515 results about "Active safety" patented technology

The invention discloses a driver state sensing system based on physiological indexes and external behavior analysis, which relates to the technical field of vehicle active safety control, and comprises a sensor group integrated in a driver contact or close area and a vehicle control part and used for collecting physiological signals and behavior characteristic signals of a driver in real time, the physiological signals at least comprise heart rate and skin electric signals, and the behavior characteristic signals at least comprise eyelid movement, head posture and steering wheel operation signals. According to the driver state sensing system, the comprehensiveness and accuracy of driver state sensing are remarkably improved by integrating physiological indexes and external behavior analysis, the limitation of single signal judgment is effectively overcome through a multi-source signal fusion mechanism, the reliability of state evaluation is ensured, and the advanced signal preprocessing technology adopted by the system is high in reliability. The data anti-interference capability is enhanced, the feature extraction precision is improved, and the robustness of the model is enhanced.

The invention discloses a dangerous driving critical state identification method, and relates to the technical field of intelligent driving safety. According to the method, multi-mode signals of eye movement, electrocardio, skin electricity, vehicle operation and the like are collected and converted into a unified phase field, and the synchronous coherence of the unified phase field is analyzed; the individual phase dynamics manifold of the driver is learned on line by using a Shenchang differential equation, and a system instability precursor is identified by detecting the behavior that a state point escapes from a steady state attractor; further, multi-dimensional indexes such as synchronous collapse and topological fracture are fused, collapse time is estimated in combination with a Lyapunov index, and an advanced early warning instruction is generated; and finally, based on the model predictive control and the personalized phase response curve, generating and executing targeted multi-mode phase reset intervention, and forming a sensing-early warning-intervention active safety closed loop. According to the invention, normal form transformation from post-event alarm to beforehand regulation and control is realized, and early warning advancement and intervention accuracy are improved.

The invention relates to the technical field of automobile active safety, in particular to an intelligent brake pad abrasion measuring method and system based on multi-sensor fusion, and the method comprises the steps that S1, multi-source sensing signals are synchronously collected, and wheel speed pulse signals and brake pressure signals from a vehicle CAN bus are received; s2, multi-modal signal feature extraction: carrying out time domain filtering processing on the original thickness signal to form a thickness change feature vector, carrying out space partition calculation on the temperature distribution signal to form a temperature gradient feature matrix, and carrying out wavelet packet decomposition on the braking vibration signal to form a frequency domain energy feature set; s3, dynamically fusing decisions, outputting a joint feature vector, and generating a wear loss estimated value and a confidence index; and S4, performing Kalman filtering correction on the wear loss estimated value according to the confidence index to form final wear thickness data. According to the brake pad wear intelligent measurement method and system based on multi-sensor fusion, the problem that brake pad wear cannot be accurately monitored and intelligently diagnosed online in real time can be solved.

The invention relates to the field of automatic driving vehicle control, in particular to a sightseeing vehicle anti-collision control method and system based on a multi-mode radar. Comprising the following steps: acquiring a multi-modal radar original signal, and performing timestamp alignment and noise reduction processing to generate standardized fusion data; performing multi-target classification based on a convolutional neural network, and outputting a target recognition result in combination with Doppler frequency shift compensation and distance calculation; generating a hierarchical response strategy through dual-threshold comparison and environment interference dynamic weight adjustment; the strategy is converted into a PWM control signal, and a control instruction is generated through relay driving adaptation and hardware compatibility detection; a relay is driven in real time, motor response is collected, and execution effect evaluation and time sequence marking are completed; and brake release and system parameter synchronization are realized through PID closed-loop control. The problems of insufficient multi-target identification precision, poor environmental interference adaptability and response delay are solved, and the active safety performance of the sightseeing vehicle is remarkably improved.

The invention discloses an intelligent network connection automobile active safety teaching control method based on digital twinning. The method comprises the steps that S1, multi-source sensor data are collected and preprocessed to generate a driving state data set; s2, inputting the state data into a digital twin model to drive a traffic scene and outputting a synchronous state; s3, based on Dueling-DDQN, executing strategy learning to generate an active safety control instruction; s4, the instruction response effect is simulated and verified in the virtual environment; s5, collecting driver operation behaviors, inputting the improved MHA-BiLSTM model to extract time sequence features, and outputting behavior features; and S6, comparing the driving behavior with a standard instruction item by item, calculating an operation deviation and a response difference, and generating a personalized active safety teaching task. According to the invention, efficient comparison and teaching feedback of the driving behavior and the active control strategy can be realized, and the intelligent level of driving training is improved.

The invention belongs to the technical field of industrial vehicle active safety control, and particularly discloses an industrial vehicle safety anti-collision early warning method and device, and the method comprises the steps: building a unified space-time perception reference through fusing ultrasonic wave, inertial navigation, laser radar and camera data; extended Kalman filtering is fused with inertial navigation prediction and laser radar poses, and high-precision positioning in the tunnel is achieved. Constructing a high-dimensional time sequence feature vector, and predicting a future collision risk in combination with a Bi-LSTM model; the early warning threshold value is dynamically adjusted according to the load, the gradient and the sensor state; the method has the following advantages that the early warning accuracy and response speed under the complex working condition are remarkably improved, and the operation safety of the industrial vehicle is guaranteed.

The invention relates to the technical field of industrial vehicle safety control, in particular to a forklift safety online industrial monitoring management system which comprises a data fusion unit used for collecting kinematics data, position and attitude information and load distribution data of a vehicle and conducting space-time alignment processing on the collected data to generate a real-time state vector; the margin calculation unit is used for calculating a plurality of independent safety margin components and carrying out fusion processing based on the safety margin components to generate a comprehensive safety margin; the risk prediction unit is used for predicting a future safety margin value based on the time sequence of the comprehensive safety margin, and comparing and analyzing the future safety margin value with a preset safety threshold and a preset warning threshold to generate a risk level; the intervention control unit is used for generating and executing a corresponding hierarchical intervention strategy in response to the risk level; according to the invention, a complete technical link from multi-source data sensing to closed-loop control is constructed, and the conversion from passive safety to active safety is realized.

The invention provides a vehicle stability control method, device and system and electronic equipment, relates to the technical field of vehicle active safety control, and aims to construct a control framework based on a two-degree-of-freedom dynamical model and realize accurate control of the yaw velocity and the side slip angle of a vehicle. By introducing the road adhesion coefficient and combining the vertical load of each wheel for independent torque distribution, the tire adhesion utilization rate and the environmental adaptability and safety of the vehicle under different road conditions (such as ice and snow, wet and slippery conditions and dry conditions) are improved. According to the scheme, calculation is easy and convenient, response is rapid, good engineering practicability is achieved, and the robustness and adaptability of the system under the complex working condition are enhanced while the vehicle stability and the path tracking capacity are improved.

The invention relates to a safety protection method based on dynamic monitoring of circulating flow of heat source equipment. The method comprises the following steps: collecting pipeline pressure data, circulating water flow data and circulating water temperature data in a circulating pipeline of heat source equipment through a multi-source sensor; inputting the circulation pipeline data into a preset heat source equipment state model to perform working condition evaluation so as to generate an equipment working condition report; performing fault feature extraction on the equipment working condition report, and identifying an abnormal operation mode according to the extracted fault features; in response to the identified abnormal operation mode, determining a fault type matched with the abnormal operation mode according to a preset fault type library; and based on the determined fault type, generating a corresponding control instruction by using a preset risk-instruction mapping rule. According to the invention, real-time monitoring of the operation state of the heat source equipment, automatic identification of abnormal modes and active safety protection control are realized.

The invention belongs to the technical field of flight simulation training equipment control, and particularly relates to a full-motion flight simulator teacher seat adaptive control method, system and equipment. The problem that in the prior art, passive safety protection exists, high-frequency vibration interference exists, and dynamic working condition self-adaptive capacity is lacked, so that the posture imbalance risk of a teacher is caused is solved. The method comprises the steps that platform motion data, teacher skeleton key points and pressure distribution data are collected in real time, the current flight working condition is recognized after multi-source fusion processing, and the weight proportion of all data sources in deviation calculation is dynamically determined; by calculating the deviation between the weighted current posture of the instructor and the standard posture, a multi-direction self-adaptive adjustment instruction is generated to drive the seat to conduct displacement and angle adjustment, closed-loop adjustment is conducted on the basis of the residual deviation till the precision requirement is met, and active safety protection and intelligent self-adaptive adjustment of the seat are achieved.

The invention relates to a vehicle active safety control method and system, a storage medium and a vehicle, and belongs to the field of intelligent driving control. The vehicle active safety control method comprises the steps that driving state information is obtained in real time; calculating a longitudinal risk avoidance determination threshold based on the driving state information, and setting a first trigger condition based on the longitudinal risk avoidance determination threshold; if the longitudinal distance between the vehicle and the target vehicle meets the first triggering condition, an automatic emergency braking system is activated, and a first deceleration used for controlling longitudinal braking of the vehicle is generated; after the automatic emergency braking system is activated, a transverse risk avoiding judgment threshold value is calculated on the basis of the driving state information updated in real time, and a second triggering condition is set on the basis of the transverse risk avoiding judgment threshold value; if the longitudinal distance between the vehicle and the target vehicle meets a second triggering condition, an automatic emergency steering system is activated, and a second control instruction is generated; wherein the second control instruction is used for controlling the automatic emergency steering system to execute the cooperative steering angle and controlling the automatic emergency braking system to execute third deceleration cooperating with the cooperative steering angle.

The invention relates to the technical field of engineering machinery, and discloses an engineering machinery active safety control method and device based on multi-parameter analysis, and the method comprises the steps: collecting the multi-modal data of an environment where the engineering machinery is located based on a multi-modal data collection device, and constructing a dynamic model of the engineering machinery and the environment according to the multi-modal data, analyzing the multi-modal data through a dynamic model to obtain an environment data analysis result and an equipment data analysis result, determining an operation risk level of the engineering machinery according to the environment data analysis result and the equipment data analysis result, and determining an optimal working path of the engineering machinery according to the multi-modal data. And generating a safety control strategy according to the operation risk level and the optimal working path. Therefore, by implementing the method, the evaluation accuracy of the operation risk can be improved, the probability of misoperation of operators is reduced, the operation accuracy and reliability of the engineering machinery are enhanced, and the operation efficiency, the operation stability and the operation safety of the engineering machinery are further improved.

The invention relates to the technical field of automobile electronics and intelligent control, and discloses an intelligent cabin mechanical arm system which comprises a folding mechanical arm body, a tail end sensing interaction module, a driver state monitoring subsystem and a central control unit which are installed in a cabin. The central control unit controls the storage or working mode of the mechanical arm according to the comprehensive fatigue index of the driver; in the working mode, visual gazing information is used as a gating signal to activate electroencephalogram analysis, the operation intention is confirmed in combination with the visual prior probability and electroencephalogram signals, and the mechanical arm impedance parameters are adjusted in real time based on the fatigue state. According to the method, through Bayesian fusion of vision and electroencephalogram signals, the problem that single-mode control is unstable is solved; by means of variable impedance control and collision detection of the momentum observer, active safety protection based on the physiological state of the driver is achieved, and non-sensitive and high-safety intelligent auxiliary experience is provided in a limited cabin space.

The invention discloses a vehicle blind area dynamic enhancement and warning system for a low-light environment, and relates to the technical field of automobile active safety, and the system comprises a short wave infrared imaging module which is used for capturing a passive thermal feature image of a scene behind the side of a vehicle; the multi-source signal acquisition module is used for acquiring a vehicle bus signal representing a vehicle motion state and an operation intention of a driver in real time; the data processing and risk assessment module is connected with the short wave infrared imaging module and the multi-source signal acquisition module, and is used for performing fusion processing on the thermal feature image and the vehicle bus signal, identifying a blind area target and calculating a dynamic risk level of the blind area target; the grading warning and intervention module is connected with the data processing and risk assessment module; the short-wave infrared imaging technology which is sensitive to night light and high in anti-interference capacity is adopted, the imaging problem under extremely low illumination is fundamentally solved, and stable detection of vehicles and pedestrians in a long distance in a completely light-source-free environment is achieved.

The invention relates to an automobile windshield shelter removing method and system and an automobile, and belongs to the technical field of automobile active safety. The method comprises the steps that the surface of the windshield is monitored through a vehicle-mounted camera; when a shielding object is identified, calculating a view shielding coefficient based on the area, the position, the light transmittance and the type of the shielding object, and determining an initial influence level according to the view shielding coefficient; corresponding cleaning strategies are executed according to the influence levels, windscreen wiper cleaning is started at the low level, pneumatic suction generated by vehicle intersection is used for cleaning at the middle level, and negative-pressure airflow cleaning is induced through dynamic opening of a skylight at the high level; if the shielding is not removed, the influence level is increased step by step, and circulation execution is carried out until the shielding is removed or automatic parking is triggered. According to the method, grading, closed-loop and non-contact intelligent removal of different types of sheltering objects is achieved, and invalid or even harmful operation of a traditional windscreen wiper in a dry sheltering scene is effectively avoided.

The invention discloses a distributed driving electric vehicle path tracking and stability control method and system, and belongs to the technical field of electric vehicle active safety control. The method comprises the following steps: constructing a state-space equation based on a driver path tracking model and a vehicle three-degree-of-freedom motion model; establishing a dynamic envelope region based on a phase plane method, inputting the current parameters of the vehicle into the dynamic stable envelope region, and outputting a weight; constructing a controller based on the state-space equation, and taking the weight output by the dynamic stable envelope region as the control weight of the controller; inputting the vehicle state variables and the vehicle parameters into a controller, and outputting a longitudinal driving torque, an additional yaw torque, an additional front wheel turning angle and a driver intention turning angle required by driving; the additional front wheel turning angle and the driver intention turning angle are superposed to serve as input of a steering motor; a torque distributor is designed to distribute a longitudinal driving torque and an additional yaw torque, and the distributed torques are used as given values of the four hub motors.

The invention discloses a rollover risk pre-judgment and speed closed-loop control system and method integrating an IMU and an encoder, and relates to the technical field of vehicle active safety control. The system comprises an inertial measurement unit, an encoder, a controller, an execution mechanism and an alarm, the inertial measurement unit provides vehicle attitude data, and the encoder provides vehicle speed information. And the controller fuses the data in real time to calculate a vehicle rollover risk index, and sets a double-threshold criterion: when the risk is close to a threshold, an early warning is given, and when the risk exceeds the threshold, a deceleration control instruction is sent to the execution mechanism. The executing mechanism adjusts vehicle power output or braking accordingly, and automatic speed reduction is achieved. The controller forms a closed-loop control loop through feedback of the encoder, the speed reduction force is dynamically adjusted, and the speed of the vehicle is stably reduced to a safe range. Through the method, the rollover risk of the vehicle can be sensed in advance and actively intervened, rollover accidents are avoided, and the rollover-resistant safety of the vehicle is remarkably improved.

The invention belongs to the technical field of automobile seats, and provides a zero-gravity seat protection method and system based on collision risk prejudgment and application, and the method comprises the steps: monitoring collision risk information and vehicle state information in real time, carrying out the fusion processing of the collision risk information and the vehicle state information, carrying out the collision risk prejudgment, and generating an early warning signal when a judgment result meets any preset collision triggering condition; multiple zero-gravity seat active safety protection actions are executed in a millisecond level in parallel or under condition triggering, so that a seat backrest is reset to a safety protection posture from a zero-gravity posture through a synergistic effect, cooperative fixing of the posture of a passenger is synchronously completed, and the zero-gravity seat active safety protection actions are completed within 200 milliseconds; and when it is detected that the angle of the seat backrest reaches the target angle, the dual self-locking function is triggered, and locking of the seat backrest is completed. According to the invention, millisecond man-machine cooperation zero-gravity seat active safety protection can be carried out based on prospective collision risk pre-judgment, and the method has the advantages of low cost, high reliability and low false triggering rate.

The invention relates to the technical field of vehicle intelligent driving and active safety control, and discloses an active obstacle avoidance method and system considering multi-source uncertainty and fault positioning, and the method comprises the following steps: S1, predicting the tracks of a normal vehicle and a sideslip vehicle, and obtaining the predicted position and covariance of the normal vehicle and the sideslip vehicle; s2, constructing an extended state equation containing a positioning fault, and estimating and compensating a positioning deviation; s3, establishing a linear error tracking model, unifying various uncertainties into equivalent disturbance, and solving a disturbance invariant set; s4, generating a candidate obstacle avoidance track based on the compensated state; s5, track comfort is evaluated, hard constraints are checked, and infeasible tracks are removed; s6, transversely expanding the contour of the vehicle by using a disturbance invariant set, and carrying out longitudinal and transverse separation expansion on the contour of the target vehicle based on the predicted covariance; s7, performing robust collision detection by using the disconnect axis theorem; and S8, carrying out robust tracking control by adopting Tube-MPC. According to the invention, safe and stable obstacle avoidance control can be realized under complex disturbance and fault positioning conditions.

The invention discloses a multi-source data fusion vehicle anti-collision system based on a 4D millimeter-wave radar, which is applied to the technical field of vehicle active safety, and comprises a 4D millimeter-wave radar detection module used for collecting 4D information of targets around a vehicle and distinguishing a pavement target from an air interference target; the vehicle state detection module is used for synchronously acquiring running parameters of the vehicle at the same acquisition rate as that of the radar detection module; the data fusion processing module is used for carrying out fusion and noise reduction processing on the 4D information and the running parameters of the vehicle on the basis of a fusion algorithm of Kalman filtering and a DS evidence theory; the collision risk estimation module is used for calculating the collision time and the collision probability based on the fusion data and constructing a two-parameter risk level judgment model based on the collision time and the collision probability; and the anti-collision execution module is used for executing a corresponding anti-collision intervention action according to the risk level. The accuracy, the stability and the adaptability of the vehicle anti-collision system are effectively improved.

The invention provides an emergency call system, method and device based on AI vision and a storage medium, and relates to the technical field of active safety of intelligent network connection automobiles. The system comprises a camera module, a DMS module, an OMS module, an AI vision algorithm module, an AI vision application module and an eCall application module which work cooperatively. Aiming at the three defects that a traditional emergency call system depends on collision triggering, data dimensions are deficient and active protection is absent, DMS and OMS are innovatively and deeply integrated with the emergency call system, a multi-modal fusion decision mechanism is realized in combination with a layered AI visual architecture, and through deep cooperation of camera shooting, DMS, OMS, AI visual algorithm, AI visual application and an eCall application module, the multi-modal fusion decision mechanism of the emergency call system is realized. A full-link intelligent safety closed loop of sensing, decision making, control and rescue is constructed, early recognition, active intervention and accurate rescue of sudden diseases of drivers, serious fatigue disability and emergency physiological events of passengers are achieved, and meanwhile the rescue quality of collision accidents is improved.

The application relates to an expressway special section active safety prevention and control method based on edge computing, which comprises the following steps: S1, collecting vehicle driving data and traffic accident data of an expressway; S2, judging whether an abnormal running state occurs based on the vehicle driving data and the traffic accident data: if the abnormal running state occurs, judging an accident type and an accident severity, introducing a result into a risk accumulation diffusion method, simultaneously judging whether the accident occurs in an accident-prone area in combination with an accident-prone point identification model, performing risk dynamic evaluation, obtaining an accident influence range model, and thus determining the accident influence range; if the abnormal running state does not occur, judging whether the accident dissipates: if the accident dissipates, returning to S1 to update data; and if the accident does not dissipate, returning to S1 to update data; S3, determining a release scheme according to the risk evaluation result, and releasing through each release end. Compared with the prior art, the application has the advantages of risk prediction accuracy and reliable release scheme.

The invention discloses a fault diagnosis and fault-tolerant control method for in-situ steering of a distributed driving vehicle on a wet and slippery road surface, and aims to solve the problem of instability of the vehicle when a driving unit has faults such as driving failure, mechanical locking or excessive slipping. According to the method, the vehicle state is obtained in real time, the initial torque instruction is calculated, and online diagnosis and accurate recognition of the fault mode are achieved. Aiming at hardware faults such as driving failure, the system breaks and isolates a fault wheel; and aiming at excessive slipping, the torque is preferentially and actively reduced to recover the adhesive force, and temporary isolation is carried out after recovery failure. Once the fault unit is confirmed to be isolated, the system immediately starts a torque reconstruction module based on quadratic programming (QP), and the driving task is optimally redistributed to the remaining healthy wheels in real time so as to maintain the expected yaw moment of the whole vehicle. According to the strategy adopted by the invention, faults with different properties can be effectively distinguished and processed in a differentiated manner, and the control stability and active safety of the vehicle under the conditions of severe work and partial failure are remarkably improved.

The invention discloses a FanERC ship filling active safety disengaging device which comprises a left valve body, a right valve body and an air inlet connector, the left valve body and the right valve body are fixedly connected through a breaking bolt, a sealing cavity is formed in the connecting position after the left valve body and the right valve body are connected, and the air inlet connector is fixedly arranged on the left valve body and communicates with the sealing cavity. The gas inlet connector is connected with a nitrogen releasing device, after the nitrogen releasing device receives a releasing signal sent by the main control system, the nitrogen releasing device releases high-pressure nitrogen, the high-pressure nitrogen enters the sealing cavity through the gas inlet connector, the breaking bolt is thrusted and broken through high pressure, the left valve body and the right valve body are separated, and the left valve body and the right valve body automatically achieve self-sealing. The active separation mode does not need to depend on too large tension borne by the pipeline to break the bolt, impact and damage of the tension to the LNG conveying pipeline, the filling hose and other equipment can be reduced to the maximum extent, the LNG leakage risk caused by pipeline damage is reduced from the source, and the service life and safety of filling operation related equipment are guaranteed.

The invention discloses an unmanned aerial vehicle nest full life cycle management and control method, system, product and device, and relates to the field of unmanned aerial vehicle nests, and the method comprises the steps: employing a bidirectional access mechanism to carry out the bidirectional validity verification of an unmanned aerial vehicle and a nest, and generating an access safety certificate; integrating multi-dimensional data through edge computing nodes, and constructing a visual relation model and a dynamic behavior monitoring model; carrying out real-time risk identification and early warning by adopting a multi-dimensional feature fusion anomaly detection algorithm; the digital twinning technology is utilized to fuse aircraft nest operation data, and predictive maintenance is achieved through a life prediction model; and safely erasing the sensitive data of the equipment and cancelling the voucher to form a management closed loop. Through a bidirectional access mechanism, multi-dimensional anomaly detection, predictive maintenance and safe decommissioning processing, full-link active safety protection from access, operation, maintenance to decommissioning is realized, the safety and reliability of a machine nest system are improved, and the system can be applied to complex scenes with strict safety requirements such as electric power inspection.

The invention relates to a dynamic positioning switching control method for an automatic driving mine vehicle, and the method comprises the following steps: carrying out the control of the dynamic positioning switching of the automatic driving mine vehicle based on the dust concentration change rate data and the visibility change rate data of a historical set time period, and the first dust concentration change rate data and the first visibility change rate data of a current region; predicting second dust concentration change rate data and second visibility change rate data in a future set time period; and determining a risk level of the current area and a trigger condition corresponding to the risk level based on a preset area threshold table, and comparing the second dust concentration change rate data and the second visibility change rate data with the trigger condition. According to the method, the pre-switching process can be triggered before the sudden change of the dust concentration occurs, precious buffer time is gained for a system, mode conversion from traditional passive response to active pre-judgment and adjustment is achieved, the positioning interruption and out-of-control risks of a vehicle in the scene of the sudden change of the dust are reduced, and the active safety capacity of driving operation is improved.

The invention belongs to the technical field of vehicle risk assessment, and discloses a vehicle accident risk prediction method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring operation parameters of a plurality of vehicles, including load data, operation road type, gradient data, vehicle speed, driving behavior data, night driving time proportion, active safety configuration data, operation weather data, accident frequency and fault frequency; determining a plurality of risk assessment features; taking the risk assessment features as a training set, training a random forest model, and taking the random forest model as an accident risk prediction model when the performance of the random forest model reaches a preset requirement; and inputting the risk assessment features of the to-be-predicted vehicle into the accident risk prediction model, obtaining a survival function output by multiple trees of the accident risk prediction model, and determining the accident occurrence probability of the to-be-predicted vehicle within a preset duration according to the survival function. And the accident risk of the vehicle can be accurately predicted by using the multi-dimensional operation data of the vehicle.

The invention discloses a vehicle rollover risk assessment method, a vehicle rollover control method and a vehicle rollover risk assessment system, and relates to the technical field of vehicle active safety. The evaluation method comprises the following steps: acquiring an acquisition value of a first parameter of a vehicle in a first time step; inputting the collection value of the first parameter into a prediction model to obtain a prediction value of a second parameter under a second time step; according to the predicted value of the second parameter, constructing a risk measurement index based on a differential energy Lyapunov index; and evaluating the rollover risk of the vehicle according to the value and the change trend of the risk measurement index. According to the technical scheme, the vehicle rollover problem is abstracted into a stability problem of a dynamic system, a provable theoretical basis is provided for risk assessment and control, higher generalization ability and robustness are achieved, and the control accuracy and the adaptability to complex working conditions are remarkably improved.

The invention provides a forward collision risk processing method and device, equipment and a medium, and relates to the technical field of auxiliary driving, and the method comprises the steps: forming a forward target sequence through synchronously obtaining the speeds, accelerations and vehicle distances of a vehicle and a plurality of front vehicles, and constructing a relative speed, relative distance and longitudinal acceleration time sequence according to a time dimension; calculating nominal collision time and a time margin based on the motion relation between the two vehicles, and introducing a motorcade compression index and a deceleration wave intensity index to correct the nominal collision time so as to obtain effective collision time; collision hazards are quantified according to a collision relative speed, a compression index and a deceleration wave intensity index, a controllability score is constructed in combination with effective collision time and a time margin, then a severity score and the controllability score are mapped into discrete risk levels, and differential active safety measures such as transverse avoidance are triggered according to different risk levels. The method can simultaneously consider the longitudinal compression trend of the motorcade and the deceleration wave cumulative effect to trigger the evasion strategy.

The invention relates to a multi-modal active safety margin evaluation and checking method and device and a storage medium, which are applied to the technical field of electric power spot market, and the method comprises the steps: constructing a state judgment-index self-adaption evaluation framework, and when a system is in a risk state, calculating an interval overlapping index to quantify the risk exposure degree; when the system is in a safe state, a safe distance index is calculated to quantify safe redundancy, the multi-modal index system overcomes the defect that information is unilaterally evaluated by a traditional single index, refined and differentiated evaluation on the degree of risk and the degree of safe redundancy is achieved, and more comprehensive information support is provided for scheduling decision making; by comparing the calculated index with the preset threshold value, an active checking signal which can be directly fed back to a market clearing model is generated, or a clear suggestion is provided for a dispatcher, so that the problem of disjunction between evaluation and decision in the prior art is solved, and integrated intelligent cooperation from state perception, quantitative evaluation to closed-loop correction is realized.