associated with the light intensity change rate and obstacle recognition confidence. These features are then fused through spatial position encoding to generate environmental interaction features.

[0028] For driving behavior data, data on steering wheel angle changes, pedal position switching, and driver gaze direction are collected. The steering angle change data is pre-processed using a wavelet threshold denoising algorithm to remove high-frequency noise components, resulting in a denoised steering angle signal. The denoised steering angle signal is then subjected to a short-time Fourier transform (SFT) to generate a time-frequency energy distribution matrix. Within a set frequency range (assuming the frequency range is [10-20 Hz]) in the time-frequency energy distribution matrix, energy peak features are extracted. Based on the energy peak features, a parameter representing the ratio of the energy in this set frequency range to the total energy is calculated. A sliding average of these parameters over five consecutive time windows is applied to generate a steering wheel operation frequency feature. Event interval analysis is performed on the pedal position switching data series to generate a pedal switching interval feature, which contains the distribution of time intervals between alternating accelerator and brake pedal operations. Sustained deviation detection is performed on the gaze direction data series to generate a gaze deviation duration feature, which contains the correlation coefficient between the duration of a single gaze deviation event and the vehicle's lateral position offset. The above features are then normalized and concatenated to generate a behavioral abnormality feature.

[0029] Finally, the vehicle motion trend features, environmental interaction features and behavior abnormality features are temporally aligned and fused to generate dynamic spatiotemporal coding features.

[0030] Step S130 , calling a pre-trained collision risk prediction model to perform multi-dimensional risk association analysis on the dynamic spatiotemporal coding features to generate a risk prediction result for the current time window, wherein the risk prediction result includes the longitudinal collision risk level, the lateral departure risk level, and the emergency braking trigger probability.

[0031] In this embodiment, the dynamic spatiotemporal coding features can be input into the spatiotemporal attention module of the collision risk prediction model, and the dynamic spatiotemporal coding features of the current time window and the dynamic spatiotemporal coding features of the historical time window are input into the linear mapping layer of the spatiotemporal attention module to generate the current query feature vector, the historical key feature vector and the historical value feature vector, respectively, where the historical key feature vector and the historical value feature vector are arranged in the order of the time window. For example, the current query feature vector is [0.3, 0.4, 0.5], and the historical key feature vectors in the past three time windows are [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], and [0.4, 0.5, 0.6], and the corresponding historical value feature vectors are [0.4, 0.5, 0.6], [0.5, 0.6, 0.7], and [0.6, 0.7, 0.8].

[0032] The initial time window relevance sequence is generated by performing a dot product operation on the current query feature vector and the key feature vector of each historical time window. For example, the dot product of the query feature vector and the first historical key feature vector yields [0.3×0.2+0.4×0.3+0.5×0.4]=0.38. The initial time window relevance sequence is then exponentially scaled with a learnable scaling factor of 1.5 to generate the normalized attention energy distribution. Softmax normalization is performed on the attention energy distribution along the time window dimension to generate the weight allocation coefficient for each historical time window. The weight allocation coefficients are then weighted and superimposed on the historical value feature vectors in the order of the time windows to generate the feature weight distribution for each time window.

[0033] Based on the feature weight distribution, the features of the historical time window are dynamically weighted and fused to generate an enhanced spatiotemporal feature representation. The enhanced spatiotemporal feature representation is input into the risk classifier of the collision risk prediction model, which contains a longitudinal risk branch, a lateral risk branch, and an emergency braking branch set up in parallel. The longitudinal risk branch analyzes the input features and outputs a longitudinal collision risk level, for example, divided into three levels: low, medium, and high. The lateral risk branch outputs a lateral deviation risk level, which is also divided into different levels. The emergency braking branch outputs an emergency braking trigger probability, for example, an output probability of 0.2. The output results of the parallel longitudinal risk branch, lateral risk branch, and emergency braking branch are fused at the decision level to generate a risk prediction result.

[0034] Step S140 : constructing a real-time warning strategy set based on the risk prediction result, wherein the real-time warning strategy set includes warning signal generation rules and vehicle control instruction sequences corresponding to different risk levels.

[0035] For example, if the longitudinal collision risk level is high, the warning signal generation rule is to issue a voice prompt through the in-vehicle voice system, "There is a high collision risk ahead, please take immediate action," while the warning light on the instrument panel flashes red. The vehicle control command sequence is to automatically activate the emergency braking system and adjust the brake pressure to the maximum allowable value, such as 500kPa. When the longitudinal collision risk level is medium, the warning signal generation rule is to issue a voice prompt, "There is a medium collision risk ahead, please pay attention," with the warning light flashing yellow. The vehicle control command sequence is to reduce the vehicle speed appropriately, for example, by 20%. When the longitudinal collision risk level is low, the warning signal generation rule is to issue a voice prompt, "There is a low collision risk ahead, remain alert," with the warning light flashing green. The vehicle control command sequence is to maintain the current vehicle speed.

[0036] For high-risk lateral departures, the warning signal generation rule is to alert the driver by vibrating the seat and issuing a voice prompt stating "The vehicle has a serious lateral departure risk." The vehicle control command sequence is to automatically adjust the steering system to an appropriate value to correct the lateral departure. For medium-risk lateral departures, the warning signal generation rule is to slightly vibrate the steering wheel and issue a voice prompt stating "The vehicle has a moderate lateral departure risk." The vehicle control command sequence is to fine-tune the steering angle appropriately. For low-risk lateral departures, the warning signal generation rule is to display a prompt message on the instrument panel stating "The vehicle has a low lateral departure risk." The vehicle control command sequence is to not actively intervene but to continue monitoring.

[0037] For the probability of emergency braking triggering, when the probability exceeds 0.5, the warning signal generation rule is to issue a sharp alarm and rapidly flash the warning light red. The vehicle control command sequence is to immediately activate the emergency braking system. When the probability is between 0.3 and 0.5, the warning signal generation rule is to issue a voice prompt "High likelihood of emergency braking, please prepare" and the warning light flashes orange. The vehicle control command sequence is to pre-charge the brake system to improve braking response speed. When the probability is less than 0.3, the warning signal generation rule is to not issue a special warning and the vehicle control command sequence is to monitor normal driving. Based on these rules, a set of real-time warning strategies is constructed.

[0038] Step S150: deploy the real-time warning strategy set to the vehicle-mounted decision-making system, and establish a control instruction mapping relationship with the vehicle actuator to trigger a real-time risk intervention operation.

[0039] In this embodiment, the constructed real-time warning strategy set can be written into the storage module of the onboard decision-making system. Information such as the warning signal generation rules and vehicle control command sequence can be accurately and accurately transmitted to the corresponding modules via the onboard network. For example, the warning signal generation rules and vehicle control command sequence for a high longitudinal collision risk level can be stored in a dedicated risk processing module. A mapping relationship is established with the control commands of the vehicle's actuators. For the braking system, when a vehicle control command for emergency braking is received, the command is sent to the brake actuator unit via the controller area network (CAN) bus. The brake actuator unit adjusts the brake pressure to the set value based on the command. For the steering system, when a command to adjust the steering angle is received, the electronic control unit (ECU) transmits the command to the steering motor, which adjusts the steering angle accordingly. In this way, when the risk prediction results during vehicle operation trigger the corresponding warning level, the onboard decision-making system can quickly trigger real-time risk intervention operations based on the real-time warning strategy set and the control command mapping relationship with the vehicle actuators to ensure vehicle driving safety.

[0040] Based on the above steps, the embodiment of the present invention integrates multi-source sensor data sets such as vehicle operating status, environmental perception and driving behavior. On this basis, the dynamic spatiotemporal feature extraction operation not only captures key information such as vehicle movement trends and environmental interactions, but also introduces behavioral abnormality characteristics, effectively identifying potential human driving risks and enhancing the foresight and accuracy of risk identification. Furthermore, the pre-trained collision risk prediction model converts dynamic spatiotemporal coding features into risk prediction results through multi-dimensional risk association analysis, including longitudinal collision risk level, lateral deviation risk level and emergency braking trigger probability, realizing quantitative assessment and graded warning of collision risks, and providing a scientific and quantitative decision-making basis for vehicle risk control. The real-time warning strategy set constructed based on the risk prediction results dynamically generates warning signals and control instructions according to the risk level, ensuring the timeliness of warnings and the effectiveness of intervention, and effectively reducing the probability of accidents and the degree of losses. Finally, the real-time warning strategy set is deployed to the on-board decision-making system, and a control command mapping relationship with the vehicle's actuator is established, realizing the connection from risk warning to active intervention, forming an active protection network for safe vehicle driving. It not only improves the safety of vehicle driving, reduces the risk of auto insurance claims, but also realizes intelligent and precise risk control measures.

[0041] In a possible implementation, step S120 includes:

[0042] Step S121 , performing motion feature extraction processing on the vehicle running state data sequence to obtain vehicle motion trend features, wherein the vehicle motion trend features include acceleration change rate features, steering angular velocity features, and brake pressure gradient features.

[0043] In a possible implementation, step S121 includes:

[0044] Step S1211 , collecting the longitudinal acceleration data sequence, lateral angular velocity data sequence and brake pressure data sequence of the target vehicle.

[0045] For example, a high-precision sensor installed on a target vehicle continuously collects longitudinal acceleration data, lateral angular velocity data, and brake pressure data. During a specific driving period, the longitudinal acceleration sensor records longitudinal acceleration values every 0.1 seconds, forming a longitudinal acceleration data sequence, such as [1.0, 1.2, 1.4, 1.6, 1.8] (unit: m / s²). The lateral angular velocity sensor simultaneously collects lateral angular velocity data every 0.1 seconds, generating a lateral angular velocity data sequence, such as [0.1, 0.2, 0.3, 0.4, 0.5] (unit: rad / s). The brake pressure sensor also records brake pressure data at the same interval, generating a brake pressure data sequence, such as [15, 18, 21, 24, 27] (unit: kPa).

[0046] Step S1212: construct multiple parallel time convolution networks to process the longitudinal acceleration data sequence, the lateral angular velocity data sequence, and the brake pressure data sequence respectively, and generate acceleration timing features, steering timing features, and braking timing features.

[0047] In one possible implementation, step S1212 includes:

[0048] Step S1212-1: construct a first time convolutional network for the longitudinal acceleration data sequence, wherein the first time convolutional network includes alternating dilated convolutional layers and normalization layers for capturing multi-scale acceleration pattern features.

[0049] For example, the dilation coefficient of the dilated convolution layer is set to 3, which is used to capture multi-scale acceleration pattern features. When performing a convolution operation on a longitudinal acceleration data sequence through the dilated convolution layer, the data can be processed with an interval of 3. For example, for the first data point 1.0 in the longitudinal acceleration data sequence, the dilated convolution layer combines it with subsequent data points with an interval of 3 to perform feature extraction, discovering potential periodic or trend characteristics in the data. After processing through the dilated convolution layer, a series of eigenvalues is obtained, such as [0.2, 0.3, 0.4, 0.5, 0.6]. These eigenvalues are then mapped to the interval [0, 1] through the normalization layer. Specifically, the normalization process first finds the minimum value 0.2 and the maximum value 0.6 in this set of eigenvalues. For each eigenvalue, the formula (eigenvalue - minimum value) / (maximum value - minimum value) is used for calculation. For example, the first eigenvalue is 0.2, which is calculated as (0.2-0.2) / (0.6-0.2)=0; for the eigenvalue 0.6, it is calculated as (0.6-0.2) / (0.6-0.2)=1. The entire sequence is normalized in this way, and the acceleration time series features are finally obtained.

[0050] Step S1212-2: construct a second temporal convolutional network for the lateral angular velocity data sequence, where the second temporal convolutional network includes a bidirectional convolutional layer and a gated activation unit, and is used to extract timing-dependent features of the steering operation.

[0051] For example, the bidirectional convolutional layer can simultaneously convolve lateral angular velocity data in both the forward and reverse directions, effectively extracting the temporal-dependent features of steering maneuvers. For example, when processing the lateral angular velocity data sequence [0.1, 0.2, 0.3, 0.4, 0.5], the bidirectional convolutional layer can detect a trend of increasing lateral angular velocity over time through forward convolution, while verifying the stability of this trend through reverse convolution. The gated activation unit then filters and activates the convolution results based on data importance. After this series of processing, the steering temporal features are generated.

[0052] Step S1212-3: construct a third temporal convolutional network for the brake pressure data sequence, wherein the third temporal convolutional network includes a residual convolution module and an attention pooling layer, and is used to identify the sudden characteristics of the braking behavior.

[0053] For example, the residual convolution module preserves key information in the brake pressure data, preventing it from being lost during the convolution process. The attention pooling layer focuses on key features of braking behavior to identify sudden changes. For example, if a sudden increase in the brake pressure data sequence occurs, the attention pooling layer can highlight this feature. After processing by this network, the braking time series features are obtained.

[0054] Step S1212-4: Perform feature dimensionality reduction processing on the outputs of the first temporal convolutional network, the second temporal convolutional network, and the third temporal convolutional network, respectively, to obtain the acceleration timing features, the steering timing features, and the braking timing features.

[0055] Step S1213: performing differential calculation on the acceleration time series feature to obtain an acceleration change rate feature, performing sliding window variance calculation on the steering time series feature to obtain a steering angular velocity fluctuation feature, and performing gradient analysis on the braking time series feature to obtain a braking pressure gradient feature.

[0056] Assuming the acceleration time series characteristic is [0.2, 0.3, 0.4, 0.5, 0.6], the difference calculation is to subtract the previous eigenvalue from the next eigenvalue. The first acceleration rate of change eigenvalue is 0.3-0.2=0.1, the second is 0.4-0.3=0.1, the third is 0.5-0.4=0.1, and the fourth is 0.6-0.5=0.1. This gives the acceleration rate of change characteristic of [0.1, 0.1, 0.1, 0.1].

[0057] Assume further that the sliding window size is set to 3. For the steering time series feature [0.2, 0.3, 0.4, 0.5, 0.6], the first sliding window contains the data 0.2, 0.3, and 0.4. First, calculate the average of these three data points, that is, (0.2 + 0.3 + 0.4) / 3 = 0.3. Then calculate the variance. The variance is calculated by summing the squares of the differences between each data point and the average value and dividing it by the number of data points. That is, [(0.2 - 0.3)² + (0.3 - 0.3)² + (0.4 - 0.3)²] / 3 = (0.01 + 0 + 0.01) / 3 ≈ 0.0067. Following the same method, calculate the subsequent sliding window data in sequence to obtain the steering angular velocity fluctuation feature.

[0058] Furthermore, gradient analysis can be understood as calculating the rate of change between two adjacent braking time series eigenvalues. Assuming the braking time series is [0.3, 0.5, 0.7, 0.9, 1.1], the first brake pressure gradient eigenvalue is (0.5-0.3) / (0.1)=2 (where 0.1 is the time interval), the second is (0.7-0.5) / (0.1)=2, the third is (0.9-0.7) / (0.1)=2, and the fourth is (1.1-0.9) / (0.1)=2, resulting in a brake pressure gradient characteristic of [2, 2, 2, 2].

[0059] Step S1214 , combining the acceleration change rate feature, the steering angular velocity fluctuation feature, and the brake pressure gradient feature to obtain the vehicle motion trend feature.

[0060] For example, the acceleration change rate characteristic is [0.1, 0.1, 0.1, 0.1], the steering angular velocity fluctuation characteristic is [0.0067, 0.008, 0.009, 0.01], and the brake pressure gradient characteristic is [2, 2, 2, 2]. After splicing, the vehicle motion trend characteristic is [0.1, 0.1, 0.1, 0.1, 0.0067, 0.008, 0.009, 0.01, 2, 2, 2, 2]. This vehicle motion trend characteristic comprehensively reflects the motion trend characteristics of the vehicle during operation.

[0061] Step S122 , performing spatial correlation analysis on the environmental perception data sequence to extract environmental interaction features, wherein the environmental interaction features include obstacle relative motion trajectory features, lane line curvature matching features, and visibility influence coefficient features.

[0062] In a possible implementation, step S122 includes:

[0063] Step S1221: Obtain a relative position data sequence of obstacles around the target vehicle, a lane line curvature data sequence, and an ambient light intensity data sequence.

[0064] In this embodiment, sensors surrounding the target vehicle continuously acquire data sequences of the relative positions of surrounding obstacles, lane curvature, and ambient light intensity. The millimeter-wave radar measures the relative position of the obstacle and the vehicle every 0.2 seconds, generating a relative position data sequence. For example, the relative position data of an obstacle at several time points may be [(15, 4), (13, 5), (11, 6), (9, 7)] (unit: meters, representing the lateral and longitudinal distances, respectively). The lane recognition camera monitors the lanes and acquires a lane curvature data sequence, such as [0.008, 0.01, 0.012, 0.014, 0.016]. The ambient light sensor acquires an ambient light intensity data sequence, such as [450, 480, 510, 540, 570] (unit: lux), at 1-second intervals.

[0065] Step S1222: construct an obstacle motion trajectory prediction model, perform motion state estimation on the relative position data sequence based on a Kalman filter algorithm, and generate obstacle relative motion trajectory features. The obstacle relative motion trajectory features include lateral displacement prediction error features and longitudinal velocity covariance features.

[0066] In a possible implementation, step S1222 includes:

[0067] Step S1222-1, initialize the motion state vector of the obstacle, wherein the motion state vector includes a lateral position observation value, a longitudinal velocity observation value, and an acceleration estimation value.

[0068] In a given intelligent traffic scenario, the target vehicle is constantly moving, and the surrounding environment is complex and dynamically changing, making accurate prediction of obstacle trajectories crucial. At this point, the millimeter-wave radar continuously measures the relative positions of obstacles around the target vehicle at 0.2-second intervals, generating a sequence of relative position data.

[0069] In this step, assume that at a certain moment, millimeter-wave radar measurements indicate that the obstacle's lateral position relative to the target vehicle is 25 meters, its longitudinal velocity is 12 meters per second, and its estimated acceleration is 0.4 meters per second squared. By combining these values, we form the obstacle's motion state vector, [25, 12, 0.4], which includes the lateral position observation, longitudinal velocity observation, and acceleration estimate.

[0070] Step S1222-2: Generate a state transfer matrix and an observation noise covariance matrix based on the vehicle coordinate system, perform time step iterative prediction on the motion state vector using the state transfer matrix, and output the predicted state vector and the corresponding predicted covariance matrix.

[0071] In this embodiment, the state transition matrix is generated based on the principles of vehicle kinematics and describes the law of change in the state of an obstacle within a time step. The time step is set to 0.2 seconds. Within this time interval, according to the kinematic formula, the change in the lateral position of the obstacle is related to the longitudinal velocity and acceleration, and the change in longitudinal velocity depends on the acceleration. After a series of calculations based on the principles of kinematics, the state transition matrix is determined. For example, the calculation of the lateral position takes into account the effects of the current longitudinal velocity and acceleration within 0.2 seconds, while the calculation of the longitudinal velocity is based solely on the cumulative effect of acceleration within 0.2 seconds. The observation noise covariance matrix comprehensively considers the noise factors present in the millimeter wave radar measurement process. Through statistical analysis of a large amount of historical measurement data, the values of each element of the observation noise covariance matrix are determined to reflect the distribution of measurement errors.

[0072] Specifically, the current motion state vector [25, 12, 0.4] is used as the basis for calculations based on the state transition matrix. The lateral position is calculated as the current lateral position plus the longitudinal velocity multiplied by the time step plus 0.5 times the acceleration multiplied by the square of the time step, which is 25 + 12 × 0.2 + 0.5 × 0.4 × (0.2)² = 27.408 meters. The longitudinal velocity is the current longitudinal velocity plus the acceleration multiplied by the time step, which is 12 + 0.4 × 0.2 = 12.08 meters per second. The acceleration remains constant at 0.4 meters per second squared. This yields the predicted state vector [27.408, 12.08, 0.4] and the corresponding prediction covariance matrix. This prediction covariance matrix reflects the degree of uncertainty in this prediction.

[0073] Step S1222-3, performing residual calculation on the predicted state vector and the actual observation value of the current time window, and calculating the Mahalanobis distance of the residual vector in combination with the predicted covariance matrix.

[0074] Assume that the actual lateral position of the obstacle observed by the millimeter-wave radar in the current time window is 27 meters, and its longitudinal velocity is 12.1 meters per second. The lateral position residual is the predicted lateral position minus the observed lateral position, which is 27.408 - 27 = 0.408 meters. The longitudinal velocity residual is the predicted longitudinal velocity minus the observed longitudinal velocity, which is 12.08 - 12.1 = -0.02 meters per second. This results in the residual vector [0.408, -0.02]. The Mahalanobis distance of the residual vector is calculated using the predicted covariance matrix. The Mahalanobis distance calculation considers the relationship between the residual vector and the predicted covariance matrix. The specific calculation process involves matrix multiplication of the residual vector with the inverse of the predicted covariance matrix, then performing a dot product of the result with the residual vector, and finally taking the square root of the dot product. Through a series of matrix operations and numerical calculations, the Mahalanobis distance is obtained.

[0075] Step S1222-4: When the Mahalanobis distance exceeds a dynamic adjustment threshold, the noise parameters of the observation noise covariance matrix are corrected by a scaling factor to generate an updated observation noise covariance matrix.

[0076] Assume the dynamic adjustment threshold is set to 0.3, and the actual calculated Mahalanobis distance is 0.35, exceeding the threshold. In this case, the noise parameters of the observation noise covariance matrix are corrected by a scaling factor of 1.3. Specifically, each element in the observation noise covariance matrix is multiplied by the scaling factor of 1.3 to generate the updated observation noise covariance matrix.

[0077] Step S1222-5: re-execute the measurement update step of the Kalman filter using the updated observation noise covariance matrix, and output the corrected predicted state vector and lateral displacement prediction error characteristics.

[0078] For example, during this process, the predicted state vector is readjusted using the updated observation noise covariance matrix, combined with the actual observations and the previously predicted state vector, through a series of predefined calculation methods. This calculation results in a revised predicted state vector. Simultaneously, the lateral displacement prediction error characteristic is calculated—that is, the difference between the revised predicted lateral position and the actual observed lateral position. Assume that the resulting lateral displacement prediction error characteristic is 0.3 meters.

[0079] Step S1222-6: Input the corrected predicted state vector into the trajectory pattern matching network, perform convolution similarity comparison with the typical motion patterns in the historical trajectory database, and generate longitudinal velocity covariance features.

[0080] The trajectory pattern matching network stores a historical trajectory database containing various typical motion patterns. The revised predicted state vector is then subjected to a convolutional similarity comparison with the typical motion patterns in the historical trajectory database. By performing convolution operations on different typical motion patterns and the revised predicted state vector, a similarity score is calculated between them. For example, for each typical motion pattern, it is matched and calculated with the revised predicted state vector in both time and space dimensions to obtain a similarity value. By comparing all similarity values, the best-matching typical motion pattern is found, and a longitudinal velocity covariance feature is generated based on this match. Assume that the generated longitudinal velocity covariance feature is 0.5.

[0081] Step S1222-7: Fusion the lateral displacement prediction error feature and the longitudinal velocity covariance feature to generate the obstacle relative motion trajectory feature.

[0082] For example, the lateral displacement prediction error feature of 0.3 meters and the longitudinal velocity covariance feature of 0.5 are combined to form the obstacle relative motion trajectory feature [0.3, 0.5]. This obstacle relative motion trajectory feature comprehensively reflects the motion trajectory characteristics of the obstacle relative to the target vehicle, providing an important basis for subsequent risk assessment and decision-making, helping the target vehicle to better deal with obstacles in the surrounding environment and ensure driving safety.

[0083] Step S1223: Call a pre-trained lane feature encoder to perform multi-scale feature extraction on the lane curvature data sequence to generate a lane curvature matching feature. The lane curvature matching feature includes a dynamic matching coefficient between the current lane curvature and the vehicle steering angle.

[0084] For example, the lane feature encoder can analyze lane curvature data at different scales, focusing on local lane curvature at a small scale and overall curvature trends at a large scale. This encoder processes the lane curvature data sequence [0.008, 0.01, 0.012, 0.014, 0.016] and generates a lane curvature matching feature containing the dynamic matching coefficient between the current lane curvature and the vehicle steering angle. Assume that the generated feature value is 0.8.

[0085] Step S1224 , performing sliding window statistical processing on the ambient light intensity data sequence to generate a visibility influence coefficient feature, wherein the visibility influence coefficient feature includes an associated weight parameter of the light intensity change rate and the obstacle recognition confidence.

[0086] Assuming a sliding window size of 3, for the ambient light intensity data sequence [450, 480, 510, 540, 570], the first sliding window contains data at 450, 480, and 510. To calculate the rate of change of light intensity, first calculate the difference between adjacent data points: 480 - 450 = 30, 510 - 480 = 30, and the average rate of change is (30 + 30) / 2 = 30. Furthermore, based on the algorithm and the obstacle recognition confidence, assuming the empirically calculated association weight parameter is 0.6, the resulting visibility impact coefficient feature is [30, 0.6].

[0087] Step S1225 , performing spatial position encoding and fusion on the obstacle relative motion trajectory feature, lane line curvature matching feature, and visibility influence coefficient feature to obtain the environment interaction feature.

[0088] For example, the obstacle relative motion trajectory feature [0.5, 0.4], the lane line curvature matching feature 0.8 and the visibility influence coefficient feature [30, 0.6] are fused according to the set rules. It is assumed that the fused environmental interaction feature is [0.5, 0.4, 0.8, 30, 0.6].

[0089] Step S123 , performing behavior pattern recognition processing on the driving behavior data sequence to generate behavior abnormality features, wherein the behavior abnormality features include steering wheel operation frequency features, pedal switching interval features, and line of sight deviation duration features.

[0090] In a possible implementation, step S123 includes:

[0091] Step S1231 , collecting a steering wheel angle change data sequence, a pedal position switching data sequence, and a driver's sight direction data sequence.

[0092] For example, sensors installed on the steering wheel, pedals, and in front of the driver's seat can collect data sequences of steering wheel angle changes, pedal position switching, and driver's line of sight direction.

[0093] Step S1232 , performing frequency domain feature extraction on the steering angle change data sequence to generate a steering wheel operation frequency feature, wherein the steering wheel operation frequency feature includes an energy proportion parameter of a set frequency band.

[0094] In a possible implementation, step S1232 includes:

[0095] Step S1232-1, performing denoising preprocessing on the original corner signal, using a wavelet threshold denoising algorithm to remove high-frequency noise components to obtain a denoised corner signal.

[0096] Step S1232-2: Perform short-time Fourier transform on the denoised corner signal to generate a time-frequency energy distribution matrix.

[0097] Step S1232-3: extracting energy peak features within a set frequency band of the time-frequency energy distribution matrix, and calculating a parameter of the ratio of the energy within the set frequency band to the total energy based on the energy peak features.

[0098] Step S1232-4: performing sliding average processing on the proportional parameters of N consecutive time windows to generate the steering wheel operation frequency characteristics.

[0099] The steering wheel angle sensor records steering wheel angle changes in real time, generating a data sequence of angle changes at 0.1-second intervals, such as [8, 10, 12, 14, 16] (unit: degrees). This angle change data sequence undergoes denoising preprocessing, using a wavelet threshold denoising algorithm to remove high-frequency noise components. The wavelet threshold denoising algorithm first decomposes the original steering wheel angle signal into wavelet coefficients of different frequencies. Based on a set threshold, the wavelet coefficients above the threshold are retained and those below the threshold are reset to zero. The signal is then reconstructed using an inverse wavelet transform to obtain the denoised steering wheel angle signal. Assume that the denoised steering wheel angle signal is [8.2, 10.1, 12.1, 14.1, 16.1].

[0100] In this embodiment, the short-time Fourier transform converts the time domain signal into a representation of the time-frequency domain, segments the signal by selecting a suitable window function, and then performs a Fourier transform on each segment to obtain a time-frequency energy distribution matrix. For example, in the time-frequency energy distribution matrix, the frequency band is set to [15-25Hz], and the energy peak feature is extracted within this frequency band. Assuming that the energy peaks found in this frequency band are [10, 12, 14, 16, 18], the ratio parameter of the energy of the set frequency band to the total energy is calculated based on the above energy peak feature. The total energy is calculated as the sum of the energies of all frequency bands. Assuming that the total energy is 100, the first ratio parameter is 10 / 100=0.1, the second is 12 / 100=0.12, and so on. The ratio parameters of four consecutive time windows are subjected to sliding average processing to generate the steering wheel operation frequency feature. For example, after sliding average, the steering wheel operation frequency feature is obtained as [0.11, 0.13, 0.15, 0.17].

[0101] Step S1233: performing event interval analysis on the pedal position switching data sequence to generate a pedal switching interval feature, wherein the pedal switching interval feature includes a time interval distribution feature of alternating operations of the accelerator pedal and the brake pedal.

[0102] The pedal position sensor monitors pedal position switching, generating a pedal position switching data sequence and recording the duration of each pedal switch, such as [0.4, 0.8, 1.2, 1.6, 2.0] (unit: seconds, representing the time interval between the last switch and the current switch). This pedal position switch data sequence is analyzed for event intervals to generate pedal switch interval features. For example, the analysis yields the distribution of the time intervals between alternating accelerator and brake pedal operation, assuming the resulting feature value is [0.4, 0.4, 0.4, 0.4].

[0103] Step S1234 , performing continuous deviation detection on the gaze direction data sequence to generate a gaze deviation duration feature, wherein the gaze deviation duration feature includes a correlation coefficient between the duration of a single gaze deviation event and the vehicle lateral position offset.

[0104] The driver's gaze monitoring device tracks the driver's gaze direction using a camera, generating a gaze direction data sequence. This data sequence records the duration of each gaze deviation from the vehicle's forward direction and the amount of the vehicle's lateral position offset, for example, [(2, 0.08), (3, 0.1), (4, 0.12), (5, 0.14), (6, 0.16)] (units: seconds and meters). This gaze direction data sequence is then tested for sustained deviations, generating a gaze deviation duration feature. For example, this feature contains the correlation coefficient between the duration of a single gaze deviation event and the vehicle's lateral position offset. For example, the calculated correlation coefficient is [0.4, 0.33, 0.3, 0.28, 0.25].

[0105] Step S1235 , normalizing and splicing the steering wheel operation frequency feature, the pedal switching interval feature, and the line of sight deviation duration feature to obtain the behavior abnormality feature.

[0106] In this embodiment, standardization is to map the feature values of the steering wheel operation frequency feature, the pedal switching interval feature, and the line of sight deviation duration feature to a specific range, for example, the interval [0, 1], and then splice them together in order. Assume that the standardized steering wheel operation frequency feature is [0.2, 0.3, 0.4, 0.5], the pedal switching interval feature is [0.3, 0.3, 0.3, 0.3], and the line of sight deviation duration feature is [0.2, 0.2, 0.2, 0.2], and the behavior abnormality feature obtained after splicing is [0.2, 0.3, 0.4, 0.5, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2].

[0107] Step S124 , performing spatiotemporal alignment and fusion processing on the vehicle motion trend feature, the environment interaction feature, and the behavior abnormality feature to generate the dynamic spatiotemporal coding feature.

[0108] In this embodiment, spatiotemporal alignment involves matching and calibrating different features in time and space to ensure they can be integrated in the same dimension. For example, the vehicle motion trend feature is [0.1, 0.1, 0.1, 0.1, 0.0067, 0.008, 0.009, 0.01, 2, 2, 2, 2], the environment interaction feature is [0.5, 0.4, 0.8, 30, 0.6], and the behavior abnormality feature is [0.2, 0.3, 0.4, 0.5, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2, 0.2].

[0109] When performing fusion, the eigenvectors are first dimensionally aligned. Because different eigenvectors may have different dimensions, they need to be aligned through padding or dimensionality reduction. Here, we assume that zero padding is used to pad the environment interaction feature vector to the same length as the other two feature vectors. For example, after padding, the environment interaction feature vector is [0.5, 0.4, 0.8, 30, 0.6, 0, 0, 0, 0, 0, 0].

[0110] Then, the three feature vectors are fused according to the set rules. For example, a simple splicing method can be used to splice the vehicle motion trend feature, the filled environmental interaction feature, and the behavior abnormality feature together in sequence to obtain the dynamic spatiotemporal coding feature [0.1, 0.1, 0.1, 0.1, 0.0067, 0.008, 0.009, 0.01, 2, 2, 2, 2, 0.5, 0.4, 0.8, 30, 0.6, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.3, 0.4, 0.5, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2, 0.2].

[0111] This dynamic spatiotemporal encoding feature comprehensively integrates information from multiple aspects, including vehicle operating status, environmental perception, and driving behavior. It combines data features from different sources into a single vector, providing rich and valuable input for subsequent multi-dimensional risk association analysis using a pre-trained collision risk prediction model. This enables the collision risk prediction model to more accurately analyze and predict the various risk conditions currently facing the vehicle based on these comprehensive features. This approach allows for a better understanding of the vehicle's operating status in complex environments, providing a solid data foundation for the development of real-time warning strategies and risk intervention operations, ultimately ensuring safe driving.

[0112] In a possible implementation, step S130 includes:

[0113] Step S131: input the dynamic spatiotemporal coding features into the spatiotemporal attention module of the collision risk prediction model to generate feature weight distributions of different time windows.

[0114] For example, in one possible implementation, step S131 includes:

[0115] Step S1311, input the dynamic spatiotemporal coding features of the current time window and the dynamic spatiotemporal coding features of the historical time window into the linear mapping layer of the spatiotemporal attention module, and generate the current query feature vector, the historical key feature vector and the historical value feature vector respectively, wherein the historical key feature vector and the historical value feature vector are arranged in the order of the time window.

[0116] For example, in this process, the dynamic spatiotemporal encoding features of the current time window and the dynamic spatiotemporal encoding features of the historical time windows are input into the linear mapping layer of the spatiotemporal attention module. Assume that the dynamic spatiotemporal encoding features of the current time window are a vector of length 50, where each element represents a different feature value. For example, the first 10 elements represent feature values related to the acceleration rate of change in the vehicle motion trend feature, the middle 20 elements represent feature values related to the relative motion trajectory of obstacles in the environmental interaction feature, and the last 20 elements represent feature values related to the steering wheel operation frequency in the behavioral abnormality feature. The historical time window is set to include the dynamic spatiotemporal encoding features of the first three time windows, and each feature vector is also of length 50.

[0117] After processing by the linear mapping layer, the current query feature vector, historical key feature vector, and historical value feature vector are generated respectively. The linear mapping layer realizes mapping by performing matrix multiplication operations on the input feature vector using a specific weight matrix. For example, for the dynamic spatiotemporal coding feature vector of the current time window, the weight matrix in the linear mapping layer is a 50×30 matrix. Through matrix multiplication operations, the 50-dimensional feature vector is mapped to the 30-dimensional current query feature vector. For the dynamic spatiotemporal coding feature vector of the historical time window, a similar linear mapping operation is also performed to generate the historical key feature vector and historical value feature vector arranged in the order of the time window. Here, the historical key feature vector and the historical value feature vector are also 30-dimensional vectors.

[0118] Step S1312 : performing a dot product operation on the current query feature vector and the key feature vector of each historical time window to generate an initial time window association degree sequence.

[0119] In this embodiment, the dot product operation is to multiply the elements of the corresponding positions of the two vectors and then sum them. Assuming that the current query feature vector is [q1, q2, ..., q30] and the key feature vector of the first historical time window is [k11, k12, ..., k130], then their dot product operation is q1×k11+q2×k12+...+q30×k130, and a scalar value is obtained, which is the initial correlation between the current time window and the first historical time window. According to the same method, the dot product of the current query feature vector and the key feature vector of the second and third historical time windows is calculated in turn to generate the initial time window correlation sequence. Assume that the obtained sequence is [0.2, 0.3, 0.4].

[0120] Step S1313 , performing an exponential scaling operation with a learnable scaling coefficient on the initial time window relevance sequence to generate an attention energy distribution before normalization.

[0121] The learnable scaling factor is a parameter learned by the model during training. Assume the current learnable scaling factor is 1.5. For each value in the initial time window relevance sequence, perform an exponential scaling operation. For the first value, 0.2, we calculate it as e raised to the power of (0.2 × 1.5), where e is a natural constant of approximately 2.718. Following the same calculation method, we perform the same calculation for values 0.3 and 0.4 in the sequence, obtaining the normalized attention energy distribution, assumed to be [1.34, 2.01, 2.71].

[0122] Step S1314: Softmax normalization calculation is performed on the attention energy distribution along the time window dimension to generate a weight distribution coefficient for each historical time window.

[0123] In this embodiment, the calculation process of Softmax normalization is to calculate the result of dividing each value in the attention energy distribution by the sum of all values. For example, for the above attention energy distribution [1.34, 2.01, 2.71], the sum is 1.34+2.01+2.71=6.06. Then the weight distribution coefficient of the first historical time window is 1.34÷6.06≈0.22, the weight distribution coefficient of the second historical time window is 2.01÷6.06≈0.33, and the weight distribution coefficient of the third historical time window is 2.71÷6.06≈0.45. In this way, the weight distribution coefficient sequence [0.22, 0.33, 0.45] for each historical time window is generated.

[0124] Step S1315, performing a window-by-window weighted superposition operation on the weight distribution coefficient and the historical value feature vector in the order of the time windows to generate feature weight distributions for different time windows, wherein the dimension of the feature weight distribution is consistent with the number of historical time windows and each element corresponds to the weight value of a single window.

[0125] Taking the first historical time window as an example, the weight distribution coefficient is 0.22, the historical value feature vector is [v11, v12, …, v130], and the weighted vector is [0.22×v11, 0.22×v12, …, 0.22×v130]. The same method is used to weight the historical value feature vectors of the second and third historical time windows. The weighted vectors are then superimposed in the order of the time windows to obtain the feature weight distribution for different time windows. The dimension of the feature weight distribution here is the same as the number of historical time windows, 3, and each element corresponds to the weight value of a single window. For example, the resulting feature weight distribution is [[0.22×v11, 0.22×v12, …, 0.22×v130], [0.33×v21, 0.33×v22, …, 0.33×v230], [0.45×v31, 0.45×v32, …, 0.45×v330]].

[0126] Step S132: Dynamically weighted fusion is performed on the features of the historical time window based on the feature weight distribution to generate an enhanced spatiotemporal feature representation.

[0127] In this embodiment, dynamic weighted fusion is to perform weighted summation of the feature weight distribution of different time windows obtained above and the original features of the corresponding historical time windows. Taking the first element as an example, assuming that the element of the original feature vector of the first historical time window at a certain position is f1, the element of the original feature vector of the second historical time window at the same position is f2, and the element of the original feature vector of the third historical time window at the same position is f3, the corresponding weight distribution coefficients are 0.22, 0.33, and 0.45, then the enhanced feature value at this position after fusion is 0.22×f1+0.33×f2+0.45×f3. According to the same method, the elements at all positions are calculated to obtain a new vector, which is the enhanced spatiotemporal feature representation, which integrates the feature information of different time windows and reasonably weights the features of different time windows according to the feature weight distribution, highlighting the feature information of historical time windows with a higher correlation with the current time window.

[0128] Step S133: Input the enhanced spatiotemporal feature representation into a risk classifier of the collision risk prediction model. The risk classifier includes a longitudinal risk branch, a lateral risk branch, and an emergency braking branch, all arranged in parallel. The longitudinal risk branch outputs a longitudinal collision risk level, the lateral risk branch outputs a lateral departure risk level, and the emergency braking branch outputs an emergency braking trigger probability.

[0129] For example, the longitudinal risk branch contains multiple neural network layers that deeply analyze features related to longitudinal motion within the enhanced spatiotemporal feature representation. These features may include the vehicle's longitudinal acceleration rate of change and the trend of changes in the longitudinal distance to the obstacle ahead. Through the neural network layers, these features are first weighted and combined, then subjected to a nonlinear transformation, and finally the longitudinal collision risk level is determined based on a preset threshold. Assume that, after calculation, if the result of the weighted combination and nonlinear transformation is greater than a high-risk threshold, the longitudinal collision risk level is output as high; if the result is within the medium-risk threshold, the longitudinal collision risk level is output as medium; and if the result is less than the low-risk threshold, the longitudinal collision risk level is output as low.

[0130] The lateral risk branch focuses on analyzing features related to lateral departure risk, such as changes in the vehicle's lateral angular velocity and the lateral distance from the lane line. Similarly, calculations and judgments are performed through the internal neural network layer to generate a lateral departure risk level. For example, the neural network layer can extract and analyze the above features, calculate the relationship and change trends between the features, and determine the lateral departure risk level according to the set rules. If the vehicle's lateral angular velocity changes significantly and the lateral distance from the lane line is close to the dangerous range, the neural network layer may output a high lateral departure risk level; if the above features are within a certain safe range, the corresponding medium or low risk level will be output.

[0131] The emergency braking branch primarily outputs the probability of an emergency brake trigger based on key features from the enhanced spatiotemporal feature representation, such as changes in the vehicle's brake pressure and the approaching speed of the obstacle ahead. The emergency braking branch processes these features using a predefined algorithm. For example, it performs a trend analysis on brake pressure changes and, combined with the approaching speed of the obstacle ahead, calculates the probability of an emergency brake trigger using a probabilistic model. Assuming that, after a series of calculations, when the brake pressure rises rapidly and the approaching speed of the obstacle ahead exceeds a certain critical value, the probability model calculates that the probability of an emergency brake trigger is 0.6. If these conditions are less urgent, the calculated probability may be lower.

[0132] Step S134 , performing decision-level fusion on the output results of the longitudinal risk branch, lateral risk branch, and emergency braking branch arranged in parallel to generate the risk prediction result.

[0133] In this embodiment, decision-level fusion can employ a variety of methods, such as simple voting or weighted averaging. Assuming the weighted averaging method is used, the longitudinal collision risk level output by the longitudinal risk branch is numerically represented, with high risk being 3, medium risk being 2, and low risk being 1. The lateral departure risk level output by the lateral risk branch is also numerically represented, with high risk being 3, medium risk being 2, and low risk being 1. The emergency braking trigger probability output by the emergency braking branch remains unchanged. Different weights are assigned to these three branches, assuming a weight of 0.4 for the longitudinal risk branch, 0.3 for the lateral risk branch, and 0.3 for the emergency braking branch.

[0134] For the longitudinal collision risk level, assume the output is medium risk, or a value of 2; the lateral risk branch outputs low risk, or a value of 1; and the emergency braking branch outputs an emergency braking trigger probability of 0.6. First, the weights for the longitudinal risk branch are calculated as 2 × 0.4 = 0.8, the lateral risk branch as 1 × 0.3 = 0.3, and the emergency braking branch as 0.6 × 0.3 = 0.18. These three weights are then added together, yielding a total of 0.8 + 0.3 + 0.18 = 1.28. Based on pre-defined rules, this sum is mapped to a corresponding risk level and probability range. For example, a sum within a certain range corresponds to a specific overall risk profile. Assuming the range of 1-1.5 corresponds to a risk prediction result of "medium-low risk, low likelihood of emergency braking." The resulting risk prediction clearly indicates the vehicle's current longitudinal collision risk, lateral departure risk, and emergency braking trigger probability. This provides an accurate basis for subsequently developing a real-time warning strategy based on this result, enabling the vehicle to take timely and appropriate measures to ensure safe driving.

[0135] Figure 2 A schematic diagram illustrates exemplary hardware and software components of an artificial intelligence-based vehicle collision warning system 100 that can implement the concepts of the present invention, as provided in some embodiments of the present invention. For example, a processor 120 can be used in the artificial intelligence-based vehicle collision warning system 100 to perform the functions of the present invention.

[0136] The AI-based vehicle collision warning system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the AI-based vehicle collision warning method of the present invention. Although only one server is shown in the present invention, for convenience, the functions described in the present invention can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0137] For example, the artificial intelligence-based vehicle collision warning system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. For example, the artificial intelligence-based vehicle collision warning system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present invention may be implemented based on these program instructions. The artificial intelligence-based vehicle collision warning system 100 also includes an input / output (I / O) interface 150 between the computer and other input / output devices.

[0138] For ease of explanation, only one processor is described in the artificial intelligence-based vehicle collision warning system 100. However, it should be noted that the artificial intelligence-based vehicle collision warning system 100 of the present invention may also include multiple processors, so the steps performed by one processor described in the present invention may also be performed jointly or individually by multiple processors. For example, if the processor of the artificial intelligence-based vehicle collision warning system 100 performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually in one processor. For example, the first processor performs step A and the second processor performs step B, or the first processor and the second processor perform steps A and B together.

[0139] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the above-mentioned vehicle collision warning method based on artificial intelligence is implemented.

[0140] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A vehicle collision warning method based on artificial intelligence, characterized in that: The method comprises: Acquire a multi-source sensor data set of a target vehicle, wherein the multi-source sensor data set includes a vehicle operating status data sequence, an environment perception data sequence, and a driving behavior data sequence, each data sequence consisting of standardized monitoring data of multiple continuous time windows; Performing a dynamic spatiotemporal feature extraction operation on the multi-source sensor data set to generate dynamic spatiotemporal coding features for each time window, the dynamic spatiotemporal coding features including vehicle motion trend features, environmental interaction features, and behavior abnormality features; Calling a pre-trained collision risk prediction model to perform multi-dimensional risk association analysis on the dynamic spatiotemporal coding features to generate a risk prediction result for the current time window, the risk prediction result including the longitudinal collision risk level, the lateral departure risk level, and the emergency braking trigger probability; Building a real-time warning strategy set based on the risk prediction results, the real-time warning strategy set including warning signal generation rules and vehicle control instruction sequences corresponding to different risk levels; Deploy the real-time warning strategy set to the vehicle decision-making system and establish a control command mapping relationship with the vehicle actuator to trigger real-time risk intervention operations; The performing of a dynamic spatiotemporal feature extraction operation on the multi-source sensor data set to generate a dynamic spatiotemporal coding feature for each time window includes: Performing motion feature extraction processing on the vehicle running state data sequence to obtain vehicle motion trend features, wherein the vehicle motion trend features include acceleration change rate features, steering angular velocity features, and brake pressure gradient features; Performing spatial correlation analysis on the environmental perception data sequence to extract environmental interaction features, wherein the environmental interaction features include obstacle relative motion trajectory features, lane line curvature matching features, and visibility influence coefficient features; Performing behavior pattern recognition processing on the driving behavior data sequence to generate behavior abnormality features, wherein the behavior abnormality features include steering wheel operation frequency features, pedal switching interval features, and line of sight deviation duration features; The vehicle motion trend feature, the environmental interaction feature, and the behavior abnormality feature are subjected to spatiotemporal alignment and fusion processing to generate the dynamic spatiotemporal coding feature.

2. The vehicle collision warning method based on artificial intelligence according to claim 1, characterized in that: The performing motion feature extraction processing on the vehicle running state data sequence to obtain vehicle motion trend features includes: Collecting a longitudinal acceleration data sequence, a lateral angular velocity data sequence, and a brake pressure data sequence of the target vehicle; Constructing multiple parallel time convolution networks to process the longitudinal acceleration data sequence, the lateral angular velocity data sequence, and the brake pressure data sequence respectively, to generate acceleration time series features, steering time series features, and braking time series features; Performing differential calculation on the acceleration time series characteristics to obtain acceleration change rate characteristics, performing sliding window variance calculation on the steering time series characteristics to obtain steering angular velocity fluctuation characteristics, and performing gradient analysis on the braking time series characteristics to obtain braking pressure gradient characteristics; The acceleration change rate feature, the steering angular velocity fluctuation feature and the brake pressure gradient feature are feature-concatenated to obtain the vehicle motion trend feature.

3. The vehicle collision warning method based on artificial intelligence according to claim 2, characterized in that: The constructing of multiple parallel time convolution networks to respectively process the longitudinal acceleration data sequence, the lateral angular velocity data sequence, and the brake pressure data sequence to generate acceleration timing features, steering timing features, and braking timing features includes: Constructing a first temporal convolutional network for the longitudinal acceleration data sequence, wherein the first temporal convolutional network comprises alternating dilated convolutional layers and normalization layers for capturing multi-scale acceleration pattern features; Constructing a second temporal convolutional network for the lateral angular velocity data sequence, wherein the second temporal convolutional network comprises a bidirectional convolutional layer and a gated activation unit, and is used to extract temporal dependency features of the steering operation; constructing a third temporal convolutional network for the brake pressure data sequence, wherein the third temporal convolutional network includes a residual convolution module and an attention pooling layer, and is used to identify the sudden characteristics of the braking behavior; The outputs of the first time convolutional network, the second time convolutional network and the third time convolutional network are respectively subjected to feature dimensionality reduction processing to obtain the acceleration timing features, the steering timing features and the braking timing features.

4. The vehicle collision warning method based on artificial intelligence according to claim 1, characterized in that: The performing of spatial correlation analysis on the environmental perception data sequence to extract environmental interaction features, wherein the environmental interaction features include obstacle relative motion trajectory features, lane line curvature matching features, and visibility influence coefficient features, including: Obtaining a relative position data sequence of obstacles around the target vehicle, a lane line curvature data sequence, and an ambient light intensity data sequence; Constructing an obstacle motion trajectory prediction model, performing motion state estimation on the relative position data sequence based on a Kalman filter algorithm, and generating obstacle relative motion trajectory features, wherein the obstacle relative motion trajectory features include lateral displacement prediction error features and longitudinal velocity covariance features; Calling a pre-trained lane feature encoder to perform multi-scale feature extraction on the lane curvature data sequence to generate a lane curvature matching feature, wherein the lane curvature matching feature includes a dynamic matching coefficient between the current lane curvature and the vehicle steering angle; Performing sliding window statistical processing on the ambient light intensity data sequence to generate a visibility influence coefficient feature, wherein the visibility influence coefficient feature includes an association weight parameter between the light intensity change rate and the obstacle recognition confidence; The obstacle relative motion trajectory feature, lane line curvature matching feature and visibility influence coefficient feature are spatially encoded and fused to obtain the environmental interaction feature.

5. The vehicle collision warning method based on artificial intelligence according to claim 4 is characterized in that: The step of constructing an obstacle motion trajectory prediction model, performing motion state estimation on the relative position data sequence based on a Kalman filter algorithm, and generating obstacle relative motion trajectory features includes: Initializing a motion state vector of the obstacle, wherein the motion state vector includes a lateral position observation value, a longitudinal velocity observation value, and an acceleration estimate; Generate a state transfer matrix and an observation noise covariance matrix based on the vehicle coordinate system, perform time step iterative prediction on the motion state vector using the state transfer matrix, and output the predicted state vector and the corresponding predicted covariance matrix; Perform residual calculation on the predicted state vector and the actual observation value of the current time window, and calculate the Mahalanobis distance of the residual vector in combination with the predicted covariance matrix; When the Mahalanobis distance exceeds a dynamic adjustment threshold, the noise parameters of the observation noise covariance matrix are corrected by a scaling factor to generate an updated observation noise covariance matrix; The updated observation noise covariance matrix is used to re-execute the measurement update step of the Kalman filter, and the corrected predicted state vector and lateral displacement prediction error characteristics are output; Inputting the corrected predicted state vector into a trajectory pattern matching network, performing convolution similarity comparison with typical motion patterns in a historical trajectory database, and generating longitudinal velocity covariance features; The lateral displacement prediction error feature and the longitudinal velocity covariance feature are integrated to generate the obstacle relative motion trajectory feature.

6. The vehicle collision warning method based on artificial intelligence according to claim 1, characterized in that: The performing behavior pattern recognition processing on the driving behavior data sequence to generate behavior abnormality characteristics includes: Collecting steering wheel angle change data sequence, pedal position switching data sequence and driver's sight direction data sequence; Performing frequency domain feature extraction on the steering angle change data sequence to generate a steering wheel operation frequency feature, wherein the steering wheel operation frequency feature includes an energy proportion parameter of a set frequency band; performing event interval analysis on the pedal position switching data sequence to generate a pedal switching interval feature, wherein the pedal switching interval feature includes a time interval distribution feature of alternating operations of an accelerator pedal and a brake pedal; Performing continuous deviation detection on the gaze direction data sequence to generate a gaze deviation duration feature, wherein the gaze deviation duration feature includes a correlation coefficient between the duration of a single gaze deviation event and a lateral position offset of the vehicle; The steering wheel operation frequency feature, the pedal switching interval feature, and the line of sight deviation duration feature are standardized and spliced to obtain the behavior abnormality feature.

7. The vehicle collision warning method based on artificial intelligence according to claim 6, characterized in that: The extracting frequency domain features of the steering angle change data sequence to generate steering wheel operation frequency features includes: The original corner signal is pre-processed to remove the high-frequency noise components by using the wavelet threshold denoising algorithm to obtain the denoised corner signal. Performing short-time Fourier transform on the denoised corner signal to generate a time-frequency energy distribution matrix; Extracting energy peak features within a set frequency band of the time-frequency energy distribution matrix, and calculating a parameter of the ratio of energy within the set frequency band to total energy based on the energy peak features; Perform sliding average processing on the proportional parameters of N consecutive time windows to generate the steering wheel operation frequency feature.

8. The vehicle collision warning method based on artificial intelligence according to claim 1, characterized in that: The calling of the pre-trained collision risk prediction model to perform multi-dimensional risk association analysis on the dynamic spatiotemporal coding features to generate a risk prediction result for the current time window includes: Inputting the dynamic spatiotemporal coding features into the spatiotemporal attention module of the collision risk prediction model to generate feature weight distributions for different time windows; Dynamically weighting and fusing the features of the historical time window based on the feature weight distribution to generate an enhanced spatiotemporal feature representation; Inputting the enhanced spatiotemporal feature representation into a risk classifier of the collision risk prediction model, the risk classifier comprising a longitudinal risk branch, a lateral risk branch, and an emergency braking branch arranged in parallel; wherein the longitudinal risk branch outputs a longitudinal collision risk level, the lateral risk branch outputs a lateral departure risk level, and the emergency braking branch outputs an emergency braking trigger probability; The output results of the longitudinal risk branch, the lateral risk branch and the emergency braking branch arranged in parallel are fused at the decision level to generate the risk prediction result.

9. A vehicle collision warning system based on artificial intelligence, characterized in that: The artificial intelligence-based vehicle collision warning system includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the artificial intelligence-based vehicle collision warning method described in any one of claims 1 to 8 above.

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