430 results about "Emergency vehicle" patented technology

The invention discloses an intelligent traffic signal device and a remote control method, and relates to the technical field of intelligent traffic control. The method is used for solving the problems of low emergency traffic efficiency and global and local control imbalance under sudden traffic events. The method comprises the following steps: firstly, fusing an emergency vehicle navigation path, accident point vehicle motion abnormal parameters and pedestrian aggregation distribution data, constructing a multi-dimensional event evolution feature vector through a space-time encoder, and accurately describing a traffic situation; an event influence domain boundary is delimited based on vehicle trajectory and flow direction consistency analysis, a dynamic evolution model is constructed in combination with an acceleration abrupt change propagation path, and a space-time conflict probability matrix is generated; then executing a hierarchical control strategy, dynamically adjusting an intersection signal period starting time difference in a global level to form an emergency green wave coordination band, and inserting an adaptive full red phase in a local level according to a balance relation between a vehicle arrival rate and a dissipation rate; and finally, reversely correcting model parameters through an actual pass error, updating a conflict probability generation rule, and forming a closed-loop feedback mechanism.

The invention discloses an intelligent signal lamp phase optimization method and system based on vehicle infrastructure cooperation. According to the invention, through the dynamic game model, the system can sense multi-dimensional states such as traffic flow and pedestrian waiting time in real time, dynamically adjust demand weights of vehicles, pedestrians and roadside facilities, flexibly allocate green light duration, i.e., multi-direction multi-passing time of vehicles and multi-pedestrian time period to preferentially guarantee the street crossing demand; the average waiting time of vehicles and pedestrians is effectively shortened, the overall traffic efficiency of the intersection is obviously improved, and the suitability of daily traffic operation is obviously improved. The dynamic game model rapidly recognizes special requirements through a state value function, dynamically improves the weight priority of emergency vehicles or pedestrians, guarantees the efficient passing of key scenes (such as the rapid passing of emergency vehicles), avoids the long-time overstock of vehicles in other directions, and improves the safety of emergency vehicles or pedestrians. And the response capability and coordination level of the system to complex and emergent scenes are obviously enhanced.

The described aspects and implementations support sound-based emergency vehicle detection, localization, and tracking for autonomous vehicle and driver-assist systems. The techniques include obtaining, using one or more audio detectors of a vehicle, a sound recording that includes a sound emitted by an emergency vehicle (EV). The techniques further include applying a sound localization (SL) model to the sound recording to obtain a SL output, which includes a first map of possible locations of the EV in a driving environment of the vehicle and can further include a second map of possible velocities of the EV. The techniques further include simulating, using the SL output, trajectories of simulated EV(s) in the driving environment of the vehicle, and causing, responsive to proximity of the simulated trajectories to a driving path of the vehicle, modification of the driving path of the vehicle.

The invention provides a smart city split emergency management method and system based on an Internet of Things large model, and the method is executed by an emergency supervision management platform of the system, and comprises the steps: determining at least one accident based on target data; determining a data criticality based on the target data, the data basic features and the at least one accident; determining a data emergency feature based on the target data, the data abnormal feature and the data criticality; determining at least one target sub-platform based on the target data, the data exception features and the division conditions; determining emergency parameters based on the emergency type, the emergency degree and the data emergency characteristics; determining working parameters of the emergency vehicle based on the emergency degree, the emergency parameters and the data emergency characteristics; and based on the emergency parameters and the working parameters, generating and sending a scheduling instruction, and controlling the emergency vehicle to drive to the geographic area to work. According to the method and the system, accurate determination and dynamic allocation of emergency resources can be realized, and the overall efficiency of urban emergency management is improved.

The invention discloses an emergency vehicle road section priority passing control method considering a network-connected automatic driving special road, and the method comprises the steps: collecting the vehicle state information of an emergency vehicle and all vehicles of a target road section, and guiding the emergency vehicle to enter the network-connected automatic driving special road of the target road section if the emergency vehicle enters the target road section, calculating a dynamic emptying area in real time; a networked automatic driving vehicle in a dynamic emptying area is used as an optimized vehicle, and a multi-target optimization model which is established by taking minimum emergency vehicle delay and minimum interference to surrounding vehicles as targets is used for optimizing a speed change value and a lane changing decision of the optimized vehicle so as to control the optimized vehicle. Therefore, the networked automatic driving vehicle which affects passing in front of the emergency vehicle is emptied. According to the method, the longitudinal following and transverse lane changing decisions of the networked automatic driving vehicles in the social vehicles in the mixed traffic flow environment are optimized, and the influence on the social vehicles is reduced while the emergency vehicles are guaranteed to pass preferentially.

A real-time traffic signal optimization system that includes: (a) a traffic data acquisition module that collects real-time traffic data from sensors, cameras, GPS and vehicle-to-infrastructure communication; b) a machine learning-based traffic prediction engine that analyses historical and real-time data to predict congestion patterns; c) an adaptive signal control unit that dynamically adjusts traffic light switching times based on real-time traffic flow and predictive insights; (d) an emergency and priority vehicle management system that overrides normal signal operation to give priority to emergency vehicles and public transport vehicles; (e) a central traffic monitoring and control dashboard for real-time monitoring, manual interventions and system configuration; and (f) a vehicle-to-infrastructure communication interface enabling data exchange between traffic signals and connected vehicles to improve driver awareness and traffic coordination.

A patient transport apparatus includes a support structure. The support structure includes a base, a frame, and a patient support surface to support a patient. One or more handle assemblies are coupled to the frame to maneuver the patient transport apparatus. The handle assemblies include one or more handles to be manipulated by a user. The handles are capable of being adjusted to facilitate maneuvering of the patient transport apparatus, such as pushing / pulling the patient transport apparatus along a floor surface, lifting the patient transport apparatus over obstacles, loading the patient transport apparatus into an emergency vehicle, and / or unloading the patient transport apparatus from the emergency vehicle.

The invention relates to a multi-type emergency vehicle signal dynamic priority control method based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: acquiring an emergency vehicle state and traffic environment data in real time through cooperation of a vehicle-mounted terminal, roadside equipment and a cloud control platform; the cloud control platform dynamically calculates priority scores based on vehicle types, task emergency degrees, predicted arrival time, real-time traffic influences and path complexity multi-dimensional factors; when multiple vehicles have conflicts, collaborative decision making is carried out based on scores; and finally, an optimized signal control strategy is generated and executed. The system effectively solves the problems that a traditional priority control mode is extensive, and traffic jam and multi-vehicle conflicts are easily caused, achieves the purpose that interference to social traffic is minimized while efficient passing of emergency vehicles is guaranteed, and improves the overall efficiency and safety of an urban traffic system.

The invention discloses an emergency vehicle rescue control method under a virtual emergency special lane, and solves the problems that an emergency vehicle is difficult to pass in time due to traffic jam, the lane changing efficiency is low, and the path interference factor is complex in the existing traffic environment. The method comprises an emergency vehicle rescue dynamic control model; establishing a simulation scene, deploying a model and performing training; vehicle and road information is obtained, an emergency vehicle lane changing strategy is obtained according to the emergency vehicle autonomous lane changing decision and the optimal emergency lane selection model, and an optimal virtual emergency lane is selected; and guiding social vehicles to give way according to the lane dynamic emptying frame model. According to the invention, lane traffic density is monitored, an emergency vehicle rescue control strategy is triggered, an emergency vehicle lane change decision is optimized by using an emergency vehicle autonomous lane change decision model, an optimal virtual emergency lane is dynamically selected based on an optimal emergency lane selection model, social vehicles are guided to give way through a lane emptying frame, and rapid passing of the emergency vehicles is ensured.

The invention discloses a traffic light control optimization method and system based on multi-source data interaction, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining a real-time traffic information data set of a target road section; constructing a real-time traffic twin model based on the real-time traffic information data set, and embedding a traffic prediction model for traffic jam prediction to obtain a jam result; dynamically determining a vehicle passing priority, and generating a traffic light control target based on the congestion result and the vehicle passing priority; randomly simulating different signal timing schemes through a real-time traffic twin model, determining a traffic efficiency index evaluation result set, and selecting an optimal control strategy according to the traffic efficiency index evaluation result set; and adjusting traffic light signals of the target road section based on the optimal control strategy. The technical problems that traffic light signal timing is not flexible and traffic flow changes cannot be adapted in real time in the prior art are solved, and the technical effects of improving the traffic flow efficiency, reducing traffic congestion and improving the priority traffic capacity of emergency vehicles are achieved.

The invention discloses an emergency vehicle pre-emptying method suitable for large-flow highway traffic cloud control, and relates to the technical field of highway management and control. The method comprises the steps that a frame of an emergency lane pre-emptying double-layer model is adopted, a lane changing vehicle and the position of each step length of the lane changing vehicle are obtained through a lower-layer model and transmitted to an upper-layer model, and then the upper-layer model is solved to obtain the lane changing starting position of a vehicle in front of the emergency vehicle. A lower-layer model of the double-layer model obtains an optimal lane changing track by optimizing the lane changing track of the front vehicle to achieve the minimum cost of the space of the lane where the emergency vehicle is located as soon as possible; the lower-layer model is solved based on an improved A * algorithm so as to improve the calculation efficiency; the upper-layer model serves as a bounded convex function to obtain an optimal solution of the double-layer model. In this way, in the process that the emergency vehicle does not change the lane and runs at the speed close to the expected speed, the passing efficiency loss of the front vehicle is reduced to the minimum through lane changing, and the influence of the emergency vehicle on front traffic is minimum.

A teleassist system may be used to assist with detecting and responding to an emergency vehicle present within an environment of an autonomous vehicle. The teleassist system can help confirm whether the emergency vehicle is active or inactive based on evaluating sensor data that is captured by one or more sensors of the autonomous vehicle and that is associated with the emergency vehicle. For example, the teleassist system can provide a teleassist operator input confirming whether a detected vehicle is an emergency vehicle based on evaluation of various aspects, including siren, vehicle shape, vehicle color, and / or flashing light emitted by flash device(s) of the vehicle, etc. The teleassist operator input can further include one or more suggested autonomous vehicle control operations that enable the autonomous vehicle to comply with detection of an emergency vehicle.

A system and method is provided for detecting the approach of official and emergency vehicles and alerting drivers. The system is comprised of a vehicle device, a client device, a local device and a dongle. The local device includes a set of acoustic sensors and a set of light wave sensors. The local device is connected to the dongle. The dongle is connected to the local device, the vehicle device and the client device. In use, the sensors record analog light wave and acoustic signals. The signals are processed through a series of rolling frequency and amplitude summary tables to determine the type of emergency vehicle and whether or not it is approaching. If so, an alert is generated and sent to the vehicle device and the client device where it is displayed.

The invention belongs to the technical field of vehicle-road cooperation, discloses a vehicle cooperative control method for an emergency vehicle, and solves the problem that local traffic jam is easily caused due to the lack of precise linkage because the driving state of social vehicles around the emergency vehicle is not considered in a traditional scheme. Therefore, the influence on other social vehicles is reduced while the emergency vehicle passing efficiency is guaranteed. According to the scheme, the traffic data information of the emergency vehicle and the social vehicles around the emergency vehicle is obtained in real time, and when it is judged that the emergency vehicle is currently driven on the lane, the dynamic interval of the emergency vehicle is established according to the current lane where the emergency vehicle is located and the current traffic data information of the emergency vehicle; when other social vehicles in the dynamic interval are judged to meet the lane changing condition, lane changing guiding information is sent to the corresponding social vehicles; when the emergency vehicle arrives at the intersection detection area, traffic light control is executed according to the current traffic data of the emergency vehicle, the length of the intersection detection area and the length of the intersection in combination with the current traffic light state; and finally, after the emergency vehicle arrives at a designated position, ending the vehicle-road cooperative control algorithm.

The invention relates to the technical field of intelligent traffic, and discloses an emergency vehicle priority passing control method based on parameterized reinforcement learning, and the method comprises the following steps: firstly constructing an urban road network; according to the position of the emergency vehicle, using the established dynamic decision model to determine a target lane to which the vehicle is about to drive and the continuous driving time in the lane; then, a multi-agent dynamic collaborative lane changing model is used for controlling vehicles on a target lane to change lanes and give way; and finally, coupling the two models, and verifying the actual effect of the two models on an urban road with a certain length. Compared with the prior art, the method solves the problem that emergency vehicles on urban roads are blocked by social vehicles and cannot pass through, ensures that the emergency vehicles quickly pass through the road sections at low delay, reduces secondary accidents while minimizing interference to the social vehicles, and provides an efficient system architecture for priority passing of the emergency vehicles on the urban roads.

The invention provides an intersection priority passing method, device and equipment for an emergency vehicle and a medium, and relates to the technical field of traffic signal control, and the method comprises the steps: generating a passing route of the emergency vehicle according to an emergency planning route of the emergency vehicle and the predicted passing time between every two adjacent intersections in the emergency planning route, the predicted passing time is obtained according to a current traffic state and a historical traffic state, and the passing route comprises information of a plurality of intersections through which the emergency vehicle sequentially passes; under the condition that the emergency vehicle drives into the upstream preset distance of each intersection in the passing route, the green light time of the emergency passing direction of the intersection is prolonged; according to the traffic signal when the emergency vehicle is predicted to arrive at the stop line of each intersection in the passing route, the corresponding green light extension strategy or the red light early cut-off strategy is adopted to adjust the timing parameter of the traffic signal when the emergency vehicle is predicted to arrive at the intersection, and the problem of priority passing of the emergency vehicle at the intersection can be solved.

The invention belongs to the field of intelligent traffic, discloses an emergency vehicle priority and queuing optimization cooperative control method, and solves the problem of cooperation of emergency priority and social vehicle dispersion at a peak-saturated intersection. The method comprises the following steps: constructing a road network model containing a center intersection and four peripheral intersections, and collecting multi-source traffic data; a data set is established after preprocessing, and an LSTM-Transform double-branch model is trained to predict queuing and emergency tracks; constructing an intelligent agent based on PPO, and combining segmented reward training; and deploying an output signal decision. According to the invention, emergency low delay is guaranteed, social vehicle queuing is optimized, peak intersection efficiency is improved, and the method is suitable for high-saturation scenes; according to the method, the social vehicle congestion is effectively relieved while the rescue vehicles are guaranteed to pass preferentially, and the overall operation efficiency and the emergency response capability of the intersection in the complex traffic environment are remarkably improved.

A patient transport apparatus includes a support structure. The support structure includes a base, a frame, and a patient support surface to support a patient. One or more handle assemblies are coupled to the frame to maneuver the patient transport apparatus. The handle assemblies include one or more handles to be manipulated by a user. The handles are capable of being adjusted to facilitate maneuvering of the patient transport apparatus, such as pushing / pulling the patient transport apparatus along a floor surface, lifting the patient transport apparatus over obstacles, loading the patient transport apparatus into an emergency vehicle, and / or unloading the patient transport apparatus from the emergency vehicle.

The invention discloses an emergency vehicle traffic scheduling system and method based on vehicle-road cloud integration, the system comprises a cloud control basic platform constructed based on vehicle-road cloud integration, the cloud control basic platform constructs a global traffic digital twin base by converging vehicle dynamic data, roadside sensing information and cloud global resources in real time, and the traffic digital twin base is connected with the cloud control basic platform. An efficient cooperative data transmission channel of the vehicle end, the road end and the cloud end is opened; and meanwhile, real-time interaction of multi-dimensional information of people, vehicles, roads and environments is guaranteed through a V2X communication technology. According to the scheme, multivariate data interaction and fusion are realized by constructing a cloud control basic platform through a vehicle-road cloud cooperation technology, emergency vehicles and related traffic information are acquired, and by utilizing high-precision positioning and high-frequency data transmission, when a network-connected vehicle exists in front of the emergency vehicles or encounters an intersection, a way-giving prompt is sent to surrounding vehicles, so that the emergency vehicles are prompted to walk. Therefore, the emergency vehicle passing efficiency is improved.

An apparatus comprising: at least one transceiver; at least one memory; and at least one processor, in signal communication with the at least one transceiver, and the at least one memory, the at least one processor configured to: receive an alert signal of an emergency vehicle approaching the vehicle; and produce an alert notification on an infotainment system in response to receiving the alert signal.

Systems, methods, and other embodiments described herein relate to improving alerts for emergency vehicles. In one embodiment, a method includes, responsive to detecting, using an electronic device, an alert signal from a nearby vehicle, determining one or more characteristics of the nearby vehicle from at least the alert signal. The method further includes providing an alert according to the one or more characteristics.

An autonomous vehicle including one or more sensors positioned on a body of the autonomous vehicle, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, configure the at least one processor to: (i) receive sensor data from the one or more sensors; (ii) determine, from the sensor data, presence of an emergency situation and absence of an emergency vehicle at a site of the emergency situation; (iii) in accordance with the determining, trigger a first responder mode of the autonomous vehicle; and (iv) initiate one or more actions to assist or protect people or a property at the site of the emergency situation.

For training a perception algorithm to detect an emergency vehicle, respective audio datasets are received from two microphones and respective spectrograms are generated. At least one interaural difference map is generated based on the spectrograms, audio source localization data is generated, which specifies a number of audio sources in respective grid cells of a spatial grid, by applying a CRNN to first input data containing the spectrograms and the least one interaural difference map. An image is received from a camera and output data comprising a bounding box for the emergency vehicle is predicted by applying at least one further ANN to second input data containing the image and the spectrograms. Network parameters are adapted depending on the output data and the audio source localization data.

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

A lightbar for an emergency vehicle is provided. The lightbar includes lights and a camera within the same unit. An opaque and hollow cylindrical tube runs from the lens of the camera to an inside surface of the lightbar. The tube prevents IR light that reflects from the inside surface of the lightbar from entering the lens. The tube also prevents IR light and light from LED lights within the lightbar from entering the lens directly. Settings on the camera are set so that image capture is filtered at a frequency of 50 Hz and 60 Hz. By providing such a tube and by filtering out image capture at 50 Hz and 60 Hz, images captured by the camera are greatly improved in quality.

The invention discloses an energy internet park EV charging scheduling system based on dynamic priority and battery life optimization, and the system comprises a dynamic priority distribution module which monitors the SOC of a vehicle battery in real time, and inserts a vehicle into the front end of a charging queue when the SOC is less than or equal to a preset threshold value; the common queues are ranked according to a first-arrival first-service principle, and the common vehicles are continued after the emergency vehicles are jumped; the lithium battery degradation calculation module is used for quantifying the battery capacity degradation rate based on a multi-stress factor model of the charging depth, the average SOC, the temperature and the charging duration; the multi-stress factor model calculates the degradation amount through chemical characteristic parameters calibrated through experiments; the multi-target optimization module is used for integrating optimization algorithms for dynamically switching global exploration and local mining and generating a scheduling strategy by taking the minimum charging cost, the user waiting time and the battery degradation cost as targets; according to the optimization algorithm, the solving efficiency is improved through a population diversity tracking strategy and an elite-random double-search mechanism.

The embodiment of the present specification provides a smart gas emergency gas supply device, which is an emergency vehicle, the emergency vehicle comprising a skid-mounted vehicle, a gas storage tank, an extendable pipeline group, a pressure regulating device, a gasification device and a control module, wherein the control module is configured to determine the operating parameters of the pressure regulating device and the gasification device based on an emergency gas supply scheme of the smart gas emergency gas supply device, wherein the emergency gas supply scheme at least comprises a gas source selection scheme and a gas demand, and the gas source selection scheme at least comprises gas pipeline network cross connection and gas tank direct supply.

The invention discloses a hybrid reinforcement learning traffic signal hierarchical control method, and the method comprises the steps: collecting the queue length of an upstream lane and a downstream lane of each intersection, obtaining a phase pressure value through the weighted sum corresponding to the queue length of the upstream lane and the queue length of the downstream lane, and combining the queue length with the phase pressure value to obtain local traffic data. And when the congestion or emergency vehicles are not detected, the plurality of bottom layer maximum pressure controllers are utilized to obtain the optimal phases corresponding to the intersections through a maximum pressure algorithm and local traffic data so as to control the traffic signals of the intersections. And integrating local traffic data of each intersection to obtain global traffic data, encoding the global traffic data through a graph neural network, and learning a global optimal strategy through encoded features by a global near-end strategy optimization controller. And when a congestion or emergency vehicle is detected, the global near-end strategy optimization controller adjusts the current phase by using a global optimal strategy, and issues the adjusted phase to each intersection to control traffic signals.

The invention provides a multi-mode-based emergency vehicle processing method and device, a vehicle and a medium, and relates to the field of data / image processing, in particular to the fields of map navigation, automatic driving and intelligent transportation. According to the specific implementation scheme, the driving direction of the emergency vehicle and the relative position of the emergency vehicle and the vehicle are determined through collected audio data, and an avoidance strategy pushed to the emergency vehicle when emergency avoidance is needed is determined through collected image data of the emergency vehicle so that the emergency vehicle can be rapidly avoided when approaching the vehicle. By means of the method, the passing efficiency of emergency vehicles in the urban traffic environment is improved, and smoothness of a green'life channel 'is ensured.

A vehicle control device autonomously controls driving of a subject vehicle. The vehicle control device is configured to grasp information; and determine autonomous driving control of the subject vehicle. The vehicle control device grasps information about an environment of an intersection where the subject vehicle is scheduled to travel and information about an emergency vehicle present around the subject vehicle. The vehicle control device decides whether to execute evacuation control for evacuating the subject vehicle from the emergency vehicle. The vehicle control device searches for an evacuation position that is deviated from a prediction track of the emergency vehicle and from which the subject vehicle is configured to return to the traveling direction, and decides to evacuate the subject vehicle to the evacuation position.