CN111402588A - High-precision map rapid generation system and method for reconstructing abnormal roads based on space-time trajectory - Google Patents
High-precision map rapid generation system and method for reconstructing abnormal roads based on space-time trajectory Download PDFInfo
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Abstract
The invention discloses a high-precision map rapid generation system and a method for reconstructing abnormal roads based on space-time trajectories, wherein the system comprises the following steps: the system comprises a road side sensing unit, a vehicle-mounted unit and a big data service platform, wherein the road side sensing unit acquires dynamic and characteristic information of a running vehicle and acquires road condition and traffic state information, the vehicle-mounted unit acquires track information of the vehicle and surrounding environment information, the acquired data are all transmitted to the big data service platform for reverse space-time track reconstruction, the reconstructed data are in differential comparison with original high-precision map data in an area, a difference part and an original high-precision map are overlapped to generate a brand new road high-precision map, abnormal road information acquired by the road side sensing unit and the vehicle-mounted unit is fused to the brand new road high-precision map, abnormal positions are marked on the map, and a plurality of user terminals can call the abnormal positions. The invention solves the problems that the prior map can not feed back the road burst state in time and can not generate a new traffic scheme quickly.
Description
Technical Field
The embodiment of the invention relates to the fields of space-time trajectory reconstruction, road side measurement, road surveying, three-dimensional model generation, map data generation, high-precision map drawing, automatic navigation, unmanned driving, automatic control, data transmission, road reconstruction and the like, in particular to a system and a method for quickly generating a high-precision map of an abnormal road based on space-time trajectory reconstruction.
Background
The existing high-precision map collects road information in advance through a road reconnaissance measuring vehicle, then the high-precision map is drawn, a user carries out navigation by combining a positioning device after loading the map, however, when the road surface has sudden conditions, dangerous pedestrians and animals invade, objects are thrown away, debris flow, road collapse and the like, and the situation that partial lanes cannot normally pass is caused, the original high-precision map cannot respond in time, and the vehicle about to pass through the road section is informed in advance. The information can not be fed back to the user quickly under the conditions of road congestion and queuing. Particularly, for unmanned vehicles and automatic vehicles, the requirement for road state information is high in precision, and if the feedback is not timely, early warning cannot be performed in advance, so that great threat is caused to the road driving safety.
Intelligent transportation systems have created huge database resources where there is implicitly a large amount of knowledge useful for path planning. For example, in a traffic navigation system, historical similar motion trajectories can be obtained according to a similarity query technology for the trajectories, and a group of more appropriate driving routes or estimated required time can be recommended to novice or lost drivers based on the obtained knowledge. In addition, the motion modes of most vehicles in the area where traffic jam frequently occurs can be found through similarity query of the tracks, and suggestions such as planning and developing some new roads or providing road condition information for traveling vehicles and the like can be provided according to the results. Clustering analysis, which is a task of data mining technology, is a data preprocessing process, which is the basis for further analyzing and processing data. The moving object track clustering technology based on the road network space can provide powerful guarantee for the reasonability and the optimality of path planning, corresponding decision support is provided for users, and the research of track clustering has important significance in practical application.
Disclosure of Invention
Therefore, the invention provides a system and a method for quickly generating a high-precision map for reconstructing an abnormal road based on a space-time trajectory, which are used for solving the problems that the conventional map cannot timely feed back the road burst state and cannot quickly generate a new traffic scheme.
In order to achieve the above purpose, the invention provides the following technical scheme:
according to the first aspect of the invention, a system for quickly generating a high-precision map of an abnormal road based on space-time trajectory reconstruction is disclosed, and the system comprises: the system comprises a road side sensing unit, a vehicle-mounted unit and a big data service platform, wherein the road side sensing unit is arranged at the side edge of a road, the road side sensing unit acquires dynamic information, road condition information, characteristic information, traffic state information, climate information and various abnormal event information of all types of vehicles running on the road, the vehicle-mounted unit is arranged in an unmanned vehicle, an automatic driving vehicle, a manual auxiliary driving vehicle and a road surveying and mapping vehicle, the position information of the vehicle, the road condition information around the vehicle, the environment information, identification marking line information, event information damaging the safe running of the vehicle, road safety running auxiliary infrastructure and various operation data information of the safe running of the vehicle can be dynamically acquired in real time through various vehicle-mounted sensors and a positioning module, and the data acquired by the road side sensing unit and the vehicle-mounted unit are transmitted to the big data service platform, the big data service platform carries out reverse space-time trajectory reconstruction according to the acquired data, carries out differential comparison on the reconstructed data and the original high-precision map data of the area, superposes the difference part with the original high-precision map to generate a brand-new road high-precision map, superposes and fuses the abnormal road condition information, the abnormal traffic incident accident information and the abnormal traffic state information acquired by the road side sensing unit and the vehicle-mounted sensor into the brand-new road high-precision map, the position, the influence range and the development situation of the new road high-precision map are marked, the early warning and warning prompt information content is generated, and the passing scheme of normal and safe running of the vehicle is allowed, so that the special high-precision map is provided for the road required by the safe running of the unmanned vehicle, the automatic vehicle and the manual auxiliary driving vehicle, and is used for being called by different user terminals.
Further, the road side sensing unit collects all types of vehicle dynamic information, road abnormal event information, vehicle characteristic information, traffic state information, climate information and vehicle abnormal event information which are driven on a road, and the vehicle dynamic information comprises: the real-time motion speed information, the motion direction information, the longitude and latitude information, the acceleration information, the motion direction angle information, the vehicle size information, the vehicle type information, the lane information, the motion track information and the unique ID identification number information of the vehicle in the whole system of each vehicle; the vehicle abnormal event information includes: the vehicle has abnormal conditions and abnormal behaviors; the road abnormal event information includes: whether dangerous pedestrians appear or not, whether dangerous animals appear or not, whether road landslide exists or not, whether sprinkled objects appear or not, whether falling rocks appear or other dangerous conditions influencing normal road traffic exist or not; the vehicle characteristic information includes: vehicle license plate information, vehicle logo information, vehicle series information, vehicle type information and vehicle color information; the traffic state information includes: traffic, congestion, and queuing information. The road side sensing unit transmits various collected data to the big data service platform for calling through a special communication channel and a communication mode.
Further, the on-board unit includes: the system comprises a vehicle real-time positioning module and vehicle sensors, wherein the vehicle real-time positioning module comprises a Beidou system, a Galileo system and a GPS (global positioning system), and provides periodic real-time position longitude and latitude information, speed information and a clock synchronization time service function for a vehicle through various sensors and the real-time positioning module in the vehicle running process; the vehicle sensor comprises a vehicle surrounding sensing unit which is responsible for sensing the environment around the vehicle, mapping the environment around the vehicle, determining the position of the vehicle at any time and providing decision-making capability of safe driving for the vehicle in various driving scenes through sensing data; the vehicle-mounted unit transmits various collected data to the big data service platform for calling through a special communication channel and a communication mode.
Furthermore, the road side sensing unit and the vehicle-mounted unit transmit various collected information to the big data service platform through a wireless transmission device or a wired network circuit, and the big data service platform integrates and analyzes the received data.
Furthermore, the big data service platform receives data sent by the road side sensing unit and the vehicle-mounted unit, then carries out real-time analysis and processing, carries out reverse space-time track reconstruction through the processed data, draws a motion track and a driving path of a vehicle by utilizing vehicle point tracks collected every second, draws the number of lanes of a section of an area and the boundary of the road through the driving track or the driving path of the vehicle, forms graphic data of the whole road through superposition of continuous road sections, and fuses identification marking line information and safety auxiliary infrastructure information together to form road graphic data information, lane data information, actual vehicle driving track information, path information and vehicle driving direction information for brand new reference.
Furthermore, the big data service platform compares the generated road graphic data information for brand-new reference, lane data information, actual vehicle driving track information, path information, identification marking line information, safety auxiliary infrastructure information and vehicle driving direction information with the road graphic information, lane data information, planning vehicle driving track information, path information, road infrastructure information, safety protection information, identification marking line information and vehicle driving direction information contained in the original region or high-precision map of the road stored in the system to find different places, superposes the different places with the original high-precision map to generate a brand-new high-precision map, and corrects all related content data information in the brand-new high-precision map respectively according to the principle of constructing different roads, driving lanes and auxiliary facilities by highway, national provincial road and urban road infrastructure, the method comprises the following steps: the road comprises the following components of road traffic lane width, the number of lanes, correct driving paths, road basic equipment, safety protection facilities, road boundaries, mark lines and driving directions.
Furthermore, the big data service platform fuses and identifies data information collected by the road side sensing unit and the vehicle-mounted unit, marks positions, areas and lanes of various abnormal events which harm safe driving of vehicles on a brand-new road high-precision map, and forms a complete high-precision map after secondary fusion of the data and stores the complete high-precision map into the navigation system.
Furthermore, the big data service platform marks the abnormal event information collected by the road side sensing unit and the vehicle-mounted unit on a completely new high-precision map which is generated completely, automatically generates an early warning region which extends forwards towards the driving direction of the vehicle by taking the position of the abnormal event as the center according to the event type, the range, the region and the lane influenced by the event type and combining the safe driving principle of the vehicle, generates early warning prompt information of the content of the abnormal event on the road, converts the motion track and the running direction of all the driving vehicles in the range of the abnormal region into the information of the driving path, the driving direction and the available lane of the abnormal region allowing the vehicle to normally drive, issues warning information to all the vehicles which are going through the road section in advance to warn all the passing vehicles to slowly and safely drive according to the optimal driving path and the passing scheme which are automatically generated by the system, and prompting the driver of the automatic driving vehicle to convert the automatic driving mode of the vehicle into a manual driving mode so as to improve the safety of the vehicle and the smoothness of the road.
According to the second aspect of the invention, a method for quickly generating an abnormal road high-precision map based on space-time trajectory reconstruction is disclosed, and the method comprises the following steps: the method for acquiring all types of vehicles running on the road by using the road side sensing unit comprises the following steps: unmanned vehicles, autonomous vehicles, manually assisted driving vehicles, fully manually driven vehicles; the information collected includes: vehicle dynamic information, road abnormal event information, vehicle characteristic information, traffic state information, climate information and vehicle abnormal event information; the vehicle dynamics information includes: the real-time motion speed information, the motion direction information, the longitude and latitude information, the acceleration information, the motion direction angle information, the vehicle size information, the vehicle type information, the lane information, the motion track information and the unique ID identification number information of the vehicle in the whole system of each vehicle; the vehicle abnormal event information includes: the vehicle has abnormal conditions and abnormal behaviors; the road abnormal event information includes: whether dangerous pedestrians appear or not, whether dangerous animals appear or not, whether road landslide exists or not, whether sprinkled objects appear or not, whether falling rocks appear or other dangerous conditions influencing normal road traffic exist or not; the vehicle characteristic information includes: vehicle license plate information, vehicle logo information, vehicle series information, vehicle type information and vehicle color information; the traffic state information includes: the roadside sensing unit transmits the various collected data to a big data service platform for calling through a special communication channel and a communication mode;
the on-board unit includes: real-time orientation module of vehicle and vehicle sensor, the real-time orientation module of vehicle includes: the system comprises a Beidou system, a Galileo system and a GPS (global positioning system) positioning system, and is used for providing periodic real-time position longitude and latitude information, speed information and clock synchronization time service functions for a vehicle; the vehicle sensor consists of vehicle sensing unit cells around the vehicle and is used for sensing the environment around the vehicle and mapping the environment around the vehicle, so that the position of the vehicle can be determined at any time, and the vehicle can be subjected to decision-making capability of safe driving under various driving scenes through sensing data; the method comprises the steps that position information of a vehicle, surrounding road condition information, environment information, identification marking information, event information damaging safe driving of the vehicle, road safe driving auxiliary infrastructure and various operation data information of safe driving of the vehicle are obtained dynamically in real time through a vehicle-mounted sensor, and a vehicle-mounted unit transmits various collected data to a big data service platform for calling through a special communication channel and a communication mode;
the data collected by the road side sensing unit and the vehicle-mounted unit are transmitted to a big data service platform, the big data service platform conducts reverse space-time trajectory reconstruction according to the collected data, the reconstructed data and the original high-precision map data of the area are subjected to differential comparison, the difference part and the original high-precision map are superposed to generate a brand new road high-precision map, and abnormal event information collected by the road side sensing unit and the vehicle-mounted unit is fused into the brand new road high-precision map;
the system marks the position of an abnormal event in a brand-new road map, regenerates a complete brand-new high-precision map, automatically generates an early warning area which extends forwards to the driving direction of the vehicle by taking the position of the abnormal event as the center according to the type of the event, the range, the area and the lane influenced by the event and combining the safe driving principle of the vehicle, generates early warning prompt information of the content of the abnormal event on the road, converts the motion track and the running direction of all driving vehicles in the range of the abnormal area into the driving path, the driving direction and the information of the available lane of the abnormal area, sends the information to all vehicles and other clients which are going to pass through the road section, sends warning information in advance to warn all passing vehicles to slowly and safely drive according to the optimal driving path and the passing scheme automatically generated by the system, and prompting the driver of the automatic driving vehicle to convert the automatic driving mode of the vehicle into a manual driving mode so as to improve the safety of the vehicle and the smoothness of the road.
The invention has the following advantages:
the invention discloses a system and a method for quickly generating a high-precision map of an abnormal road based on space-time trajectory reconstruction.
The high-precision map formed by a multi-system, multi-aspect and multi-integration mode has higher reliability than high-precision map data generated by a single measuring device or a single data source.
By the system and the method, when the road condition state and the traffic state change, abnormal traffic accidents occur on the road, and the driving safety is endangered by obstacles, the corresponding high-precision map, early warning information and prompt information can be rapidly generated according to the space-time trajectory reconstruction mode, so that major traffic accidents caused by unmanned vehicles, automatic vehicles and manual auxiliary driving vehicles are avoided, and secondary accidents are avoided.
By using the system and the method, the problems of traffic accidents or inconvenient travelling caused by untimely updating of high-precision map data and inaccurate data information can be effectively avoided.
The system and the method can effectively reduce or avoid the cost of using a special high-precision map measuring vehicle and a high-precision map generated by manual secondary processing.
By using the system and the method, traffic accidents and secondary accidents caused by the change of the driving path due to the fact that the road side single sensing equipment cannot detect obstacles, sprinkles, collapses, roadblocks and temporary traffic control which are long in distance, small in size and harmful to the obstacles, can be effectively avoided.
Under the premise of guaranteeing the safe driving of the vehicle, the mode assists the vehicle to change the driving state as follows: lane changing, overtaking, constant-speed driving and the like;
by the method, the large-range cooperative running of all types of vehicles is realized, and the traffic efficiency of the whole road is further improved.
Data obtained through space-time trajectory reconstruction can form a vehicle 'safe driving model' through further specification and design to guide all running vehicles on a road to safely drive and avoid danger;
the data obtained by space-time trajectory reconstruction is further standardized and designed to form a danger early warning model, vehicles which are illegal, abnormal in driving and abnormal in behavior can be warned and prompted, abnormal behavior warning information is immediately output once a relevant judgment mechanism is triggered, and point-to-point information prompting and evidence obtaining are carried out on the corresponding vehicles;
the system and the method can realize the safe and effective management of vehicle running in the behaviors of monitoring the whole process, controlling the vehicle to change lanes illegally, running at an overspeed, occupying an emergency parking lane for a long time, occupying a fast lane by a truck for a long time, escaping and the like;
the data obtained by the system and the method can enable the road sensing equipment and the edge computing equipment to be combined with each other to form a safety guarantee system with larger functions, provide enough decision basis and even instructions for unmanned vehicles, automatic vehicles and manual auxiliary driving vehicles, and improve the driving safety of the unmanned vehicles and the automatic vehicles essentially.
The system and the method can greatly reduce the development complexity of the unmanned vehicle and the automatic vehicle and greatly reduce the cost. Unmanned, autonomous commercialization can also come in advance because it does not need to traverse all scenes.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
FIG. 1 is a schematic working diagram of a system for quickly generating a high-precision map of an abnormal road based on spatiotemporal trajectory reconstruction, according to an embodiment of the present invention;
FIG. 2 is a flow chart of a system architecture for quickly generating a high-precision map of an abnormal road based on spatiotemporal trajectory reconstruction, according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for quickly generating a high-precision map of an abnormal road based on spatiotemporal trajectory reconstruction, provided by an embodiment of the present invention.
In the figure: the method comprises the following steps of 1-road side sensing unit, 2-vehicle-mounted unit, 3-common vehicle, 4-abnormal event, 5-early warning area, 6-vehicle abnormal track and running path, 7-vehicle normal track and running path, 8-original high-precision map of road area, 9-reference area road map, 10-brand new road base high-precision map, 11-high-precision map with correct running path, 12-high-precision map with early warning information, 13-brand new road high-precision map, 14-vehicle running path and lane running direction, and 15-early warning area and warning prompt information.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 and fig. 2, the present embodiment discloses a system for reconstructing a high-precision map of an abnormal road based on a spatiotemporal trajectory, the system comprising: the road side sensing unit 1 is arranged on the side of a road, and collects dynamic information, characteristic information, road condition information, traffic state information, climate information and various abnormal event information of all types of vehicles running on the road in real time. The vehicle-mounted unit 2 is installed in an unmanned vehicle, an automatic vehicle, an artificial auxiliary driving vehicle and a road surveying and mapping vehicle, the vehicle-mounted unit 2 can dynamically acquire position information of the vehicle, surrounding road condition information, environment information, marking line information, event information damaging safe driving of the vehicle, road safe driving auxiliary infrastructure, various operation data information of safe driving of the vehicle and the like in real time through various vehicle-mounted sensors, data acquired by the road side sensing unit 1 and the vehicle-mounted unit 2 are transmitted to a big data service platform, the big data service platform carries out reverse space-time trajectory reconstruction according to the acquired data, the reconstructed data is differentially compared with original high-precision map data in the area, the difference part is superposed with the original high-precision map to generate a brand-new road base high-precision map, and the road condition information acquired by the road side sensing unit 1 and the vehicle-mounted unit 2, And the traffic state information, the abnormal event information, the environment information, the identification marking line information and the road safety driving auxiliary infrastructure information are fused into a brand-new road base high-precision map. The system uploads a brand-new high-precision map to a cloud or a third-party service platform for different clients to call and use.
The road side sensing unit 1 collects all types of vehicle dynamic information, road abnormal event information, vehicle characteristic information, traffic state information, climate information and vehicle abnormal event information which are driven on a road, and the vehicle dynamic information comprises: the real-time motion speed information, the motion direction information, the longitude and latitude information, the acceleration information, the motion direction angle information, the vehicle size information, the vehicle type information, the lane information, the motion track information and the unique ID identification number information of the vehicle in the whole system of each vehicle; the vehicle abnormal event information includes: the vehicle has abnormal conditions and abnormal behaviors; the road abnormal event information includes: whether dangerous pedestrians appear or not, whether dangerous animals appear or not, whether road landslide exists or not, whether sprinkled objects appear or not, whether falling rocks appear or other dangerous conditions influencing normal road traffic and the like; the vehicle characteristic information includes: vehicle license plate information, vehicle logo information, vehicle series information, vehicle type information, vehicle color information and the like; the traffic state information includes: information such as smooth, congested, blocked, queued, etc. The road side sensing unit transmits various collected data to the big data service platform for calling through a special communication channel and a communication mode, and the big data service platform draws preliminary basic data information and graphs of a brand new road map through the information big data service platform in a space-time track reconstruction mode.
The vehicle-mounted unit 2 positions the vehicle in real time, records the motion track of the vehicle, and acquires road condition information, environmental information, identification marking information, event information damaging safe driving of the vehicle, road safety driving auxiliary infrastructure information, various operation data information of safe driving of the vehicle and the like around the vehicle. The on-board unit includes: the system comprises a vehicle real-time positioning module and vehicle sensors, wherein the vehicle real-time positioning module comprises a Beidou system, a Galileo system and a GPS (global positioning system), and provides periodic real-time position longitude and latitude information, speed information and a clock synchronization time service function for a vehicle through various sensors and the real-time positioning module in the vehicle running process; the vehicle sensor comprises a vehicle surrounding sensing unit which is responsible for sensing the environment around the vehicle, mapping the environment around the vehicle, determining the position of the vehicle at any time and providing decision-making capability of safe driving for the vehicle in various driving scenes through sensing data; the vehicle-mounted unit transmits various collected data to the big data service platform for calling through a special communication channel and a communication mode.
When a vehicle passes through a closed road section, the environmental information and other data information of the road section around which the vehicle bypasses are dynamically acquired in real time, and the vehicle-mounted unit transmits various acquired data to the big data service platform through a special communication channel and a communication mode. The information collected by the road side sensing unit 1 and the vehicle-mounted unit 2 passes through respective special communication channels and communication modes, such as: the wireless transmission device or the network line sends the acquired data to the big data service platform, and the big data service platform performs fusion analysis on the received data.
The big data service platform receives the data sent by the road side sensing unit 1 and the vehicle-mounted unit 2 and then carries out real-time processing, reverse space-time track reconstruction is carried out through the processed data, vehicle abnormal tracks and driving paths 6 are drawn by using vehicle point tracks collected every second, the number of regional section lanes and the boundaries of roads are reversely drawn through the driving tracks or the driving paths of the vehicles, graphic data of the whole road are formed by superposing continuous road sections, and marking line information and safety auxiliary infrastructure information are fused together to form road graphic data information, lane data information, actual vehicle driving track information, path information and vehicle driving direction information for brand new reference. The big data service platform compares the generated road graphic data information for brand-new reference, the lane data information, the actual vehicle driving track information, the path information, the identification marking line information, the safety auxiliary infrastructure information and the vehicle driving direction information with the road graphic information, the lane data information, the planning vehicle driving track information, the path information, the road infrastructure information, the safety protection information, the identification marking line information and the vehicle driving direction information contained in the original area or the high-precision map of the road stored in the system to find different places, superposes the different places with the original high-precision map to generate a brand-new base high-precision map, and plans a vehicle normal track and a driving path 7. And according to the construction principle of the highway, the national province road and the urban road infrastructure on different roads, driveways and auxiliary facilities, respectively correcting and perfecting all related content data information in the high-precision map with the brand-new base, comprising the following steps: the road comprises the following components of road traffic lane width, the number of lanes, correct driving paths, road basic equipment, safety protection facilities, road boundaries, mark lines and driving directions.
The big data service platform generates a complete brand-new high-precision map by fusing and identifying the data information collected by the road side sensing unit 1 and the vehicle-mounted unit 2 and marking the position of an abnormal event 4 on the road, an abnormal area influencing traffic and abnormal lane information on the brand-new high-precision map, and the system carries out secondary fusion on the data, and automatically generating an early warning region 5 extending to the vehicle driving direction by taking the position of the abnormal event as the center according to the event type, the affected range, region and lane and combining the principle of safe driving of the vehicle, and generating early warning prompt information of the content of the abnormal event 4 occurring on the road, issuing the early warning prompt information to all vehicles and other clients about to pass through the road section, and sending out warning information in advance to warn all passing vehicles to slowly and safely run according to the optimal running path and the passing scheme automatically generated by the system. And prompting the driver of the automatic driving vehicle to convert the automatic driving mode of the vehicle into a manual driving mode so as to improve the safety of the vehicle and the smoothness of the road.
Aiming at the driving process of unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles and full-manual driving vehicles, the system can provide more reliable, accurate and timely high-precision maps and guarantee the driving safety. The high-precision map formed by a multi-system, multi-aspect and multi-integration mode has higher reliability than high-precision map data generated by a single measuring device or a single data source. When the road condition state and the traffic state change, abnormal traffic accidents occur on the road, and the abnormal events 4 endanger the driving safety, the corresponding high-precision map, the early warning information and the prompt information can be rapidly generated according to the space-time trajectory reconstruction mode, so that the serious traffic accidents caused by unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles and fully manually driven vehicles and the occurrence of secondary accidents are avoided. The roadside sensing unit 1 and the vehicle-mounted unit 2 are used for collecting real-time information of roads, so that the cost of updating a high-precision map can be reduced, and the use of a special high-precision map measuring vehicle for measurement is effectively reduced or avoided. The traffic accident and the secondary accident caused by the change of the driving path caused by the fact that the road side single sensing equipment cannot detect the obstacle, the throwing object, the landslide, the roadblock and the temporary traffic control are far away, small and harmful can be effectively avoided.
In the normal running process of the vehicle, the lane with the abnormal accident is avoided by the aid of a brand-new high-precision map, a vehicle safe running model is further established, the vehicle is guided to run safely, and the vehicle passing efficiency is improved.
The data obtained through space-time trajectory reconstruction is further standardized and designed to form a danger early warning model, vehicles which are illegal, abnormal in driving and abnormal in behavior can be warned and prompted, abnormal behavior warning information is immediately output once a relevant judgment mechanism is triggered, and point-to-point information prompting and evidence obtaining are carried out on the corresponding vehicles. The safe and effective management of vehicle running is realized by monitoring the whole process, controlling the behaviors of changing lanes of the vehicle against regulations, driving at an overspeed, occupying an emergency parking lane for a long time, occupying a fast lane by a truck for a long time, escaping fee and the like.
Example 2
Referring to fig. 3, the embodiment discloses a method for quickly generating a high-precision map of an abnormal road based on spatio-temporal trajectory reconstruction, where the method includes: the system comprises an original high-precision map 8 of a system road area and an area road map 9 for reference formed by preliminarily fusing various data acquired by a road side sensing unit 1 and an on-board unit 2, wherein the difference data is compared with each other, the difference part and the original high-precision map 8 of the road area are superposed to generate a brand-new road base high-precision map 10, the system analyzes and processes various data to acquire the motion tracks and the running directions of all vehicles, and converts the motion tracks and the running directions of all vehicles into lane vehicle running paths and lane running directions 14 allowing normal vehicles to run on the road, so as to form a high-precision map 11 with correct running paths, the system marks abnormal events acquired by the road side sensing unit 1 and the on-board unit 2 on the brand-new road base high-precision map 10, and automatically generates abnormal events 4 which extend to the vehicle running direction by taking the position of the abnormal event as the center and occur on the road according to the type of the abnormal events 4, the affected range The system carries out further data fusion on road marking information, safety guarantee infrastructure information, road infrastructure information, data information of a brand-new road base high-precision map 10, data information of a high-precision map 11 with a correct driving path, an early warning area and early warning prompting information 15 collected by a vehicle-mounted unit 2 to form a high-precision map 12 with early warning information, and finally fuses the brand-new road base high-precision map, the high-precision map with the correct driving path and the high-precision map with the early warning information to form a brand-new road high-precision map 13.
The system issues the data of the high-precision map 13 of the brand-new road to all vehicles and other clients about to pass through the road section, and sends out warning information in advance to warn all passing vehicles to slowly and safely travel according to the optimal travel path and the passing scheme automatically generated by the system. And prompting the driver of the automatic driving vehicle to convert the automatic driving mode of the vehicle into a manual driving mode so as to improve the safety of the vehicle and the smoothness of the road.
The data obtained by the method can enable the roadside sensing equipment and the edge computing equipment to be combined with each other to form a safety guarantee system with larger functions, provide enough decision basis and even instructions for unmanned vehicles, automatic driving vehicles, manual auxiliary driving vehicles and fully manually driven vehicles, and improve the driving safety of the unmanned vehicles and the automatic driving vehicles essentially. The complexity of development of the unmanned vehicle and the automatic driving vehicle can be greatly reduced, and the cost can be greatly reduced. Unmanned, autonomous commercialization can also come in advance because it does not need to traverse all scenes.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (9)
1. Based on space-time trajectory reconstruction abnormal road high-precision map rapid generation system, which is characterized by comprising the following steps: the system comprises a road side sensing unit, a vehicle-mounted unit and a big data service platform, wherein the road side sensing unit is arranged at the side edge of a road, the road side sensing unit acquires dynamic information, road condition information, characteristic information, traffic state information, climate information and various abnormal event information of all types of vehicles running on the road, the vehicle-mounted unit is arranged in an unmanned vehicle, an automatic driving vehicle, a manual auxiliary driving vehicle and a road surveying and mapping vehicle, the position information of the vehicle, the road condition information around the vehicle, the environment information, identification marking line information, event information damaging the safe running of the vehicle, road safety running auxiliary infrastructure and various operation data information of the safe running of the vehicle can be dynamically acquired in real time through various vehicle-mounted sensors and a positioning module, and the data acquired by the road side sensing unit and the vehicle-mounted unit are transmitted to the big data service platform, the big data service platform carries out reverse space-time trajectory reconstruction according to the acquired data, carries out differential comparison on the reconstructed data and the original high-precision map data of the area, superposes the difference part with the original high-precision map to generate a brand-new road high-precision map, superposes and fuses the abnormal road condition information, the abnormal traffic incident accident information and the abnormal traffic state information acquired by the road side sensing unit and the vehicle-mounted sensor into the brand-new road high-precision map, the position, the influence range and the development situation of the new road high-precision map are marked, the early warning and warning prompt information content is generated, and the passing scheme of normal and safe running of the vehicle is allowed, so that the special high-precision map is provided for the road required by the safe running of the unmanned vehicle, the automatic vehicle and the manual auxiliary driving vehicle, and is used for being called by different user terminals.
2. The system for quickly generating the high-precision map of the abnormal road based on the spatiotemporal trajectory reconstruction as claimed in claim 1, wherein the roadside sensing unit collects all types of vehicle dynamic information, road abnormal event information, vehicle characteristic information, traffic state information, climate information and vehicle abnormal event information which are driven on the road, and the vehicle dynamic information comprises: the real-time motion speed information, the motion direction information, the longitude and latitude information, the acceleration information, the motion direction angle information, the vehicle size information, the vehicle type information, the lane information, the motion track information and the unique ID identification number information of the vehicle in the whole system of each vehicle; the vehicle abnormal event information includes: the vehicle has abnormal conditions and abnormal behaviors; the road abnormal event information includes: whether dangerous pedestrians appear or not, whether dangerous animals appear or not, whether road landslide exists or not, whether sprinkled objects appear or not, whether falling rocks appear or other dangerous conditions influencing normal road traffic exist or not; the vehicle characteristic information includes: vehicle license plate information, vehicle logo information, vehicle series information, vehicle type information and vehicle color information; the traffic state information includes: the road side sensing unit transmits various collected data to the big data service platform for calling through a special communication channel and a communication mode.
3. The system for quickly generating the high-precision map of the abnormal road based on the spatio-temporal trajectory reconstruction as claimed in claim 2, wherein the on-board unit comprises: the system comprises a vehicle real-time positioning module and vehicle sensors, wherein the vehicle real-time positioning module comprises a Beidou system, a Galileo system and a GPS (global positioning system), and provides periodic real-time position longitude and latitude information, speed information and a clock synchronization time service function for a vehicle through various sensors and the real-time positioning module in the vehicle running process; the vehicle sensor comprises a vehicle surrounding sensing unit which is responsible for sensing the environment around the vehicle, mapping the environment around the vehicle, determining the position of the vehicle at any time and providing decision-making capability of safe driving for the vehicle in various driving scenes through sensing data; the vehicle-mounted unit transmits various collected data to the big data service platform for calling through a special communication channel and a communication mode.
4. The system for quickly generating the high-precision map of the abnormal road based on the spatiotemporal trajectory reconstruction as claimed in claim 1, wherein the roadside sensing unit and the vehicle-mounted unit transmit the collected various information to a big data service platform through a wireless transmission device or a wired network line, and the big data service platform performs the integration analysis on the received data.
5. The system for rapidly generating the abnormal road high-precision map based on the spatio-temporal trajectory reconstruction as claimed in claim 1, it is characterized in that the big data service platform carries out real-time analysis and processing after receiving the data sent by the road side sensing unit and the vehicle-mounted unit, the processed data is used for reconstructing a reverse space-time track, the motion track and the driving path of the vehicle are drawn by using the vehicle point trace collected every second, the number of regional cross section lanes and the boundary of the road are drawn through the driving track or driving path of the vehicle, the method comprises the steps of forming graphic data of the whole road by overlapping sections of continuous roads, and fusing identification marking line information and safety auxiliary infrastructure information together to form road graphic data information, lane data information, actual vehicle driving track information, path information and vehicle driving direction information for brand new reference.
6. The system as claimed in claim 5, wherein the big data service platform compares the generated road graphic data information for new reference, lane data information, actual vehicle driving track information, path information, identification marking line information, safety assistance infrastructure information and vehicle driving direction information with road graphic information, lane data information, planned vehicle driving track information, path information, road infrastructure information, safety protection information, identification marking line information and vehicle driving direction information contained in the original region or high-precision map of the road stored in the system to find different places, superimposes the different places on the original high-precision map to generate a new high-precision map, and constructs different roads, lanes and assistance facilities according to the highway, provincial road and urban road infrastructure, the method respectively corrects and perfects all related content data information in the brand-new high-precision map, and comprises the following steps: the road comprises the following components of road traffic lane width, the number of lanes, correct driving paths, road basic equipment, safety protection facilities, road boundaries, mark lines and driving directions.
7. The system for rapidly generating the high-precision map of the abnormal road based on the spatiotemporal trajectory reconstruction as claimed in claim 5, wherein the big data service platform is used for fusing and identifying data information collected by the road side sensing unit and the vehicle-mounted unit, marking the positions, areas and lanes of various abnormal events which endanger the safe driving of vehicles on a brand-new high-precision road map, and performing secondary fusion on the data to form a complete high-precision map and storing the complete high-precision map into the navigation system.
8. The system for rapidly generating the high-precision map of the abnormal road based on the spatio-temporal trajectory reconstruction as claimed in claim 5, wherein the big data service platform labels the abnormal event information collected by the road side sensing unit and the vehicle-mounted unit on the completely new high-precision map which has been generated, and automatically generates an early warning region extending to the driving direction of the vehicle with the position of the abnormal event as the center according to the event type, the affected region, the affected lane and the safe driving principle of the vehicle, and generates the early warning prompt information of the content of the abnormal event which occurs on the road and the system converts the motion trajectory and the driving direction of all the driving vehicles in the abnormal region into the information of the driving path, the driving direction and the available lane of the abnormal region which allows the vehicle to normally drive, and issues to all the vehicles which are going through the road section, warning information is sent out in advance to warn all passing vehicles to slowly and safely run according to the optimal running path and the passing scheme which are automatically generated by the system, and drivers of automatic driving vehicles are prompted to convert the automatic driving mode of the vehicles into a manual driving mode so as to improve the safety of the vehicles and the smoothness of roads.
9. A method for quickly generating a high-precision map of an abnormal road based on space-time trajectory reconstruction is characterized by comprising the following steps: the method for acquiring all types of vehicles running on the road by using the road side sensing unit comprises the following steps: unmanned vehicles, autonomous vehicles, manually assisted driving vehicles, fully manually driven vehicles; the information collected includes: vehicle dynamic information, road abnormal event information, vehicle characteristic information, traffic state information, climate information and vehicle abnormal event information; the vehicle dynamics information includes: the real-time motion speed information, the motion direction information, the longitude and latitude information, the acceleration information, the motion direction angle information, the vehicle size information, the vehicle type information, the lane information, the motion track information and the unique ID identification number information of the vehicle in the whole system of each vehicle; the vehicle abnormal event information includes: the vehicle has abnormal conditions and abnormal behaviors; the road abnormal event information includes: whether dangerous pedestrians appear or not, whether dangerous animals appear or not, whether road landslide exists or not, whether sprinkled objects appear or not, whether falling rocks appear or other dangerous conditions influencing normal road traffic exist or not; the vehicle characteristic information includes: vehicle license plate information, vehicle logo information, vehicle series information, vehicle type information and vehicle color information; the traffic state information includes: the road side sensing unit transmits various collected data to a big data service platform for calling through a special communication channel and a communication mode;
the on-board unit includes: real-time orientation module of vehicle and vehicle sensor, the real-time orientation module of vehicle includes: the system comprises a Beidou system, a Galileo system and a GPS (global positioning system) positioning system, and is used for providing periodic real-time position longitude and latitude information, speed information and clock synchronization time service functions for a vehicle; the vehicle sensor consists of vehicle sensing unit cells around the vehicle, is used for sensing the environment around the vehicle and mapping the environment around the vehicle, so that the position of the vehicle can be determined at any time, and the vehicle is provided with decision-making capability for safe driving under various driving scenes through sensing data; the vehicle-mounted unit dynamically acquires position information of a vehicle, surrounding road condition information, environment information, identification marking information, event information damaging safe driving of the vehicle, road safe driving auxiliary infrastructure and various operation data information of safe driving of the vehicle in real time through a vehicle-mounted sensor, and transmits various acquired data to a big data service platform for calling through a special communication channel and a communication mode;
the data collected by the road side sensing unit and the vehicle-mounted unit are transmitted to a big data service platform, the big data service platform conducts reverse space-time trajectory reconstruction according to the collected data, the reconstructed data and the original high-precision map data of the area are subjected to differential comparison, the difference part and the original high-precision map are superposed to generate a brand new road high-precision map, and abnormal event information collected by the road side sensing unit and the vehicle-mounted unit is fused into the brand new road high-precision map;
the system marks the position of an abnormal event in a brand-new road map, regenerates a complete brand-new high-precision map, automatically generates an early warning area which extends forwards to the driving direction of the vehicle by taking the position of the abnormal event as the center according to the type of the event, the range, the area and the lane influenced by the event and combining the safe driving principle of the vehicle, generates early warning prompt information of the content of the abnormal event on the road, converts the motion track and the running direction of all driving vehicles in the range of the abnormal area into the driving path, the driving direction and the information of the available lane of the abnormal area, sends the information to all vehicles and other clients which are going to pass through the road section, sends warning information in advance to warn all passing vehicles to slowly and safely drive according to the optimal driving path and the passing scheme automatically generated by the system, and prompting the driver of the automatic driving vehicle to convert the automatic driving mode of the vehicle into a manual driving mode so as to improve the safety of the vehicle and the smoothness of the road.
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| CN116878487B (en) * | 2023-09-07 | 2024-01-19 | 河北全道科技有限公司 | Method and device for establishing automatic driving map, vehicle and server |
| CN117705090A (en) * | 2023-12-20 | 2024-03-15 | 南京智绘星图信息科技有限公司 | Comprehensive control method of high-precision map |
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