17 results about "Road accident" patented technology

The application discloses a CAV speed guiding system based on deep reinforcement learning, which comprises the following steps: a vehicle-mounted system detects vehicle driving data and vehicle state information data; a roadside monitoring system detects driving environment, vehicle information data on different roads and pedestrian information data, matches the pedestrian and vehicle information data with a lane-level road link to obtain a data packet; a vehicle-road cooperation cloud computing center processes and stores the received data, and sends the processed data to a mobile edge computing center; the mobile edge computing center comprehensively evaluates road accident risks, constructs a Markov decision process and a speed guiding model of a multi-target additional reward function, calculates an optimal guiding speed of a CAV by using the multi-target additional reward function, and sends the optimal guiding speed of the CAV to the vehicle-mounted system. The communication delay problem is solved; a multi-target environment reward mechanism is comprehensively integrated, and the guiding speed of the CAV is improved in terms of traffic safety, stability and passing efficiency.

The utility model discloses an equipment fire fighting truck for handling various dangerous chemical tanker road accidents, which comprises a fire fighting truck body, a movable carriage is arranged on the fire fighting truck body, a pull arm hook is arranged between the fire fighting truck body and the movable carriage, and a plurality of placing bins are arranged in the movable carriage. The movable carriage is moved to the ground through the pull arm hook, the movable carriage can incline in the moving process, the limiting assembly in the movable carriage can limit the bearing frame in the containing bin, and the device is prevented from moving and inclining; the electric carrying vehicle can be taken out from the interior of the containing bin by moving the getting-off slope plate on the side edge of the compartment, and devices and instruments in the corresponding containing bin can be taken out through the electric carrying vehicle according to dangerous case requirements, so that other functional vehicles can be quickly, effectively and independently handled or assisted in handling various dangerous chemical tank truck road accidents.

This application provides a vehicle detection method for foggy road accident scenes using a fusion backbone and hypergraph computing approach, relating to the field of image processing technology. The method acquires target traffic accident images from traffic accident video data on foggy roads according to a set image sampling frequency. Then, a backbone network extracts original features, eliminates fog interference, and extracts semantic features from the target traffic accident images to obtain original features, fog-dissipated features, and multiple semantic features. Next, a neck network performs cross-level feature fusion on the original features, fog-dissipated features, and multiple semantic features to obtain a first fused feature. The first fused feature is then enhanced and subjected to multi-scale feature fusion to obtain multiple second fused features. Finally, the multiple second fused features are input into a prediction network to obtain multiple vehicle detection results for the foggy road accident scene. This improves the accuracy and real-time performance of vehicle detection in foggy road accident scenes.

The invention provides a big data analysis-based dynamic speed limit control system for a road section with high occurrence of road accidents, and belongs to the technical field of road control. According to the invention, the video camera is used for collecting the vehicle flow data, the vehicle flow data of the two sections of the road section are compared and analyzed, the emergency in the road section area can be judged at low cost, and the situation of the emergency can be quickly judged in time in combination with the tracking processing of the data acquisition module. The cloud computing control platform is overlapped with the ramp control system to dynamically adjust the speed limit in the road section area, and the vehicle flow entering the high-incidence road section is synchronously limited, so that the vehicle flow pressure of the high-incidence road section is relieved from the source, the secondary accident phenomenon is reduced in an auxiliary manner, the active defense for the road traffic accident is enhanced, and the safety of the road traffic accident is improved. The dynamic adjustment effect of the system in the using process is effectively improved, and the road safety is greatly improved.

The application provides an automatic driving accident prevention simulation scene generation technology based on swarm intelligence, which can realize random sampling in a road accident area, design an algorithm for splitting a lane to establish a KD tree, effectively reduce the time for searching a nearest neighbor lane, quickly determine the direction of a generated accident vehicle, and greatly reduce the search time compared with a method without optimization. Then, the constraint relationship of participants in the automatic driving accident prevention simulation scene is analyzed, an evaluation function is designed, a lane trajectory and a normal direction are searched along a lane in the road accident area based on a firework algorithm, and a suitable vehicle generation area is gradually approached. A sensor object is created through an interface provided by a Carla simulator, and an accident image is generated according to simulation data received by the sensor object. Compared with an existing scheme, the application can effectively improve the speed of automatic driving accident prevention simulation scene generation.

The invention belongs to the technical field of intelligent network connection automobile driving control, and discloses an intelligent network connection automobile formation driving control method, which comprises the following steps: acquiring position information, passing point information and terminal point information of each automobile; obtaining a vehicle formation according to the position information, the passing point information and the terminal point information of each vehicle; the vehicle formation is processed according to the vehicle formation logic, the vehicle logic, the position information, the passing point information and the terminal point information, and an updated driving route is obtained; obtaining a route completion action instruction according to the updated driving route and sending the route completion action instruction to each vehicle; and each vehicle receives and executes the route completion action instruction to complete vehicle formation driving. According to the invention, each intelligent network connection vehicle can orderly run on the road, so that the accident rate of the road is reduced, and the traffic efficiency of the road is improved.

The application discloses a road accident and illegal parking identification and early warning method and system based on intelligent traffic, relates to the technical field of intelligent traffic, and comprises the following steps: analyzing a preprocessed road monitoring video stream in real time, perceiving multi-dimensional accident features and multi-modal vehicle state features; constructing a parallel accident identification model and an illegal parking detection model, constructing an adaptive threshold based on camera parameters and environmental features, identifying accident scenes and illegal parking scenes by using the multi-dimensional accident features and the multi-modal vehicle state features in combination with the adaptive threshold; estimating the influence range of the accident scenes and the illegal parking scenes, and combining a multi-level early warning response mechanism to link sound and light alarms, navigation push and a rescue platform. The application constructs a more efficient and more accurate expressway accident prevention and control system by combining intelligent accident and illegal parking identification with a multi-level early warning linkage mechanism, and guarantees travel safety.

The application provides a highway maintenance equipment vehicle anti-collision system, belonging to the anti-collision technical field, which comprises a vehicle-mounted end, a vehicle position detection end and a highway maintenance equipment vehicle end, the vehicle-mounted end is wirelessly connected with the vehicle position detection end, the vehicle position detection end is wirelessly connected with the highway maintenance equipment vehicle end, and the vehicle-mounted end is used for sending an addressing signal in real time for the vehicle position detection end to detect.The application can effectively avoid the collision of the vehicle with the cone barrel or the highway maintenance equipment vehicle, reduce the occurrence of road accidents, and effectively avoid the personnel injury caused by the accident, while the staff on the highway maintenance equipment vehicle can be informed to make an avoidance notification in time when the vehicle information is not avoided in time, and the real-time detection of the traffic flow information at the rear can timely make an adjustment when congestion or preparation for congestion occurs, so as to avoid the influence of the highway maintenance equipment vehicle on the road traffic.

The invention discloses a road accident prediction method, system and device under an air-ground network based on RBF-DNN approximation. The method comprises the following steps: step 1, obtaining road data through a mobile sensor and a fixed sensor of the network and processing the road data; step 2, according to the air-ground networking system model and a possible unknown fault type model, in combination with a tracking error function and a robust adaptive fault-tolerant sliding mode function, obtaining a dynamic system model with an internal actuator unknown fault; step 3, according to the obtained dynamic system model, designing an adaptive law with projection and sigma-correction and a non-Zeno event triggering mechanism, and constructing a DNN closed-loop control air-ground networking prediction control model based on RBF; 4, after the obtained prediction control model is trained, road accident prediction is carried out on the obtained road real-time data; the problem that communication link defects still exist in an existing prediction technology in a highway sudden dangerous case scene is solved.

The invention discloses a bracelet and vehicle combined early warning method and device, a vehicle, a medium and a product. Under the condition that the vehicle is started, automatically detecting a target bracelet in a driving position association range, identifying a current driver in combination with the target bracelet, and synchronizing corresponding target vehicle account information; safety early warning related information is obtained, wherein the safety early warning related information at least comprises accident monitoring information of the vehicle and the road in front of the vehicle and driving state information of a current driver; determining a target early warning object, a target early warning mode and target early warning intensity based on the safety early warning related information and the target vehicle account information; wherein the target early warning object is a target bracelet or all bracelets bound with the vehicle; and based on the target early-warning mode and the target early-warning intensity, carrying out multi-modal early-warning notification in linkage with the target early-warning object. Multi-mode combined early warning is carried out through the vehicle and the target bracelet or all bound bracelets, so that the combined application of the bracelets and the vehicle is enriched, and the driving safety is improved.

The invention discloses an emergency early warning light control system based on deep learning, and the system comprises a data sensing module which is used for collecting visual features, radar features and thermal image features and carrying out the preprocessing; the deep learning analysis module is used for generating an anomaly score based on a feature adaptive weight LOF, and outputting an accident probability and an accident position through feature channel attention and improved DeepFM; the intelligent decision and control module is used for generating an early warning light control instruction according to the accident probability and the accident position; the communication and coordination module is used for transmitting the control instruction to roadside early warning light equipment; the early warning light execution module is used for switching a red early warning mode, a yellow warning mode or a normal light mode according to the control instruction; and the monitoring and operation and maintenance module is used for collecting the light operation state and abnormal information and executing maintenance processing. According to the invention, efficient cooperation of road accident real-time identification and light linkage early warning is realized, and the accident response speed and the driving safety level are improved.

The invention discloses a high-value traffic scene image screening method and device based on null-space features. The method comprises the following steps: mining a null space of a marked traffic scene image feature matrix, depicting unlearned feature dimensions of a model, quantifying the innovation of an unmarked sample in an unlearned feature space, and selecting a key image set capable of most effectively improving the model performance in combination with a coverage index. The method can explicitly describe the learned space and the unlearned space of the model, breaks through the limitation that traditional active learning depends on prediction uncertainty or sample surface difference, and enables the sample selection process to better fit the real learning state of the model. The method can be used for efficiently excavating images of key events such as rain and fog weather, road accidents, obstacle falling and low visibility at night.

The application discloses a kind of expressway confusing event discrimination method based on radar trajectory data, collect the driving trajectory of each car and the position information of real-time vehicle on current road surface, judge whether vehicle is stopped driving, stop driving vehicle is stored in set, there is vehicle stop driving, the number of vehicle around stop driving vehicle is counted, if it meets car many constraint condition, then continue to judge the average speed of surrounding vehicle, whether it is congestion state is judged according to speed, enter step 4;If it does not meet car many constraint condition, enter step 5;Using the way of deceleration grading, improve the distinction of each event;Multi-dimensional analysis and discrimination mode is used to each event, such as traffic flow, deceleration, distance, parking time, deceleration time, heading angle, pedestrian and other factors, using hierarchical analysis method, more comprehensive and meticulous discrimination congestion, car accident, car road accident, illegal parking, realize the purpose of low cost, high reliability, high precision, all-weather, full coverage discrimination confusing event.

The utility model provides a highway accident temporary warning device which comprises a base and a temporary warning indicator, the temporary warning indicator is detachably connected with the base, and the base is fixed to a set position beside a highway. The temporary warning indicator comprises a warning indicator shell and an active warning unit arranged in the warning indicator shell; the active warning unit comprises a warning lamp LED, a lithium battery, a lithium battery charging circuit and a lithium battery power supply control circuit, the input end of the warning lamp LED is connected to the output end of the lithium battery power supply control circuit, and the input end of the lithium battery power supply control circuit is connected to the positive electrode of the lithium battery; the lithium battery charging circuit is used for managing charging of the lithium battery; the warning indicator shell is provided with a light-transmitting window, and the light-transmitting window is sealed by a transparent material; a reflective tape is arranged on the outer side wall of the warning indicator shell.

The invention relates to a road width-limited area safety passage control system, which comprises a signal capturing mechanism arranged at a road width-limited area and used for executing field image signal capturing operation on an automobile which is about to pass to the road width-limited area; the signal detection mechanism is used for intelligently identifying an entity width value of a vehicle which is about to enter the road width limiting area; and the state identification mechanism is used for sending a width limiting reminding command when the received entity width value is greater than the maximum passing width corresponding to the road width limiting area. The safe traffic control system for the road width-limited area is compact in design, safe and reliable. On the basis of customized visual data intelligent analysis, when the entity width value of the vehicle which is about to enter the width-limited road surface area is greater than the maximum passing width corresponding to the road width-limited area through intelligent analysis, the width-limited reminding command is sent out, so that the number of traffic road surface accidents is reduced.

The present disclosure is about a process and a system for evaluating the driving performance in particularly to the process that evaluates the road-risk factors of a driver through driver's quality measurement using a Drivuum Continuum Score (702). The said driver evaluation system (100) comprises of a driver platform (200), a Drivuum card (300), a processor (400), a user platform (500), a call center platform (600) and a driver evaluation process. The driver evaluation process is performed under five different components which includes physiological fitness (204), psychomotor skills (205), cognitive skills (206), road¬ man-ship (207) and post-rating component (208). The Drivuum Continuum Score (702) which takes a 360-degree view with safety concerns, is utilized for safe certification and it provides a sense of security to the traveler, end users, employers etc.