5096 results about "Vehicle driving" patented technology

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention discloses an automatic driving control method based on passenger motion sickness perception and comfort, which comprises the following steps: acquiring and preprocessing vehicle dynamic performance data, passenger physiological signals and subjective scores, extracting dynamic characteristics such as linear acceleration, angular velocity, jerk and omnidirectional acceleration, and physiological characteristics such as electrocardio and galvanic skin; feature fusion and standardization are carried out; a passenger motion sickness perception and comfort score prediction model is constructed; an individualized motion sickness threshold value is constructed based on a model output result, a motion sickness risk score is calculated in combination with future short-time vehicle motion characteristics, and perspective estimation of the motion sickness risk is achieved; and executing a hierarchical processing strategy according to the sensitivity typing and the risk level, and dynamically adjusting the vehicle driving style, the sound-light-temperature environment in the vehicle and the voice prompt. According to the automatic driving control method based on the motion sickness sensing and comfort of the passenger, real-time identification and active intervention of the motion sickness state of the passenger can be achieved, and the riding comfort and the man-machine cooperation level of an automatic driving vehicle are improved.

The invention discloses a large language model enhanced reinforcement learning training method for key automatic driving scene stable decision, and the method comprises the steps: constructing an LLM-based vehicle decision intelligent agent, and providing a high-level guidance strategy through a chain thinking technology and a knowledge experience pool; then, jointly constructing a dynamic intervention mechanism based on the environmental risk sign and the decision boundary parameter, thereby determining an LLM guidance opportunity; finally, through expert guidance algorithm design, the prior knowledge of the LLM is effectively integrated into the deep reinforcement learning strategy network by adopting an adaptive exploration-imitation fusion loss function. The DRL model is better in performance in a vehicle driving decision-making task and shows higher generalization ability in various scenes, and the robustness, the sample efficiency and the overall generalization level of the DRL model can be remarkably improved.

The invention discloses a curve driving collision early warning method and system based on multi-risk field fusion, and the method comprises the steps: 1, collecting vehicle dynamics data in real time, and carrying out the short-time prediction; step 2, geometric modeling and rasterization of a road; 3, constructing a geometric risk field; 4, constructing a kinetic energy risk field; 5, constructing a behavior risk field; step 6, constructing a comprehensive risk field; step 7, collision risk judgment; and step 8, generating an active intervention strategy. Collaborative perception and risk prediction of vehicles, roads and driving behaviors are realized through multi-risk field fusion, and potential collision hidden dangers of curve driving are identified in advance; a vehicle dynamics model and risk correction based on road adhesion are introduced to realize accurate risk assessment of the vehicle; multi-time window trajectory prediction and behavior modeling are adopted, so that the recognition capability of complex driving behaviors is improved; a closed-loop system from risk prediction and decision making to active control is realized, and the safety and stability of the vehicle driving in the curve scene are effectively improved.

The invention provides a vehicle driving speed planning method and device, a vehicle, a medium and a program product, and belongs to the field of automatic driving. The vehicle driving speed planning method comprises the steps that all obstacles around a target vehicle are traversed, and the types of the obstacles are determined based on the relative position relation between the obstacles and a target vehicle driving lane; calculating a first target vehicle speed according to the obstacle type and updating a global target vehicle speed; acquiring an obstacle prediction track, and if the confidence coefficient of the prediction track meets a preset requirement, determining a collision risk point based on the prediction track and the target vehicle planning path; calculating a second target vehicle speed based on the collision risk point and updating the global target vehicle speed; and after the traversal is completed, outputting the minimum global target vehicle speed in the traversal process as the reference vehicle speed. According to the technical scheme provided by the invention, the risk assessment based on the lane relationship and the collision risk assessment based on the predicted trajectory are fused, and the driving safety and comfort of the automatic driving vehicle in a complex dynamic scene can be improved.

The invention provides a mountainous road driving assistance method and device and a vehicle, and relates to the technical field of vehicles. The method is based on a vehicle carrying an unmanned aerial vehicle. The method comprises the following steps: in response to a mountain road navigation instruction, controlling the unmanned aerial vehicle to acquire traffic information about a mountain road where the vehicle is located; constructing a three-dimensional terrain model about the mountain road according to the traffic information; and generating driving path suggestion information and / or early warning information according to vehicle positioning information and the three-dimensional terrain model. On the basis of the vehicle carrying the unmanned aerial vehicle, the traffic information of the mountain road where the vehicle is located is obtained timely and accurately, the three-dimensional terrain model about the mountain road is constructed, and the driving path suggestion information and / or early warning information are / is generated, so that active guidance and risk prompt for the driving behavior of a driver are achieved, and the driving safety of the driver is improved. And the driving safety of the vehicle on the mountainous road and the driving experience of a driver are 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 embodiment of the invention provides a vehicle driving track prediction method and system and electronic equipment. The method comprises the steps that vehicle driving information corresponding to a target vehicle is determined; obtaining aerial view feature information according to the vehicle driving information; determining query feature information, semantic segmentation information and a target object detection result according to the vehicle driving information and the aerial view feature information; determining predefined anchor point information, and obtaining first driving track information corresponding to the target vehicle according to the predefined anchor point information and the query feature information; and obtaining more accurate second driving track information corresponding to the target vehicle according to the aerial view feature information, the query feature information, the semantic segmentation information, the target object detection result and the first driving track information. Therefore, based on a strategy from coarse to fine, the relatively rough first driving track information can be converted into the relatively accurate second driving track information, so that a more accurate vehicle driving track is obtained, and the accuracy of the vehicle driving track is improved.

The invention discloses an intelligent chassis preview control system and method based on a roadside sensor, and the system comprises a roadside sensing unit which is used for obtaining the road condition information of a line road, and transmitting the road condition information to a roadside communication base station; the roadside communication base station is used for preprocessing the road condition information and packaging the road condition information into a set format; the vehicle-mounted communication module is used for sending the road condition information with the set format to the vehicle-mounted controller; the vehicle-mounted controller is used for performing fusion processing on the road condition information and the vehicle state data, and analyzing the fused data by adopting a preview control algorithm model to generate a control instruction of the intelligent chassis executing mechanism; and the intelligent chassis executing mechanism is used for performing preview control on the vehicle chassis based on the control instruction. The method has the advantages that pre-judgment adjustment of the vehicle chassis is achieved, the safety and stability of vehicle driving are effectively improved, and the system reliability is enhanced especially under complex road conditions and severe weather conditions.

Methods for training a series of neural networks to output driving behavior control parameters is disclosed. The training dataset for the neural networks includes sensor-based vehicle driving recordings that may be categorized by geographical area, by qualitative driving behaviors, or by some combination, such that various training data subsets are used to train the series of neural networks. By learning either city-specific driving behavior control parameters, qualitative driving behavior specific driving behavior control parameters, or both, the resulting parameters may then be provided to a motion planning model for use in modeling predictive control for an autonomous vehicle. Rather than relying on XYZ trajectories of agent vehicles when planning future trajectories of the ego vehicle, the motion planning model is adaptive, due to the use of the learned driving behavior control parameters.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention discloses a vehicle driving planning method and device, electronic equipment and a storage medium. The method comprises the steps that multi-mode sensing data and vehicle environment data are acquired; the multi-modal perception data comprises multiple perception data of the vehicle; determining a fusion weight based on the vehicle environment data and the plurality of perceptual data; fusing the multiple pieces of sensing data based on the fusion weight to obtain fused environment sensing data; determining a scene danger level based on the multiple pieces of perception data and the fused environment perception data; and performing automatic driving planning based on the scene danger level and the fusion environment perception data. In the embodiment of the invention, the problems of poor environmental adaptability and insufficient static fusion robustness of a single sensor are solved, and the accuracy of environmental perception in a complex scene is improved; the scene grading decision improves the response efficiency in an emergency scene, reduces the collision risk, and enhances the safety of automatic driving.

The invention discloses a road surface friction coefficient estimation method, device and equipment. The method at least comprises the steps that image data, collected by a vehicle camera, of a driving road surface and point cloud data, collected by a first sensor, of the driving road surface are acquired; determining a target fusion feature at least based on the image data and the point cloud data; the target fusion feature is a multi-dimensional pavement feature related to a friction coefficient; at least processing the target fusion feature through an estimation model, and determining a target friction coefficient of the vehicle driving road surface; the estimation model is a trained model used for determining the friction coefficient at least based on the fusion features. The road surface friction coefficient estimated by the scheme is more accurate.

The embodiment of the invention provides an automobile gateway dynamic priority routing method and system. The method comprises the following steps: acquiring associated data of a running vehicle in real time; on the basis of driving data clustering analysis, feature sets of different driving scenes and scene conversion rules among different driving scenes are determined, the feature vectors and the feature sets are compared, and the driving scene and decision priority of the vehicle are determined on the basis of the operation confidence of a comparison result; detecting whether the current feature vector meets a scene conversion rule or not during vehicle driving, if so, and if the real-time confidence of the scene conversion rule reaches the standard, performing driving scene conversion, and determining a decision priority after conversion; and converting into a corresponding execution command based on the decision priority, continuously monitoring an execution performance index of the execution command, and carrying out feedback adjustment on feature sets of different driving scenes and scene conversion rules among the different driving scenes based on the execution performance index.

The invention relates to the technical field of electronics, and discloses a boost circuit control method and device, computer equipment and a storage medium, and the method comprises the steps: detecting a state change event in a boost circuit; calculating a voltage difference value between the front-end voltage value and the rear-end voltage value in the booster circuit, and determining a corresponding target control strategy based on the state change event and the voltage difference value, the target control strategy at least comprises a step of determining a corresponding target voltage value and a feed-forward current value based on vehicle driving parameters and working condition parameters of a load motor so as to execute voltage regulation, or a step of determining a corresponding feed-forward duty ratio based on a rear end voltage value of a booster circuit so as to execute voltage regulation; and executing corresponding control operation on the booster circuit according to the target control strategy. The problems that in the prior art, due to the fact that a control strategy of a booster circuit is fixed, and a state change event and voltage difference value dynamic adjustment are not combined, voltage adjustment response lags behind, and stability is insufficient are solved.

The invention relates to a collision early warning method based on a laser radar in a non-structural environment, belongs to the technical field of vehicle driving safety, and solves the technical problem of low accuracy and effectiveness of collision early warning in the non-structural environment. Comprising the following steps: in a vehicle body coordinate system, collecting point cloud data in a non-structural environment by using a laser radar, estimating a vehicle motion pose by using the point cloud data, and obtaining a real-time pose and speed of a vehicle; generating a vehicle motion prediction trajectory based on the motion pose and speed of the vehicle, and performing ground point cloud filtering and obstacle clustering detection on the point cloud data in the vehicle prediction driving area to obtain an obstacle clustering result; carrying out obstacle geometric collision check based on an obstacle clustering result, determining an obstacle vertex closest to the vehicle in a vehicle prediction trajectory range area, estimating the movement speed of the obstacle vertex, and calculating an RSS distance; if the RSS distance is smaller than the preset safety distance threshold value, collision early warning is triggered, and accurate early warning in the non-structural environment is achieved.

The invention relates to a vehicle stability evaluation and early warning method and device, computer equipment and a medium. The method comprises the following steps: constructing a high-dimensional feature space fusing vehicle lateral, yaw, heeling and tire dynamic states; obtaining a vehicle dynamic state estimation value in the high-dimensional feature space based on the multi-source sensor data; determining a local stable manifold boundary representing the stable motion limit of the vehicle in the high-dimensional feature space according to the current vehicle driving condition; extracting stability risk characteristics of multiple dimensions in parallel according to a geometrical relationship and a dynamic evolution trend between a vehicle dynamic state estimation value and a local stability manifold boundary; generating a comprehensive risk level through a probability fusion model, and identifying a dominant instability mode; and when the comprehensive risk level exceeds a preset threshold value, triggering a corresponding predictive cooperative chassis control strategy according to the dominant instability mode. By adopting the method, multi-dimensional states can be fused in real time, and different instability modes can be recognized and distinguished in advance.

The invention relates to the technical field of intelligent traffic systems, and provides a vehicle trajectory prediction method and system, and the method comprises the steps: obtaining an RGB image and three-dimensional point cloud data of a vehicle driving environment; obtaining driving fusion data based on the RGB image of the vehicle driving environment and the three-dimensional point cloud data; based on a graph neural network GNNs algorithm and the driving fusion data, driving multi-modal data is extracted, a graph structure relation is constructed based on the driving multi-modal data, space-time interaction relation coding is carried out, and first driving track prediction is generated; based on a neural network framework of a multi-layer attention mechanism, more accurate prediction of a plurality of driving tracks is generated; performing weighted fusion on the plurality of driving track predictions, and outputting final driving candidate track predictions in combination with respective confidence scores; according to the method, efficient fusion of multi-source information is realized through the neural network, the dynamic space-time interaction relationship between the vehicle and the road environment is constructed, the high adaptive capacity and the pre-judgment capacity of driving track prediction are improved, and the method is suitable for an intelligent traffic system.

The invention provides a mixed traffic cooperative control method based on multi-agent reinforcement learning, which can be applied to the technical field of intelligent traffic and automatic driving. The method comprises the following steps: inputting first state information of a target intersection and second state information of an adjacent intersection into a signal lamp intelligent body obtained based on public traffic priority target training, and outputting a signal lamp phase control action; inputting the signal lamp information and the automatic driving vehicle information of the automatic driving vehicle at the target intersection into a signal lamp fine-tuning intelligent body obtained based on safety target training, and outputting a signal lamp fine-tuning control action; inputting the signal lamp information, the automatic driving vehicle information and the front vehicle driving information into an automatic driving vehicle intelligent body obtained based on safety efficiency cooperative target training, and outputting an automatic driving vehicle driving action; and under the condition that the first signal lamp is controlled to execute the signal lamp phase control action and the signal lamp fine adjustment control action, the automatic driving vehicle is controlled to execute the driving action of the automatic driving vehicle.

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.

The invention belongs to the technical field of road tunnel illumination, and particularly relates to a control method and system for adjusting road tunnel illumination based on dynamic traffic, which comprises the following steps: firstly, acquiring vehicle information through a high-definition camera array, a microwave radar detector and an illumination intensity sensor in a multi-source manner, then preprocessing the multi-source data, and applying an algorithm to adjust the illumination of a road tunnel; traffic flow statistics and trend prediction, vehicle driving state analysis and vehicle type accurate classification statistics are realized; based on this, the traffic flow is counted and predicted in real time through the dynamic traffic information acquisition and analysis module, the illumination brightness reference value is set and corrected based on the traffic flow by combining the multi-dimensional illumination parameter intelligent decision module, and then the illumination execution and dynamic adjustment module accurately executes the brightness instruction. The method has the advantages that the illumination brightness is dynamically matched with the traffic flow, the traffic safety requirement during high traffic flow is met, and energy waste during low traffic flow is avoided.

The invention discloses a passenger comfort determination method and device, a storage medium and a program product, and relates to the technical field of vehicles, and the method comprises the steps: obtaining vehicle driving data, and determining the impact score of the change of a vehicle driving state on the passenger comfort according to the vehicle driving data; the vehicle driving data can objectively reflect the change of the driving state of the vehicle in the driving process, and the change of the vehicle driving state can generate different influences on the comfort of the passengers, so that the influence score of the change of the vehicle driving state on the comfort of the passengers can be objectively determined through the vehicle driving data; the influence score reflects the objective influence of the running state of the vehicle on the comfort of the passengers; and determining a passenger comfort score according to the influence score, thereby eliminating the subjective feeling difference of individuals, and calculating to obtain a comfort score capable of objectively quantifying the real comfort of the passenger. Therefore, the technical problem that the real comfort degree of the passenger in the riding process cannot be objectively reflected can be solved.

The embodiment of the invention provides a vehicle risk avoiding method, electronic equipment and a vehicle, and the method comprises the steps: determining a control abnormality index value and a risk score threshold value of the vehicle according to vehicle driving information and driver control habit information; determining a real-time risk score of the vehicle according to the driving environment information, the vehicle driving information and the control abnormity index value; and determining a risk level of the vehicle according to the real-time risk score and a risk score threshold, and controlling the vehicle to execute risk avoiding operation corresponding to the risk level. According to the method, the dynamic risk score threshold is obtained by considering the vehicle driving information and the driver control habit information, the control abnormal index value is introduced, and the real-time risk score is obtained by combining the vehicle driving information, the driving environment information and the control abnormal index value, so that the risk level is accurately evaluated, and the risk assessment efficiency is improved. And the rationality and effectiveness of risk avoiding decision making are ensured.

The invention relates to the technical field of road surface monitoring, in particular to a sensor-based road surface quality detection system, which comprises a disturbance extraction module, a trajectory analysis module, a consistency identification module, a sudden change sorting module and a crack confirmation module. According to the invention, through the time sequence correlation analysis of the vertical structure vibration acceleration, the GPS direction vector and the transverse angular velocity signal in the vehicle driving process, the disturbance behavior and the trajectory deviation trend change in the vehicle and road surface contact process can be identified, and after the structure disturbance and the trajectory variation are correlated and compared, the stability of the vehicle is improved. Further screening real disturbance response from change consistency, constructing a space transfer model of crack mutation in combination with a joint state structure of disturbance intensity and offset amplitude, identifying a joint abnormal interval of structural damage and trajectory deviation in time and space, and determining a joint abnormal interval of structural damage and trajectory deviation in time and space through a multi-dimensional index distribution density and time continuity discrimination standard. And accurate judgment and spatial calibration of the crack area are realized.

The invention provides a ramp energy-saving control method based on off-line speed planning and dual-mode model prediction tracking, and aims to solve the problems of insufficient precision of ramp driving energy consumption and safety cooperative control and frequent mode switching of an automatic driving vehicle. The method comprises the following steps: firstly, identifying a key road section based on high-precision road gradient data, and generating an optimal energy-saving reference speed matched with gradient characteristics in an off-line manner by adopting a dynamic planning and sequential quadratic planning hybrid optimization strategy; secondly, when the vehicle runs, relevant information is loaded after confirmation of a driver, the system takes over the longitudinal control right, two control modes are dynamically switched based on the front vehicle distance, and frequent mode switching is avoided through a hysteresis threshold value; and finally, taking the slope-fused vehicle longitudinal dynamics model as a prediction model, solving an optimal control instruction online by adopting a model prediction control algorithm, performing closed-loop execution, monitoring intervention operation of a driver, and immediately returning the driving right after the intervention operation is detected. According to the method, offline planning and online dual-mode tracking technologies are integrated, the method has the advantages of high energy consumption optimization precision, stable mode switching and good safety adaptability, and energy-saving and safe driving of the automatic driving vehicle in a ramp scene is realized.

The invention discloses an electric vehicle energy management method and system based on vehicle speed prediction, and relates to the technical field of electric vehicle energy management, and the method comprises the steps: collecting vehicle driving data to construct a state vector, constructing a quantile LSTM vehicle speed distribution predictor, and predicting the quantile vehicle speed track of each step in the future; mapping the quantile vehicle speed track into traction power distribution through longitudinal dynamics, and carrying out battery power, current and SOC quantile rolling prediction; predicting and defining predicted time domain energy consumption based on the battery power quantile so as to construct an MPC optimization objective function of the CVaR risk constraint; constructing a deterministic constraint set based on a quantile prediction trajectory, defining a terminal risk cost according to a predicted terminal state, and merging the terminal risk cost into an MPC optimization target to obtain a final target function; the robustness and adaptability of the electric vehicle energy management system are obviously improved, especially in complex and uncertain road and traffic environments.

The invention belongs to the field of risk prediction, and relates to a wet and slippery road risk assessment method based on vehicle trajectory identification by an unmanned aerial vehicle, which comprises the following steps: extracting video data of a vehicle driving trajectory of a dangerous road section by adopting a mode of carrying a holder on a high-resolution unmanned aerial vehicle, and mapping the video data to obtain a vehicle ground trajectory sequence; calculating parameter indexes of the normal sample and the wet and slippery road according to the reference trajectory curve, comparing the wet and slippery road with the normal sample, and outputting a distribution difference; self-adaptively adjusting a threshold index under the wet and slippery road based on the meteorological coefficient, and calculating a weighted threshold proportion and duration exceeding 95% quantile of normal weather; and judging the adjacency consistency and the intra-segment space coherence, and finally forming a segment-level risk score and a risk level. The accuracy, the real-time performance and the interpretability of risk early warning are remarkably improved, and the false alarm rate caused by environment misjudgment or equipment errors is effectively reduced.

The invention provides a method for controlling a vehicle and the vehicle. The method responds to a target request for requesting a vehicle to enter a rest mode, and determines whether a vehicle state satisfies a preset condition. And further, when the vehicle state meets the preset condition, based on the demand of the target object, all or part of the first vehicle parts related to vehicle driving are controlled to be in a closed state, and all or part of the second vehicle parts used for meeting the rest demand are controlled to be in a running state. Thus, when the vehicle is in the rest mode and the target object has a rest, the target object can use the second vehicle part based on own requirements, so that the comfort of the target object in the cabin during the rest is improved. In addition, when the target object has a rest, all or part of the second vehicle components related to driving are not used. According to the method, the second vehicle part is controlled to be in the closed state, unnecessary vehicle functions can be prevented from being activated, and low vehicle operation cost can be consumed.

The invention discloses an engineering vehicle driver behavior analysis system based on federal learning. The system comprises a multi-source sensing layer which forms vehicle-road-person collaborative all-weather scenarized data input; the edge calculation layer is used for capturing a complex space relationship of a driving scene, embedding a lightweight cross-modal attention module, calculating an attention weight, and processing long-sequence driving operation data by adopting a Mamba state space model to capture a long-term dependency relationship of a driver state; the federated learning layer adopts a pFedMe algorithm to realize a driver customized model, during local training, the model learns local data distribution characteristics through a Moreau Envelopes regularization item, the cloud aggregates distribution of adaptive local data of each driver, models a federated learning process as a meta-learning problem, and performs training updating; a privacy and security layer; and differential privacy dynamic adjustment, Paillier improved homomorphic encryption and a Hyperledger Fabric block chain are adopted to prevent a log from being tampered.

Head-up display (10) for a vehicle, comprising: a display device (11) arranged to display at least one information element (33, 34, 35, 36, 51, 52, 53, 54) on a display element (37, 55) in a field of view (30, 50) of a vehicle driver; a receiving unit (12) arranged to receive vehicle data of the vehicle; an analysis unit (13) arranged to analyze the received vehicle data and to determine an analysis result for a vehicle driving situation; an adjustment unit (14) which is designed to adjust the display of the at least one information element (33, 34, 35, 36, 51, 52, 53, 54) on a display element (37, 55) of the display device (11) on the basis of the determined analysis result; wherein the adjustment unit (14) is arranged to adjust a position of the at least one information element (33, 34, 35, 36, 51, 52, 53, 54) on a display element (37, 55) of the display device (11). The invention also relates to a vehicle and a computer-implemented method.