11171 results about "Vehicle driving" patented technology

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The invention provides an intelligent traffic vehicle behavior modeling method and system based on collaborative learning, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining traffic data in a target region; performing data calibration processing according to the traffic data to obtain a collaborative perception data set; a vehicle space-time trajectory function is generated according to the collaborative perception data set, and a conflict domain space-time distribution diagram is obtained; according to the vehicle space-time trajectory function and the conflict domain space-time distribution diagram, probability distribution characteristics of the vehicle driving intention and a multi-workshop competition cooperation relation matrix are extracted, and a multi-dimensional interaction characteristic vector is obtained; and behavior modeling is carried out according to the multi-dimensional interaction feature vectors, and a vehicle behavior model representing interaction behavior rules and decision logic between vehicles is obtained. According to the method, through multi-vehicle cooperative sensing data calibration and dynamic fusion of road geometric constraints, the problems of single-vehicle sensor shielding and cross-vehicle data heterogeneity are effectively solved, and the global consistency of vehicle position and motion state estimation is remarkably improved.

The invention relates to the technical field of data mining, in particular to a data security management system and method based on cloud computing. The method comprises the following steps: acquiring real-time vehicle driving data, and performing homomorphic modular exponentiation dangerous case sensing fragmentation encryption to obtain dangerous case sensing encrypted fragmentation data; acquiring an Internet of Vehicles data stream, and implementing zero-trust adaptive data stream encryption to obtain a zero-trust authentication credential; implementing federal learning privacy feature mapping on the dangerous case sensing encrypted fragmented data to obtain an anonymized encrypted feature mapping model; performing Merkle tree aggregation on the anonymized encrypted feature mapping model to obtain a block chain global dangerous case fingerprint; extracting a threat behavior quantization vector according to the block chain global dangerous case fingerprint and the anonymization encryption feature mapping model; and carrying out transmission strategy optimization according to the threat behavior quantization vector and the zero-trust authentication credential to obtain a global dynamic transmission strategy matrix. According to the invention, the safety of vehicle data uploading and the overall uploading efficiency can be improved.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention provides a vehicle driving track prediction method, system and device and a medium, and belongs to the technical field of vehicle track prediction.The method comprises the steps that vehicle state information and environment information of each track point of a vehicle are collected to serve as track data, and a deep learning denoising algorithm is used for preprocessing and feature sequence extraction; constructing a trajectory prediction model by using a Transform in combination with a graph neural network, constructing a training set by using the extracted feature sequence to train the trajectory prediction model, and adjusting model parameters by using a multi-task loss function constructed by trajectory prediction precision and uncertainty estimation in the training process; and using the trajectory prediction model to predict the vehicle trajectory, using the Bayesian optimization method to optimize the trajectory parameters with the goal of minimizing the prediction error, and adjusting the optimization parameters with the vehicle dynamics constraint and the road constraint as constraint conditions in the optimization process. According to the invention, through multi-fusion and multi-stage optimization, high-precision prediction of the vehicle track in a complex traffic environment is realized.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

The invention relates to the technical field of intelligent logistics scheduling, in particular to a logistics scheduling planning method and system based on a graph neural network and reinforcement learning, and the method comprises the following steps: S1, constructing a dynamic graph structure of a logistics network; s2, carrying out embedded learning on the dynamic graph structure through a graph attention network, and extracting a multi-dimensional feature vector of each node; s3, inputting the multi-dimensional feature vector into a multi-agent reinforcement learning framework to generate an initial vehicle path planning scheme; s4, dynamically correcting the road section traffic state in the initial vehicle path planning scheme; s5, iteratively updating the vehicle path planning scheme through local reinforcement learning; and S6, outputting a final collaborative optimization cargo transportation track and a vehicle driving path. According to the method, dynamic modeling and multi-agent path collaborative optimization of a logistics network structure can be realized, and the method has adaptive adjustment capability on real-time traffic and environment change, so that the overall scheduling efficiency is improved.

The invention belongs to the technical field of intelligent driving, and particularly relates to an intelligent driving method and system with a body, and the method comprises the steps: fusing the V2X data of a vehicle-mounted laser radar, a millimeter wave radar and a road side unit, and obtaining the vehicle driving information; constructing a centimeter-level dynamic environment model by adopting a space-time alignment algorithm, and realizing multi-source data space-time synchronization and obstacle real-time prediction; designing a hierarchical reward function including security, efficiency and comfort rewards based on a user risk cognition mechanism, and generating an end-to-end reinforcement learning decision in combination with a Transform architecture; optimizing the dynamic environment model through reinforcement learning decision; inputting vehicle driving information into the optimized centimeter-level dynamic environment model, and predicting obstacles and vehicle tracks in a vehicle driving path; an MPC-Hybrid feedforward-feedback control system is constructed, a feedforward module is adopted to calculate a front wheel turning angle and acceleration parameters according to obstacles in a vehicle driving path and a vehicle track, and a feedback module is adopted to optimize transverse and longitudinal errors; controlling the driving direction of the vehicle according to the front wheel turning angle and the acceleration parameter transverse and longitudinal errors; according to the invention, through deep fusion of vehicle-road collaborative perception and reinforcement learning decision, the reliability and adaptability of the automatic driving system are significantly improved.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

Aspects of the present invention relate to an apparatus for providing driver assistance of a vehicle, an autonomous system, a vehicle, a method, a controller and a non-transitory computer readable medium. The apparatus comprises a first sensor and a second sensor mounted on a vehicle. The first sensor is configured to transmit and receive electromagnetic radiation to detect the presence of an object in a first area, and the second sensor is configured to transmit and receive electromagnetic radiation to detect the presence of an object in a second area. The first area and the second area overlap to define an overlapping area forward of the vehicle. The first area extends from the overlapping area to a first extreme direction having a rearward component and a leftward component, and the second area extends from the overlapping area to a second extreme direction having a rearward component and a rightward component.

The invention discloses an intelligent automobile electro-hydraulic composite braking system integrating driver intention, and belongs to the field of intelligent automobile braking control. The system comprises a vehicle environment fusion sensing module, a braking intention identification module, a multi-target braking force distribution module, a regenerative braking torque constraint module and an optimal distribution coefficient decision module. When a driver brakes, the vehicle environment fusion sensing module is used for acquiring vehicle driving state information, road adhesion coefficient information, driving environment information and driver state information; the braking intention identification module adopts a neural network algorithm to accurately identify the braking intention of the driver; the multi-target braking force distribution module optimizes the required braking torque by using an NSGA-II multi-target optimization algorithm; the regenerative braking torque constraint module dynamically corrects the calculation result of the regenerative braking torque through a comprehensive constraint condition; and the optimal distribution coefficient decision module screens out the optimal braking distribution coefficient under the current working condition from the Pareto solution set through dynamic weighting.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support enhanced sensor fusion techniques. In a first aspect, a method of includes receiving point cloud data for two or more frames from a radar device and generating scene flow parameter data based on the point cloud data. The method also includes generating voxel position adjustment data based on the scene flow parameter data, and generating feature concatenation information associated with two or more sensors based on the voxel position adjustment data and feature information associated with the two or more sensors. The method further includes performing feature detection and tracking based on the feature concatenation information to generate tracking information for one or more objects, and outputting the tracking information. Other aspects and features are also claimed and described.

The invention relates to the field of vehicle braking fault detection, in particular to a real-time fault detection method for a vehicle electronic braking system, which comprises the following steps of: acquiring vehicle driving state data through a vehicle-mounted sensor network and an electronic control unit, calculating filtered residual data of a target wheel speed sensor, and calculating the filtered residual data of the target wheel speed sensor; establishing calibration reference parameters of the health state; performing conjoint analysis on the acceleration data and the residual fluctuation data of the vehicle to obtain a dynamic response adaptive adjustment factor of the vehicle; obtaining a tiny drift confirmation factor of the vehicle through the candidate residual error in the low dynamic interference state of the vehicle; continuous fault judgment is performed by combining the dynamic response adjustment factor and the tiny drift confirmation factor of the vehicle, and the fault judgment result of the wheel speed sensor is obtained, so that the accuracy and sensitivity of fault detection when tiny systematic deviation occurs to the wheel speed sensor are improved.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

A method of controlling drift of a vehicle in which the vehicle more easily enters, maintains, releases, and transitions drift behavior, includes determining, by a controller, whether a current driving situation of the vehicle is a drift entry situation or a release situation based on vehicle driving information, and generating a drift index configured to indicate a current degree of drift from a determination result, determining, by the controller, a target yaw moment for left and right speed control depending on a driving situation or a braking situation of the vehicle and target yaw moments for drift assistance control depending on a driver input and a vehicle state in the drift entry situation, based on the vehicle driving information, and generating and outputting, by the controller, a target torque of a motor configured to drive each wheel of the vehicle based on the determined target yaw moments.

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 relates to a vehicle suspension control method and device, computer equipment and a readable storage medium. The method comprises the following steps: combining obtained vehicle body dynamics time series data, vehicle control time series data and environment perception time series data in a vehicle driving process into time series data of multiple channels, determining a self-adaptive modal number of the time series data under each channel according to the time series data under each channel, and determining the self-adaptive modal number of the time series data under each channel according to the self-adaptive modal number of the time series data under each channel; and performing modal decomposition on the time sequence data based on the adaptive modal number, extracting frequency domain characteristics of the time sequence data, integrating the frequency domain characteristics of the time sequence data of the multiple channels, inputting the frequency domain characteristics into a trained suspension control parameter prediction model to obtain target time sequence characteristics for predicting suspension control parameters, and performing prediction on the suspension control parameters based on the target time sequence characteristics. Vehicle suspension control parameters are obtained through prediction, and a suspension system of the vehicle is controlled based on the vehicle suspension control parameters. By adopting the method, the risk of rollover of the vehicle can be obviously reduced.

The invention provides a dynamic traffic flow distribution method based on multi-agent reinforcement learning, and the method achieves the dynamic optimization and real-time response of a large-scale vehicle path through a cloud-edge collaborative architecture, and comprises the steps: constructing a multi-agent traffic simulation environment, and carrying out the real-time traffic flow distribution according to the real-time traffic flow distribution. Generating an intelligent body vehicle with a random starting point through the Poisson distribution model; constructing a multi-dimensional observation vector comprising road topological coding, current road density, a neighborhood speed mean value, a target distance ratio and a congestion coefficient; adopting a near-end strategy optimization algorithm to carry out parallel strategy optimization on the multi-agent enhanced strategy network under a Ray RLlib framework; designing a multi-target reward mechanism based on path efficiency, congestion punishment and progress reward; a steering decision is generated according to the real-time observation state, and a vehicle driving route is dynamically updated through a Nash equilibrium solution of the game theory; and a cooperative emergency mechanism is triggered when the traffic jam suddenly occurs. According to the method, the problems of path convergence, response delay and secondary congestion of a traditional method are solved.

The invention relates to the technical field of driver behavior recognition, in particular to an intelligent behavior monitoring and recognition system for coping with a vehicle driver, which comprises a multi-mode driver data information module used for acquiring first driver multi-dimensional state information in a preset historical driving period, extracting and generating a first dangerous behavior feature set and a first dangerous response feature set according to the multi-dimensional state information of the driver; the environment information acquisition module is used for acquiring first driving environment interaction risk information in a preset historical driving period; a risk assessment model construction module; a real-time data evaluation module; according to the invention, the dangerous behavior of the driver is identified by combining the multi-dimensional state information of the driver with the environmental information, and the parameters of the dangerous behavior risk assessment model are continuously updated through the real-time data, so that the accuracy of monitoring and identifying the behavior of the vehicle driver is improved; meanwhile, high-risk behaviors can be obtained in advance, and the driving risk is prevented.

The invention discloses a magneto-rheological suspension semi-active feedback control system based on laser radar and multi-sensor fusion. The magneto-rheological suspension semi-active feedback control system comprises a radar assembly, a sensor assembly and a feedback control assembly which are integrally installed on a vehicle body. Related road surface and vehicle driving data are obtained through the laser radar, the vehicle attitude sensor and the vehicle speed sensor, the collected data are analyzed and processed through the data processing unit, and real-time road condition scores and vehicle driving parameters are obtained; generating a suspension control signal according to the real-time road condition score and the vehicle driving parameters, and transmitting the suspension control signal to a suspension controller; the suspension controller transmits current to the magnetorheological dampers, and therefore the magnetorheological dampers are controlled to output damping force to the corresponding wheels. The response speed of the magneto-rheological suspension can be remarkably increased, the adaptability of the magneto-rheological suspension to complex road conditions is enhanced, and therefore the driving stability, controllability and riding comfort of a vehicle are remarkably improved.

The invention belongs to the technical field of traffic optimization, and particularly relates to a traffic optimization method based on a three-layer Stackelberg game, and the method comprises the steps: obtaining mixed traffic data; according to the mixed traffic data, constructing a three-layer Stackelberg game model; an equilibrium solution of the three-layer game model is solved through a deep reinforcement learning algorithm, an SAC algorithm is introduced into an upper layer to generate a global constraint strategy, and a DDPG algorithm is adopted in a lower layer to optimize cooperative driving and safe obstacle avoidance driving behaviors of the intelligent vehicle; according to the global constraint strategy and the intelligent vehicle driving behavior optimization result, the traffic efficiency, safety and energy consumption and carbon emission indexes of the mixed traffic flow are optimized, the traffic state is continuously updated through behavior feedback data of a middle layer and a lower layer, and strategy optimization is carried out in combination with SAC and DDPG models. Therefore, the problems of insufficient dynamic adaptability, low efficiency of main body interaction processing, limitation of a game model and the like in the prior art are solved.

The described aspects and implementations enable online lane estimation and tracking in autonomous vehicle and driver assistance applications. In one implementation, disclosed is a method and a system to perform the method that includes receiving perception data indicative of driving lanes and reference objects in a driving environment of a vehicle, identifying target destinations corresponding to the driving lanes, determining candidate driving paths connecting a starting location with one of the target destinations, selecting a target driving path from the candidate driving paths based on geometric properties of each candidate driving path and a spatial relationship of a respective candidate driving path to the reference objects, and causing a motion of the vehicle to be modified based on the target driving path.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.

The described aspects and implementations enable training and deploying of accurate one-shot models capable of predicting trajectories of vehicles and other objects in driving environments. The disclosed techniques include, in one implementation, obtaining training data that includes a training input representative of a driving environment of a vehicle and one or more ground truth trajectories associated with a forecasted motion of the vehicle within the driving environment. The one or more ground truth trajectories are generated by a teacher model using the training input. The techniques further include training, using the training data, a student model to predict one or more trajectories of the vehicle and / or objects in the driving environment of the vehicle.

The invention discloses an expressway in-transit vehicle dynamic behavior analysis method and device and a medium, and belongs to the field of electric digital data processing. The method comprises the following steps: preprocessing multivariate data collected in real time, and fusing the preprocessed multivariate data to form original path information of a vehicle; on the basis of a road network topological structure and a shortest path algorithm, original path information is fitted, and a continuous and complete vehicle driving track is generated; based on the vehicle driving track, comparing the multivariate data in real time, determining whether the vehicle has an abnormal behavior, if yes, continuing to judge an abnormal type corresponding to the vehicle, and generating early warning information corresponding to the abnormal type; and according to the multivariate data of the to-be-predicted road section in the expressway, predicting the traffic flow of the to-be-predicted road section in real time, and displaying the traffic flow and early warning information of the to-be-predicted road section through a user interaction interface, thereby realizing dynamic behavior analysis of the in-transit vehicles on the expressway.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

The invention relates to a driver assistance system (2) for a vehicle (1), comprising at least a first detection unit (3) for detecting a lane (F1) being traveled by the vehicle (1) on a multi-lane roadway (F), a second detection unit (4) for detecting neighboring vehicles (9), and an evaluation unit (6) for evaluating data acquired by the detection units (3, 4). The invention further relates to a method for supporting the driver of a vehicle (1) with such a driver assistance system (2).

The invention relates to the field of decision planning of automatic driving vehicles, in particular to an automatic driving vehicle decision planning method based on deep reinforcement learning. The method comprises the following steps: when a vehicle runs on a structured road, acquiring position information of an environmental vehicle, and introducing historical position data of the environmental vehicle into a long short-term memory network to obtain a driving intention hiding state of surrounding vehicles; and generating a polynomial curve-based planning track through an SAC algorithm according to the obtained hidden state and vehicle motion information fusion. The problem of poor safety and stability of deep reinforcement learning in the field of automatic driving decision planning is solved, the overall training difficulty of reinforcement learning is reduced, and the method can be widely applied to the technical field of automatic driving decision planning.