1983 results about "Road traffic safety" patented technology

The invention relates to the technical field of water traffic risk assessment, particularly provides a traffic risk dynamic assessment model construction system, and solves the problem that the intelligent degree and dynamic performance of traffic risk assessment need to be further improved. According to the system, a first modeling assembly constructs a risk propagation model adopting meta-learning; the second modeling component constructs a multi-subject collaborative risk assessment model; an active intervention component; and generating an active intervention strategy. The traffic safety is improved, and the system can effectively reduce the occurrence rate of traffic accidents through real-time risk perception, accurate evaluation and active intervention; a traffic management dynamic risk map and an intervention strategy are optimized, a scientific decision basis is provided for a traffic management department, and the management efficiency is improved; through multi-channel early warning and path induction, safer and more convenient travel experience can be provided for a driver; the development of intelligent traffic is promoted, and technical support is provided for the realization of an intelligent traffic system.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention provides a traffic safety detection method and system based on big data analysis, and the method comprises the steps: obtaining a to-be-detected traffic internet-of-things data set, carrying out the fusion representation information mining of the to-be-detected traffic internet-of-things data set, and obtaining first fusion representation information; the first fusion representation information comprises multiple pieces of data dimension representation information corresponding to multiple data dimensions; indexing a plurality of historical traffic internet-of-things data sets matched with the traffic internet-of-things data set to be detected according to the first fusion representation information, and obtaining a traffic congestion level mark corresponding to each historical traffic internet-of-things data set according to second fusion representation information corresponding to each historical traffic internet-of-things data set and the traffic congestion level mark corresponding to each historical traffic internet-of-things data set; generating a traffic situation fusion template according to the first fusion representation information of the traffic internet-of-things data set to be detected; and according to the traffic situation fusion template, detecting a traffic congestion level mark of a traffic internet-of-things data set to be detected. According to the invention, the traffic jam grade marks can be efficiently and accurately obtained.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention belongs to the technical field of vehicle trajectory prediction, and discloses a multi-scale interactive perception highway vehicle trajectory prediction method, which comprises the following steps: jointly modeling short-term burst features and long-term evolution trends through a convolutional neural network and a bidirectional gating cycle unit, and introducing a time sequence attention mechanism to improve the perception ability for key time slices; in combination with a dynamic graph attention mechanism including physical edge features such as relative position, relative speed and relative acceleration, a vehicle interaction relationship is updated in real time so as to improve spatial modeling precision and interpretability; in the decoding stage, the guide vector and the semantic information of the lane are fused, so that the predicted trajectory conforms to the geometric structure of the road in space and keeps smooth and continuous in time. According to the method, the robustness and adaptability of the model in the sparse adjacent vehicle environment of the expressway can be improved while the prediction precision is ensured, a more stable and reliable trajectory prediction result is provided for an intelligent traffic system, and powerful technical support is provided for traffic safety management and operation scheduling of the expressway.

The invention discloses an urban tunnel intelligent safety operation and maintenance method and system combined with multiple data, particularly relates to the field of intelligent traffic operation and maintenance, and is used for solving the problems of prediction deviation and congestion management caused by tunnel flow fluctuation. In combination with traffic parameters of a multi-point detector, a wave similarity analysis model is utilized to accurately evaluate a propagation path and a target section of fluctuation; meanwhile, by means of a time sequence superposition correction mechanism, a historical fluctuation rule and real-time data are fused, a traffic flow transmission prediction curve after dynamic correction is generated, and balance between physical constraint and prediction precision is achieved; finally, key indexes are output through dynamic updating of the flow prediction model, efficient linkage of signal coordination and induction management and control modules is guided, congestion accumulation and traffic risks caused by fluctuation propagation can be reduced to the maximum extent, the tunnel operation and maintenance efficiency is optimized, and the traffic safety guarantee capacity is enhanced.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

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 belongs to the technical field of traffic safety monitoring technologies, and discloses a defogging method and equipment based on infrared light and visible light image fusion. The method comprises the following steps: image preprocessing: denoising and enhancing a foggy image; inputting the preprocessed picture into a defogging model based on infrared light and visible light image fusion to obtain a defogged image; the defogging model based on infrared light and visible light image fusion carries out the following processing on an input picture: processing an infrared light image, enhancing the penetration effect of the infrared light image in a fog environment, and highlighting the contour information of a target; and processing the visible light image, and recovering the color and texture details of the visible light image in the fog environment. According to the method, the definition and the visual effect of the foggy day image can be remarkably improved, the target is prominent, the color is natural, the contrast ratio is effectively improved, the detail information of the image is reserved, rapid defogging processing is realized, and the accuracy of fog concentration estimation is enhanced.

The invention belongs to the technical field of vehicle-mounted human-computer interaction, and particularly relates to a vehicle-mounted music recommendation system based on context awareness, which covers the technologies of computer vision, recommendation systems, human-computer interaction and the like. The whole system is divided into two main parts: the first part is a context information collection module, and the module realizes real-time recognition of the emotion of a driver by collecting facial images and voice information of the driver; and the second part is a feedback module used for processing interaction between the driver and different emotion analysis results, including mapping rule reasoning and music recommendation. The system generates a personalized music recommendation list by analyzing the facial expression of the driver and the emotional characteristics extracted from the voice information, so as to reduce the music selection burden of the driver and prevent the occurrence of extreme emotions, thereby having important significance for guaranteeing the personal safety of the driver and the road traffic safety.

The invention relates to a railway bridge post-earthquake traffic safety probability evaluation method and device, which are applied to the technical field of traffic safety, and the method comprises the steps: obtaining an earthquake-induced damage value set of each component through a probability distribution function of different material parameters of a railway track-bridge system; the method comprises the following steps: acquiring a mapping relation between the earthquake-induced damage of a key component and track irregularity through a balance differential equation of a bridge and railway track structural mechanical model, and acquiring earthquake-induced track random irregularity samples of different components based on an earthquake-induced damage value set of each component and the mapping relation between the earthquake-induced damage of the key component and track irregularity; constructing a power spectrum of the track irregularity caused by vibration of different components; establishing a rapid prediction model of the driving performance indexes on the axle after the earthquake through the earthquake-induced track irregularity sample and the coupling dynamic response result; through a Monte Carlo method, based on the earthquake-induced track irregularity power spectrum and the rapid prediction model, the overrun probability and the confidence interval of the driving safety on the axle after the earthquake are rapidly and accurately obtained.

The invention discloses a dynamic expansion management and control method for highway accident congestion based on an ETC portal, and relates to the technical field of traffic management and control, and the method comprises the steps: obtaining accident information; calculating the queuing length of the accident scene; the congestion duration is predicted, wherein the congestion duration is composed of rescue time, obstacle clearing time and congestion dissipation time; dilatation management and control are carried out in different areas, a control area and a buffer area are sequentially divided from the accident occurrence place along the rear, and dynamic dilatation management and control are carried out according to the actual traffic capacity of the accident road section. The method has remarkable advantages in the aspects of quick response, resource adjustment, timely rescue, accurate prediction, plan making and the like, decision scientificity is improved, effectiveness of active traffic management is improved, management strategies of active traffic management are refined, scientificity and rationality of dynamic management and control measures are improved, and the method is suitable for popularization and application. The method is of great significance in improving the emergency processing capacity of the expressway and guaranteeing traffic safety and smoothness.

The invention, which relates to the technical field of highway traffic safety monitoring, discloses an expressway pavement ice condensation monitoring system comprising a data acquisition module for acquiring environmental data and pavement icing state information on an expressway pavement; and the self-calibration module is connected with the data acquisition module, performs self-calibration on the data acquisition module according to the environment data acquired in real time and the road surface icing state information, and corrects sensor errors caused by external environment factors. According to the highway pavement ice condensation monitoring system, a self-calibration function is added in monitoring equipment and an intelligent algorithm is introduced, so that the monitoring system can automatically correct measurement errors caused by external environment changes in real time, and a machine learning model is combined to predict pavement conditions more accurately; the working stability of the system in severe weather can be improved, and the risk of icing of the road surface can be found earlier, so that more accurate early warning information is provided for a traffic management department, and the probability of traffic accidents is reduced.

The invention relates to the technical field of traffic safety, in particular to a highway risk map construction method and device fusing multi-source data, and the method comprises the following steps: S1, selecting a traffic conflict risk, a driving behavior risk and a vehicle safety risk as risk dimensions, selecting risk indexes according to the traffic conflict risk, the driving behavior risk and the vehicle safety risk, and calculating risk index data and the weight of the risk indexes; s2, calculating the total risk of the road section according to the weight of the risk dimension, the weight of the risk index and the risk index data; and S3, obtaining a risk map according to the total risk of the road section and the weight of the risk dimension. According to the method, by constructing the risk map and identifying the high-risk area, the application of multi-source data fusion in the aspect of highway risk assessment is enhanced, a scientific basis and technical support can be provided for traffic safety management, and the safety of the highway is improved.

The invention, which relates to the technical field of traffic safety dispersion, discloses an anti-vehicle-blocking traffic safety dispersion method for reconstruction and expansion of an expressway, and the method comprises the following components: S1, constructing a multi-dimensional information collection system, S2, establishing an information intelligent transmission network, S3, carrying out a traffic jam prediction algorithm, and S4, carrying out precise early warning and intelligent guidance. According to the invention, rapid and efficient transmission of information is realized through the converged communication technology, the communication technology not only improves the speed and stability of data transmission, but also can realize intelligent switching in different signal environments, thereby providing timely and reliable traffic information for a traffic management center, enabling the traffic management center to make a response rapidly, and improving the traffic management efficiency. Accurate early warning and intelligent guidance are implemented, and traffic jam and safety accidents are effectively avoided.

The invention discloses a traffic question and answer method, device and equipment based on a large language model, and a medium. A traffic safety knowledge graph is constructed in advance. After the question text is divided into a plurality of sub-question texts, for the plurality of sub-question texts, determining a target entity with the highest similarity corresponding to the sub-question texts in the traffic safety knowledge graph; and determining a target sub-graph having a first-order relationship with the target entity. And for the multiple sub-question texts, inputting the sub-question texts, the entities and relationships in the corresponding target sub-graphs and the traffic long text data into a large language model, and determining sub-answer texts corresponding to the sub-question texts based on the large language model. And finally, inputting the question text, the plurality of sub-question texts and the corresponding sub-answer texts into a large language model, and determining a target answer text corresponding to the question text based on the large language model. And the accuracy of the determined target answer text is ensured.

The invention relates to the technical field of intelligent traffic safety management and control, and discloses a highway high-risk road section early warning method and system, and the method comprises the steps: collecting real-time environment and vehicle data through a vehicle side and a roadside sensor, uploading the real-time environment and vehicle data to a cloud platform for data fusion, and obtaining fusion data; establishing a risk judgment model on the cloud platform, updating the risk judgment model in real time through incremental training, analyzing the fused data by using the risk judgment model, and generating a comprehensive risk score; and triggering graded early warning according to the comprehensive risk score, carrying out early warning in advance at a vehicle end, and carrying out visual display on an early warning result. According to the method, the adaptive capacity of the safety detection system to variable weather and vehicle behaviors is improved, finally, accidents are effectively reduced, and the driving experience and the traffic efficiency are improved.

The invention relates to the technical field of highway traffic safety guarantee, and particularly discloses a highway early warning system and an early warning equipment layout method under ice and snow conditions. The problems that recognition lags, the early warning response has the regional splitting phenomenon and the early warning information has deviation in the common technology are solved. Comprising a data acquisition layer which is used for acquiring meteorological data, road surface data, traffic flow data and vehicle behavior data in real time and eliminating abnormal data through a cross validation mechanism; the analysis decision-making layer analyzes the fused multi-dimensional data based on a multi-factor coupling dynamic risk assessment model, and generates a blue / yellow / orange / red four-level early warning signal; and the early warning issuing layer is used for issuing early warning information according to the early warning grade matching equipment combination. Through multi-source data cross validation and feature fusion, the false report and missing report rate is remarkably reduced, and the risk identification accuracy is improved.

The invention, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably improved.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

The invention discloses a road defect detection method based on an improved RT-DETR-R18 model. A backbone network adopts a CSPNet architecture. According to the invention, innovative improvement is carried out on an original C2f module, and Bottleneck in the original C2f module is replaced by DynamicIncMixerBlock to form a C2fDCMB module. The DynamicIncMixerBlock is characterized in that a DynamicIncMixerBlock is fused with a DynamicInceptionMixer component and a ConvolutionalGLU component, and the DynamicIncMixerBlock and the ConvolutionalGLU component are fused with each other. A dynamic Inception deep convolution structure is adopted by the DynamicInception Mixer, and features of different scales and directions are adaptively captured through dynamic kernel weight distribution; according to the method, AIFI (intra-scale feature interaction) in a high-efficiency hybrid encoder is improved, a module is combined with an EfficentAdditiveAttach and a feedforward network structure to form TransformerEncoder LayerEfficentAdditiveAttach, a RepC3 module is replaced by a RetBlockC3 module in a cross-scale feature fusion module (CCFM) through a multi-head attention mechanism and a nonlinear activation function, the RetBlockC3 is improved based on the RepC3, RetBlock and RelPos2d are introduced, and the RetBlock and RelPos2d are introduced into the RetBlockC3 module to form a multi-scale feature fusion module. According to the method, the accuracy, recall rate and detection speed of road defect detection are obviously superior to those of a traditional detection model, and a more accurate and efficient technical solution is provided for maintenance of road infrastructures and traffic safety guarantee.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic accident prediction and emergency response system and method based on vehicle-mounted data. The method comprises the steps of collecting vehicle-mounted data, and then performing denoising processing on the vehicle-mounted data by using an adaptive convolutional neural network; constructing an intelligent traffic accident prediction model based on the de-noised data, capturing key features in time sequence data by using a bidirectional LSTM, an attention mechanism, residual connection and a Dropout layer, and predicting an accident occurrence probability; according to a prediction result, an early warning threshold value is dynamically adjusted, alarms of different levels are triggered, and corresponding measures are taken through automatic emergency response or driver reminding; through collecting data before an accident, a prediction model and an emergency response strategy are optimized, prediction precision is continuously improved, and traffic management efficiency is enhanced. According to the invention, vehicle-mounted data and an intelligent algorithm are comprehensively utilized, accurate accident prediction and efficient emergency response are provided, and traffic safety and traffic efficiency are improved.

The invention relates to a railway bridge damage and driving performance mapping method and device and a storage medium, and is applied to the technical field of traffic safety, and the method comprises the steps: building a mapping relation between rail-bridge key component earthquake-induced damage and rail irregularity, and comprehensively considering the influence of rail-bridge system earthquake-induced damage on driving safety performance; a deep learning method is adopted, a deep learning model is trained through a component earthquake-induced damage numerical value of the rail-bridge system and a train-rail-bridge coupling dynamic response result, and then a quantitative mapping relation between structural earthquake-induced damage of the rail-bridge system and driving performance is established. Therefore, safety and stability indexes of driving on the railway bridge track after key components or parts of the railway bridge track are damaged due to earthquakes can be quickly and accurately provided.

The invention relates to the technical field of intelligent sensing control, in particular to an intelligent sensing control method and system based on multi-modal sensor fusion. According to the method, vision, millimeter-wave radar, geomagnetic and other multi-mode sensors are deployed to collect traffic data, feature information is extracted after preprocessing, an adaptive weighted fusion algorithm is adopted for fusion, and an intelligent control decision is generated according to a fusion result. The method comprises the steps of signal lamp timing optimization based on fuzzy logic, traffic flow induction based on reinforcement learning and traffic event detection and response based on event rules, and finally an instruction is sent to traffic control equipment to adjust the flow. The advantages of various sensors are integrated, the limitation of a single sensor is overcome, traffic information is accurately and comprehensively obtained, intelligent decision is made on the basis, the traffic condition can be comprehensively and accurately sensed, traffic control is intelligently optimized, the self-adaptive adjusting capacity is high, the traffic operation efficiency can be improved, and the traffic safety can be enhanced.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

A Level IV fuel-saving robot system for heavy-duty trucks (HDT) focuses on the minimization of actual fuel consumption (L / 100 km) for long-haul freight based on an electrical power split device and a mixed hybrid powertrain architecture. The Level IV fuel-saving robot has an L4 autonomous driving function within the Operational Design Domain (ODD) of expressways, operates in a “shadow mode” or “Disengagement Mode,” automatically generates a discrepancy report or detachment report, completes the “3R” (Real Vehicle, Real Road, Real Payload) batch validation for an L4 system on a billion mile scale quickly with high performance to cost ratio under the condition of ensuring the traffic safety of existing road users and reduces the total validation expense by more than 65% compared with the modern HDT with internal combustion engine equipped with the L4 system, promoting the early commercialization of the fuel-saving robot.

The invention discloses a road tunnel lighting system and method based on traffic flow and external environment. Relates to the technical field of highway tunnel lighting. At present, a traditional road tunnel lighting system mode neglects factors such as tunnel external environment change and traffic flow density, so that energy waste and poor lighting effect are caused, especially under day and night alternation, weather change or special traffic conditions; according to the system, information such as traffic flow, environment illumination intensity and meteorological conditions inside and outside a tunnel is collected in real time through multiple sensors, and the illumination brightness inside the tunnel is dynamically adjusted by combining calculation and dimming strategies of a central control unit so as to cope with different traffic and weather conditions; according to the road tunnel lighting system and method, through dimming control, the energy utilization efficiency can be remarkably improved while traffic safety is guaranteed, and high economic benefits and social benefits are achieved.