314 results about "Driving risk" patented technology

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

The invention discloses a vehicle driving right transfer method based on abnormal driving behaviors and electronic equipment, and the method comprises the steps: collecting driver state data, vehicle driving state data and road environment data, and carrying out the multi-source data fusion; abnormal driving behavior detection is performed according to the multi-source data, and the driving risk is evaluated; a rule engine decision-making model based on the situation is used for deciding countermeasures to be taken, and the countermeasures comprise early warning type actions, auxiliary control type actions, forced take-over type actions and mutual switching among the actions based on an advanced auxiliary driving system or an automatic driving system; after the vehicle is forced to take over and runs for n minutes, whether the current vehicle is suitable for the driver to take over or not is judged according to driver state data collected in real time and the current comprehensive driving risk score, and whether driving right transfer is conducted or not is determined according to the judgment result. The accuracy of abnormal driving behavior detection is improved, the vehicle driving right is intelligently transferred, and the driving safety is improved.

The invention relates to the field of automatic driving and traffic safety, in particular to a fine-grained multi-task driving risk prediction method fused with a trajectory prediction auxiliary task. Comprising the following steps: step 1, automatically labeling risk labels based on trajectory data; 2, designing a main task of the driving risk prediction model; 3, auxiliary task design of the driving risk prediction model; and 4, constructing and training a driving risk prediction model. An experiment result based on a disclosed NGSIM data set shows that the precision and robustness of a driving risk prediction model can be remarkably improved by introducing trajectory prediction as an auxiliary task.

The invention relates to an automatic driving safety behavior decision-making method with interactive risk consciousness, which belongs to the technical field of automatic driving behavior decision-making, and comprises the following steps: S1, extracting the state of a vehicle and driving environment information based on sensor data; s2, defining a state space and an action space of deep reinforcement learning; s3, adopting a D3QN reinforcement learning algorithm to construct a decision framework and integrating a self-attention mechanism to capture multi-vehicle interaction features; s4, establishing a driving risk potential field model considering multi-vehicle interaction according to the multi-vehicle interaction characteristics and the motion state and the relative position of the environment vehicle; s5, constructing a comprehensive reward function based on the driving risk potential field; s6, performing network training by adopting an experience playback mechanism; and S7, deploying a risk action correction module at the output end of the value network.

The invention discloses a vehicle driving risk prediction method and device in an off-road environment based on a large model, and relates to the technical field of off-road risk prediction, and the method comprises the steps: obtaining current vehicle state data and current environment perception data; determining a target multi-axis rollover risk index based on the current vehicle state data; determining a target lateral instability index based on the current vehicle state data and the current environment perception data; fusing the target multi-axis rollover risk index and the target lateral instability index to generate target risk level data; based on the current vehicle state data, the current environment perception data and the target risk level data, generating a target operation recommendation sequence through a large cross-country decision model; and outputting risk early warning information based on the target operation recommendation sequence.

The invention relates to the technical field of image or video recognition or understanding, and discloses a bus driver driving state evaluation method, which comprises the following steps: acquiring a face image, an electrocardiosignal and voice audio data of a bus driver, performing timestamp alignment and preprocessing, and constructing a multi-mode driving state data set; extracting multi-modal features of the collected data, and mapping the multi-modal features to a unified feature space; fusing multi-modal features through a cross-modal attention mechanism, dynamically adjusting attention weight based on an emotional change difficulty index, and extracting emotional change key features; and predicting a two-dimensional continuous emotion value of the driver by using the fusion features, calculating a long-time-sequence emotion driving risk score based on an emotion stimulation dynamic model, and performing evaluation and early warning of a driving state. The problems of single-mode analysis, lack of long-period early warning and driving state static recognition in the prior art are solved, and the purposes of accurate evaluation, high safety, multi-mode fusion and long-time-sequence prediction are achieved.

The invention discloses a remote driving risk early warning method based on communication time delay perception, which comprises the following steps: firstly, collecting related data of a target communication link, carrying out preprocessing and feature extraction on the data, fitting a nonlinear relationship between time delay and multiple features based on an extreme gradient lifting XGboost algorithm, and predicting a change condition of the time delay in real time; constructing a communication risk evaluation model, and quantifying a communication risk value based on the time delay change condition; predicting a vehicle trajectory according to the vehicle dynamics model and the driver execution instruction sequence; calculating a lane departure risk and an obstacle collision risk based on the vehicle trajectory, the environmental risk value being the sum of the two; and comprehensively considering an environment risk value and a communication risk value, and judging whether to trigger an alarm according to a designed safety threshold. According to the method, an environment risk evaluation mode is redesigned on the basis of considering the communication time delay, and the environment risk and the communication risk are comprehensively incorporated into an early warning framework, so that better safety guarantee is provided for a remote driving system.

The invention belongs to the technical field of software engineering, particularly relates to an automatic driving system performance enhancement method based on an expert hybrid architecture, and aims to solve the core problems that an end-to-end automatic driving system is confronted with semantic fuzziness to cause unreliable decision, multi-task interference hinders optimization planning, too long reasoning delay increases driving risks and the like. According to the method, an ExpertAD framework is provided, task key features are amplified through a perception adapter (PA), and the relevance of scene context understanding is guaranteed; related driving tasks are dynamically activated through a sparse expert mixture (MoSE), and task interference is minimized; and in combination with a customized training loss function, collaborative optimization of planning effectiveness and reasoning efficiency is realized. Experiments show that compared with an existing method, the method has the advantages that the average collision rate is reduced by 20%, the reasoning delay is reduced by 25%, higher multi-skill planning capacity is achieved in rare scenes (such as accident handling and first-aid vehicle avoiding), and good generalization is achieved for unseen urban environments.

To provide a small mobility in which the risk of runaway caused by switching of a traveling mode is effectively suppressed.SOLUTION: Provided is a small-sized mobility 1 having a first mode and a second mode as travel modes, the second mode having a higher maximum speed than the first mode, the small-sized mobility 1 including a first sensor 11 configured to detect that a driver is in the small-sized mobility 1 and a second sensor 12 configured to detect that the driver is holding a steering wheel, the small-sized mobility 1 being capable of switching the travel mode from the first mode to the second mode on condition that the first sensor 11 and the second sensor 12 are in a detection state.SELECTED DRAWING: Figure 1

The invention discloses a highway automatic driving risk identification system based on a multi-mode AI model, and relates to the technical field of risk identification. The information acquisition module is used for setting a judgment range by taking a vehicle as a judgment midpoint, acquiring speed information of a target vehicle in the judgment range based on the judgment range to obtain a first data set, and acquiring swing information of the target vehicle in the judgment range to obtain a second data set; according to the invention, systematic risk assessment is realized through the dynamic judgment range and multi-source data fusion of the information acquisition module, the judgment range is dynamically adjusted based on the initial value in combination with the real-time speed and the road width, and it is ensured that a key area around a vehicle is covered; speed information and swing information of a target vehicle are collected within a fixed window value, a first data set and a second data set are generated, and multi-dimensional environmental data is provided.

The invention provides a multi-scene driving risk assessment method based on transfer learning. The method is suitable for real-time assessment and short-time early warning of driving risks in tunnel and non-tunnel environments. According to the method, a multi-source data set collected by multiple sensors is constructed based on a natural driving test, static and dynamic characteristics are extracted by using a sliding time window technology, and the driving risk is evaluated through a multi-scene driving risk evaluation model. The scene driving risk assessment model integrates a static information encoder, a variable selection network and an interpretable multi-head self-attention mechanism, has strong feature selection and time sequence modeling capabilities, and improves the adaptability and generalization capability to scarce tunnel data through a transfer learning strategy. Test results show that the method is superior to the existing mainstream model in the aspects of alarm rate, accuracy rate and false alarm rate, and is suitable for a risk assessment and early warning module in an intelligent traffic system.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving risk perception and trajectory planning method fusing vehicle state prediction. Comprising the following steps: collecting historical motion information of a barrier vehicle and surrounding vehicles and road structured information, and constructing spatial-temporal feature representation based on the historical motion information of the vehicle; a vehicle state prediction model is constructed in combination with Transform and a multi-task learning framework, and prediction of the future motion state of the vehicle is achieved; then, respectively constructing a road risk field model and an obstacle vehicle risk field model based on an artificial potential field theory, and constructing a comprehensive risk field model through a weighted stacking mode; jointly forming a future comprehensive risk field by the obstacle vehicle risk field in the future time period and the global road risk field; and adopting a quintic polynomial to generate a candidate trajectory cluster of the autonomous vehicle, screening out a low-risk trajectory set meeting a safety threshold, and introducing a comfort evaluation function to obtain an optimal planning trajectory.

The invention discloses a public transport vehicle and driver joint scheduling method based on safety consideration, and the method comprises the following steps: S1, building a vehicle scheduling network flow model related to the travel of public transport vehicles based on a space-time network framework, and considering the constraint condition of a multi-parking-lot public transport vehicle scheduling problem; s2, based on survival analysis, according to historical bus travel information and driver driving risk early warning information, establishing driver survival functions under different driving characteristics; s3, designing a driving section cutting strategy and a driving section connection strategy for a vehicle task chain in the vehicle scheduling network flow model, and generating a feasible driver shift plan; s4, matching the driver survival function with the driver shift plan, and establishing a vehicle and driver joint scheduling model by considering the constraint condition of the driver scheduling problem; and S5, performing rolling solution on the vehicle and driver combined dispatching model by using a heuristic algorithm to obtain a vehicle and driver combined dispatching scheme based on safety consideration.

The invention provides a driving early warning method and device under a traffic accident condition, electronic equipment and a storage medium, and the method comprises the steps: obtaining the driving information of a current vehicle, and the driving information and the collision point location of the traffic accident vehicle under the condition that a traffic accident occurs on a driving route of the current vehicle, and obtaining a driving early warning result based on the driving information and the collision point location of the traffic accident vehicle; determining lost object information of the lost object in the traffic accident, and determining the driving influence of the lost object in the traffic accident on the current vehicle based on the driving information and the lost object information of the current vehicle; based on the driving influence, driving early warning is carried out on the current vehicle, the defect that large driving potential safety hazards exist due to the fact that effective safe driving measures are lacked in the current traffic accident scene is overcome, lost object estimation and driving influence evaluation are achieved through the driving information of the current vehicle and the driving information and the collision point position of the traffic accident vehicle, and the driving safety of the current vehicle is improved. Therefore, driving risks in a traffic accident scene can be avoided, and driving safety is protected.

The embodiment of the invention discloses a vehicle-mounted screen control method, a vehicle and a storage medium, and the method comprises the steps: obtaining the driving risk feature data of the vehicle under the condition that the vehicle enters an automatic driving mode; controlling the main driving display area to display basic driving information related to driving safety under the condition that the vehicle is determined to be in a normal operation stage based on the driving risk characteristic data, and controlling the main driving display area to display the basic driving information related to the driving safety under the condition that a first instruction input by a main driving user through the main driving display area is received, controlling a main driving display area to display basic driving information and first display content corresponding to the first instruction; and under the condition that a second instruction input by the co-driver user through the co-driver display area is received, the co-driver display area is controlled to display second display content corresponding to the second instruction. According to the embodiment of the invention, the unification of safety and user experience is achieved, and the maximization of space utilization and interaction efficiency is also realized, so that the utilization efficiency and interaction richness of the cabin space are remarkably improved on the premise of ensuring safety.

The present application is applicable to the technical field of artificial intelligence. Provided are a method for training a driving risk recognition model, and a driving risk recognition method and apparatus. The method comprises: acquiring data to be processed of a plurality of drivers; determining corresponding events on the basis of the data to be processed; textualizing identifiers of the drivers and the events corresponding to the drivers to obtain textualization results; and on the basis of the textualization results, training a driving risk recognition model to be trained to obtain a trained driving risk recognition model. The driving risk recognition model to be trained is a large language model, and the trained driving risk recognition model is used for outputting a driving risk. The model trained by means of the above method is more accurate.

The invention provides a driving assistance device, a driving assistance method, a computer program product, and a driving assistance system, which output a notification indicating that a travel position is close to a dangerous area set on a map when a driving risk level is equal to or greater than a predetermined value. A smartphone is provided with: a biological information acquisition unit (111) that acquires biological information of a driver; a driving behavior acquisition unit (113) that acquires driving behavior information indicating the driving behavior of the driver; a risk evaluation unit (114) that evaluates, on the basis of the biological information and the driving behavior information, a driving risk level indicating the level of risk when the driver is driving the vehicle; an area setting unit (115) that sets a dangerous area for the map information when the driving risk level is equal to or greater than a predetermined value; a position acquisition unit (116) that acquires the travel position of the vehicle on the map; and a notification unit (117) that outputs a first notification indicating that the travel position is close to the danger area when the travel position is close to the danger area.

The application discloses a kind of fatigue driving early warning method, vehicle controller and vehicle, belong to vehicle technical field, based on front road information whether the front of vehicle exists risk section, after determining that the front of vehicle exists risk section, from the state information of driver determines whether driver is in fatigue state, when determining that driver is in fatigue state, it can be predicted that the driving risk of driver in risk section is larger, then through vibration sensor obtains road vibration information in the process of vehicle travel, utilize electric control suspension system to be enlarged to road vibration information in proportion after directly transferred to the carriage of vehicle, trigger carriage vibration, and the frequency, amplitude and time of carriage vibration and other parameters are matched with the current road vibration information of vehicle, so that carriage vibration forms relatively unique vibration scheme, and then different from conventional and repeated wake-up scheme is used to wake up driver, avoid the tolerance ability of driver to wake-up scheme, improve wake-up effect.

The invention provides a cluster intelligent scheduling method and system for building material transport vehicles, relates to the technical field of cluster intelligent scheduling, and aims to provide a cluster intelligent scheduling method and system for building material transport vehicles based on traffic efficiency data, traffic density data and weather data of urban roads where the building material transport vehicles need to pass in the transport process. Determining a target driving road section and a driving parameter combination of each building material transport vehicle; establishing a data interaction channel among the dispatching center, each building material transport vehicle and the roadside equipment corresponding to each target driving road section; driving related data corresponding to the building material transport vehicles are collected and analyzed, and risk type identifiers and risk assessment results of the building material transport vehicles are formed; and the driving risk information of the risk transport vehicle with the risk assessment result of the driving risk is transmitted to the dispatching center and the transport vehicles on the same road section, so that standby resource allocation is performed through the dispatching center based on the driving risk information and the risk type identifier, cluster intelligent dispatching is realized, and the overall safety and operation toughness of the dispatching system are improved.

The invention discloses a multi-dimensional spiral tunnel entrance driving risk early warning system. Real-time monitoring, risk assessment and early warning intervention of vehicles at a tunnel entrance are realized through video monitoring, radar sensing and intelligent computing technologies. Through video and radar data fusion, the license plate, the vehicle speed, the vehicle distance and the line pressing state are accurately extracted; a multi-task identification network and LaneNet are combined with an RANSAC algorithm to improve the lane line detection precision, and a line pressing behavior is judged through a dynamic threshold value. The method further introduces an EWM + CRITIC risk assessment model, quantifies dangerous behaviors such as high-frequency speed change, abnormal yaw, risk following and aggressive lane change, divides risk levels and triggers early warning. Audio-visual linkage intervention is carried out on high-risk vehicles and the system, the accuracy and response efficiency of tunnel entrance traffic risk identification are effectively improved, and reliable support is provided for intelligent traffic safety management.

The invention discloses a city-level intelligent parking dynamic scheduling and driving risk early warning method and system, and the method is executed by a city-level intelligent parking platform, and comprises the steps: obtaining vehicle behavior and parking state data collected by a sensing device, and weather, traffic flow and closed parking lot data; fusing multi-source data to construct a dynamic parking resource database; based on the database, utilizing an AI model to dynamically evaluate regional parking pressure and predict future parking demand distribution; meanwhile, weather, traffic flow, road structure and real-time parking pressure data are fused, and the driving risk level is quantified through a multi-factor risk assessment model; and according to the prediction and evaluation result, generating a parking guide strategy and driving risk early warning information, and issuing the parking guide strategy and the driving risk early warning information to the vehicle owner terminal. According to the invention, accurate prediction and active scheduling of the parking resources are realized, forward-looking early warning can be carried out on composite traffic risks including secondary risks caused by parking saturation, and the urban parking efficiency and the road safety level are effectively improved.

The invention discloses an automatic driving risk assessment method and device, a vehicle, equipment and a medium, and belongs to the technical field of automatic driving. The method comprises the following steps: respectively acquiring a visual image and a radar reflectivity graph of a target road surface; after time synchronization and space calibration are carried out on the visual image and the radar reflectivity graph, visual features of an area where a lane line is located are extracted from the visual image by adopting a convolutional neural network, a target reflectivity area higher than a reflectivity threshold in the radar reflectivity graph is identified, and reflectivity distribution features of the target reflectivity area are extracted; fusing the visual features and the reflectivity distribution features according to time synchronization and space calibration results to obtain a lane line comprehensive feature set; generating a lane line sensing result according to the lane line comprehensive feature set; and matching is performed from the risk level relationship according to the aging degree of the lane line and the complexity of the road surface, and the target risk level corresponding to the lane line modeling information is obtained, so that the safety of automatic driving is improved.

The application provides a secure encryption method, device, system, equipment and medium for vehicle information, comprising: obtaining first feature information of a smart terminal and second feature information of a vehicle terminal after establishing communication with the smart terminal; determining the communication state of the vehicle terminal and the smart terminal, and constructing an encryption key according to the communication state and the characters of the first feature information and the second feature information; calling the encryption key to encrypt the interactive vehicle information to obtain encrypted information. The application can construct a specific encryption key for each smart terminal based on the feature information of the vehicle terminal and the smart terminal, and can encrypt by using the specific encryption key, so as to reduce the risk of information cracking or leakage, improve the security and privacy of information, and ensure information security. Meanwhile, the control safety of the vehicle terminal can also be improved by interacting with the encrypted information, and the driving risk of the user is reduced.

The invention discloses a bicycle driving risk early warning method and system based on visual identification, and relates to the technical field of visual identification. The method comprises the following steps: acquiring driving sensing data through a sensing module carried on a bicycle body; identifying a plurality of dynamic traffic participants based on the visual perception data, and predicting future behavior intentions and motion trails; carrying out risk assessment by fusing the predicted behavior intention, the motion trail and the running state data of the bicycle, and constructing a risk perception grid which comprises a traffic event, a participant, a risk level and a movement tracking state; and based on the risk perception grid, identifying the traffic event with the risk level or the mobile tracking state reaching an early warning threshold value and the positioning space, and generating early warning feedback information. The technical problems of insufficient bicycle driving risk identification and untimely early warning in the prior art are solved, and the technical effects of driving risk active perception and timely early warning based on visual prediction are achieved.

The invention provides an automobile seat sitting posture recognition method and system based on machine vision, and relates to the technical field of automobile seats.The method comprises the steps that pressure data and pressure distribution characteristics are collected in real time, and the existence condition of seat passengers is judged; passenger image data acquisition is carried out; the passenger image data, the pressure data and the pressure distribution features are subjected to preprocessing and feature extraction, and passenger types are analyzed; passenger sitting posture recognition is carried out; determining the danger level of the current riding posture; and according to the risk analysis result, generating a corresponding safety coping decision. Through multi-dimensional fusion perception of the pressure data and the image data, accurate judgment and dynamic monitoring of the existence condition of a seat passenger are realized, and the system operation efficiency is improved; through a three-level response mechanism and an active intervention strategy, the warning and correction mode can be dynamically adjusted according to the danger level, the vehicle riding safety is remarkably improved, and the driving risk caused by bad sitting postures is effectively reduced.

A driver monitoring device includes: a biometric information recognition portion that recognizes biometric information of a driver of a moving body; a driving behavior recognition portion that recognizes driving behavior of the driver, who is driving; a driving risk recognition portion that recognizes, using a predetermined recognition method, a driving risk indicating a risk level of movement of the moving body due to driving of the driver, based on at least one of the biometric information of the driver and the driving behavior of the driver, when the driver is driving the moving body; and a driving risk recognition method switching portion that switches the recognition method used when the driving risk recognition portion recognizes the driving risk at a next recognition timing, according to the driving risk recognized by the driving risk recognition portion.

The embodiment of the invention relates to the field of open pit coal mine transportation safety, and discloses a dynamic early warning method, system and device for the driving risk of a mining truck and a medium. The dynamic early warning method for the driving risk of the mining truck comprises the steps that driving information of a vehicle is collected in real time based on a vehicle-mounted positioning system, the driving information comprises driving time, a geographic position, a driving state and topographic features; extracting key risk features for reflecting driving risks based on the driving information, wherein the key risk features include dangerous driving behavior features and terrain-related features; and constructing a risk prediction model based on the key risk features so as to carry out classified early warning on the driving risk of the vehicle. According to the technical scheme disclosed by the invention, the limitation of a single data dimension is broken through, the evaluation comprehensiveness is improved, and real-time perception, multi-dimensional analysis and accurate early warning of the driving risk are realized.

The invention relates to a driver departure control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring driving behavior data of a current driver, determining a driving risk score of the current driver according to the driving behavior data, acquiring passenger evaluation data of the current driver, determining a service quality score of the current driver according to the passenger evaluation data, acquiring historical operation data of the current driver, and determining a service quality score of the current driver according to the historical operation data; according to the historical operation data, determining a revenue efficiency score of the current driver, according to the driving risk score, the service quality score and the revenue efficiency score of the current driver, determining a driving strategy of the current driver, the driving strategy including a driving address and driving time, and sending the driving strategy to a terminal corresponding to the current driver. Therefore, the current driver is controlled to leave. By adopting the method, different drivers can be guided to select proper time periods and go to proper places to receive orders, so that the waiting time of the drivers is shortened, and the order receiving success rate of the drivers is improved.

The application discloses a path tracking control method for a man-machine co-driving type intelligent vehicle, and steps are as follows: a vehicle path tracking error model is established, and a hierarchical control architecture is constructed, the hierarchical control architecture comprising an upper controller and a lower controller; the upper controller is configured to: in a human driving mode, generating a first front wheel steering angle through a two-point preview driver model, and in an automatic driving mode, generating a second front wheel steering angle through an automatic driving controller fusing a radial basis function neural network, a heuristic reinforcement adjustment mechanism and a non-singular fast terminal sliding mode control; the lower controller is configured to: driving a finite state machine to switch the driving mode based on a driving risk assessment index, and performing weighted fusion on the first front wheel steering angle and the second front wheel steering angle in a driving authority transition stage to output a final front wheel steering angle control instruction; the application solves the contradiction between path tracking precision and chattering, switching safety and comfort, and is suitable for man-machine collaborative control of an intelligent vehicle under multiple working conditions.