CN111383456A - Localized artificial intelligence system for intelligent road infrastructure system - Google Patents
Localized artificial intelligence system for intelligent road infrastructure system Download PDFInfo
- Publication number
- CN111383456A CN111383456A CN202010299071.1A CN202010299071A CN111383456A CN 111383456 A CN111383456 A CN 111383456A CN 202010299071 A CN202010299071 A CN 202010299071A CN 111383456 A CN111383456 A CN 111383456A
- Authority
- CN
- China
- Prior art keywords
- artificial intelligence
- road
- vehicle
- intelligence system
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 72
- 241001465754 Metazoa Species 0.000 claims abstract description 13
- 230000008447 perception Effects 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 30
- 230000007613 environmental effect Effects 0.000 claims description 17
- 230000033001 locomotion Effects 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 12
- 230000000007 visual effect Effects 0.000 claims description 8
- 230000006399 behavior Effects 0.000 claims description 7
- 230000006378 damage Effects 0.000 claims description 7
- 208000027418 Wounds and injury Diseases 0.000 claims description 6
- 208000014674 injury Diseases 0.000 claims description 6
- 230000004807 localization Effects 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 5
- 239000003550 marker Substances 0.000 claims description 5
- 206010039203 Road traffic accident Diseases 0.000 claims description 4
- 230000003449 preventive effect Effects 0.000 claims description 4
- 238000007726 management method Methods 0.000 claims description 3
- 238000012358 sourcing Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims 1
- 238000005457 optimization Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 7
- 206010065042 Immune reconstitution inflammatory syndrome Diseases 0.000 description 4
- 230000003068 static effect Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000001413 cellular effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000005670 electromagnetic radiation Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000001953 sensory effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000001429 visible spectrum Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Aviation & Aerospace Engineering (AREA)
- Theoretical Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Atmospheric Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a localized artificial intelligence system for an intelligent road infrastructure system, which comprises a database, a sensing unit and a calculating unit, wherein the sensing unit is used for sensing the local artificial intelligence system; the sensing unit is connected with the computing unit, and the database is connected with the computing unit; the database is used for storing accumulated historical data; the sensing unit is used for providing real-time data and comprises a sensor, and the sensor is used for acquiring the real-time data; the computing unit constructs and trains models for identifying and/or detecting vehicles, animals and other objects on the road and/or road side by comparing the real-time data with the accumulated historical data stored in the database, and provides perception, behavior prediction and management, decision making and vehicle control for the intelligent road infrastructure system. The invention can effectively improve the safety and the accuracy of the automatic driving vehicle system.
Description
Technical Field
The invention relates to the technical field of intelligent road infrastructure, in particular to a localized artificial intelligence system for an intelligent road infrastructure system.
Background
A Self-driving vehicle (Self-driving car), also called as an unmanned vehicle, a computer-driven vehicle, or a wheeled mobile robot, is an intelligent vehicle that realizes unmanned driving through a computer system. Autonomous vehicles that can sense the environment, detect objects, and navigate without human involvement are under development. However, this presents challenges for managing multiple vehicles and different modes of transportation. Existing autonomous vehicle technology requires expensive, complex and low-performance on-board systems, uses multiple sensing systems, and relies primarily on vehicle sensors to achieve vehicle control. Thus, implementation of autonomous vehicle systems is a significant challenge.
Disclosure of Invention
The present invention is directed to a localized artificial intelligence system for an intelligent roadway infrastructure system to solve the above-mentioned problems of the prior art and to improve the safety and accuracy of an autonomous vehicle system.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a localized artificial intelligence system for an intelligent road infrastructure system, wherein the intelligent road infrastructure system comprises a road side unit and an automatic driving vehicle, and the localized artificial intelligence system comprises a database, a sensing unit and a calculating unit; the sensing unit is connected with the computing unit, and the database is connected with the computing unit;
the database is used for storing accumulated historical data, wherein the accumulated historical data comprises compiled sensing data, background of local areas, vehicles, traffic, objects and/or environment data;
the sensing unit is used for providing real-time data, the sensing unit comprises a sensor, the sensor is used for acquiring the real-time data, and the real-time data comprises background, vehicle, traffic, object and/or environment data of a local area;
the computing unit constructs and trains models for identifying and/or detecting vehicles, animals and other objects on the road and/or road side by comparing the real-time data with the accumulated historical data stored in the database, and provides perception, behavior prediction and management, decision making and vehicle control for the intelligent road infrastructure system.
Preferably, the localized artificial intelligence system is embedded in one or a group of roadside units; the localization artificial intelligence system also comprises a communication interface, and the communication interface is used for the localization artificial intelligence system to communicate with each component of the intelligent road infrastructure system, the intelligent city and/or a third-party system; the third party can also retrieve and/or transmit data in the artificial intelligence system database.
Preferably, the localized artificial intelligence system further comprises a reference point for determining a location of the autonomous vehicle; the reference points comprise a vehicle reference point arranged on the automatic driving vehicle, a roadside reference point arranged on the roadside, and/or a road reference point arranged on the road;
the vehicle reference point comprises a vehicle-mounted tag, a Radio Frequency Identification Device (RFID) and/or a visual marker;
the roadside reference point and the road reference point are fixed structures and are used for broadcasting the positions of the fixed structures to the automatic driving vehicle to assist the automatic driving vehicle in determining the positions; the height of the fixing structure is higher than that of the snow line, and the fixing structure is reflective.
Preferably, the localized artificial intelligence system is further configured to improve the positioning accuracy of the autonomous vehicle; the specific method for positioning the automatic driving vehicle comprises active positioning and/or passive positioning.
Preferably, the localized artificial intelligence system is further capable of identifying a risk location and transmitting the risk location to the autonomous vehicle and/or the roadside unit.
Preferably, the localized artificial intelligence system can improve models and/or algorithms for identifying and predicting vehicle and target motion, can also predict road and/or environmental conditions, can also predict pedestrian motion, traffic accidents, weather, natural disasters and/or communication failures, and can also predict roadside objects and/or on-road object behaviors by comparing real-time data with accumulated historical data and by machine learning.
Preferably, the sensing unit further comprises radar-based sensors and/or vision-based sensors, a navigation system, vehicle identification means; the navigation system comprises a satellite based navigation system and/or an inertial navigation system.
Preferably, the localized artificial intelligence system further comprises a cloud platform unit capable of sharing real-time environment and/or road data, historical environment and/or road data.
Preferably, the localized artificial intelligence system further comprises a safety system for reducing the frequency and severity of collisions; the security system can also provide an active security method, a passive security method; the active security method is used to provide precautionary measures before an event occurs; the activity safety method is used for providing preventive measures before an accident causes injury; the passive safety method is used to eliminate and/or reduce injuries and losses caused by an accident after the accident has occurred.
Preferably, the localized artificial intelligence system further provides intelligent collaboration between the roadside unit and the autonomous vehicle, the intelligent collaboration including use of a crowd-sourcing model; the localized artificial intelligence system can also carry out self-organization control and decentralized system control; the localized artificial intelligence system can also perform work division and distribution tasks on the components of the intelligent road infrastructure system.
The invention discloses the following technical effects:
(1) the machine learning model is trained and updated through real-time data and historical data in the automatic driving process, so that the accuracy of the model can be effectively improved, an accurate decision is provided for an intelligent road infrastructure system, and the accurate control of an automatic driving vehicle is realized;
(2) according to the invention, by positioning the automatic driving vehicle, the objects on the road and the roadside, the pedestrians and the like, and detecting and analyzing the background, the behaviors of the vehicle and the objects, the traffic and the environmental data, the dangerous positions can be rapidly and accurately obtained and sent to the roadside unit and the automatic driving vehicle, so that the safety of the automatic driving vehicle system is improved;
(3) the invention can avoid or reduce the damage and loss caused by accidents in time through the safety method provided by the safety system;
(4) according to the invention, through the cloud platform unit and the communication interface, data sharing among all components of the intelligent road infrastructure system, the smart city and a third party can be realized, and the performance and efficiency of the automatic driving vehicle system are effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of a localized artificial intelligence system of the present invention;
FIG. 2 is a schematic flow chart of automatic driving vehicle positioning data according to an embodiment of the present invention; wherein 101 denotes a roadside unit, 102 denotes an autonomous vehicle;
FIG. 3 is a flow chart of a method for passively locating an autonomous vehicle in accordance with an embodiment of the present invention;
FIG. 4 is a flowchart of an active positioning method for an autonomous vehicle according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating machine learning according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of roadside objects and/or object detection on a road according to an embodiment of the invention;
where 3101 denotes a motorway, 3102 denotes a non-motorway, 3103 denotes a roadside tunnel, 3104 denotes a car, 3105 denotes a detection range of a roadside unit, 3016 denotes a communication system, 3107 denotes an on-board unit, and 3108 denotes a truck.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1-6, the present embodiment provides a localized artificial intelligence system for an intelligent roadway infrastructure system for automatic vehicle control and traffic operation; the intelligent road infrastructure system comprises a road side unit and an automatic driving vehicle, and the localized artificial intelligent system comprises a database, a sensing unit and a calculating unit; the sensing unit is connected with the computing unit; the database is connected with the computing unit;
the database is used to store accumulated historical data including compiled sensory data, the context of the local area (e.g., road conditions), vehicles (e.g., vehicle position and motion (e.g., speed, acceleration)), traffic, objects (e.g., object position, pedestrian position and motion), and/or environmental data (e.g., weather). The accumulated historical data also includes data collected by one or more roadside units (e.g., roadside unit sensors (e.g., image data), radar, lidar) and/or satellite navigation information. The data collected and stored by the roadside unit in the historical database includes: vehicles, animals and other objects on the road (e.g., motorways and/or non-motorways), vehicles, animals and other objects on the roadside, and/or road conditions, traffic conditions, weather, and/or information describing the environment; the data collected by the rsu and stored in the historical database also includes vehicles, animals, and other objects within the rsu coverage area; the data collected by the road side unit and stored in the historical database also includes data collected by vehicles (e.g., on board units) and transmitted to the road side unit (e.g., the nearest road side unit), the data collected by the vehicles (e.g., real-time data) including sensory data, weather data, and other data, such as data describing road conditions, traffic conditions, weather, object locations, pedestrian locations and movements, vehicle locations and movements (e.g., speed and/or acceleration). The roadside unit performs heterogeneous data fusion on the collected data to compare the real-time data with historical data provided by a historical database, thereby improving the accuracy of detection of vehicles, animals and other objects on the road (e.g., on motor vehicle lanes and/or non-motor vehicle lanes) and/or roadside.
The local area comprises a coverage area where the road side unit can provide service;
the sensing unit is used for providing real-time data and comprises a sensor, and the sensor comprises a sensor arranged on the road side unit and a sensor arranged on an on-board unit of the automatic driving vehicle. The sensors are used for the acquisition of the real-time data, which includes local area background, vehicle, traffic, object and/or environmental data.
The computing unit constructs and trains models for identifying and/or detecting vehicles, animals and other objects on the road and/or roadside by comparing the real-time data with the accumulated historical data stored in the database, providing awareness, behavioral prediction and management, decision-making and vehicle control for the intelligent road infrastructure system, as shown in fig. 5. Updating and/or training models for identifying and/or detecting vehicles, animals, and other objects on the road and/or roadside by collecting data describing the road, roadside, and/or environment, e.g., describing road conditions, traffic conditions, weather, target locations, pedestrian locations and motions, vehicle locations and motions (e.g., velocity and/or acceleration); the data describing the road, roadside and/or environment is collected by sensors on the roadside unit and/or on-board unit; the computing unit also includes computer perception, e.g., using sensors (e.g., cameras that detect and/or record electromagnetic radiation in the visible spectrum and/or invisible spectrum), microphones, wireless signals, radar, and/or lidar provided data to detect objects and/or describe the environment, and employing computer vision to analyze sensor data (e.g., image data).
The localized artificial intelligence system can be part of an intelligent road infrastructure system IRIS, e.g., embedded in one or a group of roadside units, for providing sensing and communication for the IRIS of autonomous vehicle operation, control of vehicle access in conjunction with an autonomous CAVH system; the localized artificial intelligence system also comprises interfaces for communicating with other IRIS components, smart cities and/or other intelligent facilities, and can provide localized applications fused with machine learning models;
the localized yet intelligent system is used to improve local knowledge (e.g., databases) and/or local intelligence (e.g., improve accuracy and precision of joint positioning of targets (e.g., obstacles, animals, pedestrians, static objects), vehicles (e.g., cars, trucks, bicycles, buses, etc.); the localized yet intelligent system is also used to detect objects and/or vehicles on the road; the localized yet intelligent system is also used to detect roadside objects and/or vehicles; detecting and/or predicting the behavior of vehicles (e.g., motorized and non-motorized), animals, pedestrians, and other objects, collecting traffic information and/or predicting traffic, and/or providing active and/or passive safety measures.
Further optimizing the scheme, the localized artificial intelligence system is also used for improving the positioning precision of the automatic driving vehicle; the specific method for locating the automatic driving vehicle comprises the following steps: active positioning and passive positioning.
Referring to fig. 2, the present embodiment provides data flow for interaction between the roadside unit and the autonomous vehicle during positioning of the autonomous vehicle by passive and/or active autonomous.
Referring to fig. 3, in the present embodiment, a passive positioning method for an autonomous vehicle is provided, in which an autonomous vehicle 102 detects whether it is in the coverage area of a roadside unit 101 through an on-line sensor and/or an on-board sensor OBU that can communicate with the roadside unit 101; the roadside unit 101 includes a storage component for storing accurate location information describing the roadside unit 101 and/or adjacent roads; the roadside unit 101 broadcasts the location information (e.g., without any specific request for the location information), or the roadside unit 101 responds to a request for location information by a service object (e.g., from a vehicle and/or OBU); the autonomous vehicle 102 receives the location information (e.g., via an OBU); autonomous vehicle 102 also self-detects its location information, the self-detection location information method comprising: its position is determined by collecting position information through its own sensors and/or receiving satellite navigation data by autonomous vehicle 102 (e.g., by the OBU). Autonomous vehicle 102 also determines its position using data provided by its own sensors and/or satellite navigation data received by autonomous vehicle 102 (e.g., by an OBU). The autonomous vehicle 102 obtains its own position information by analyzing and calculating the received roadside unit position information and the self-detected position information. Thus, in passive autonomous vehicle positioning, autonomous vehicle 102 determines its own position by receiving, processing, and analyzing position information, sensor information, satellite navigation information, and the like.
Referring to fig. 4, the present embodiment provides a vehicle active positioning method, first, a roadside unit 101 detects an autonomous vehicle 102 within its coverage area, including: roadside unit 101 detects autonomous vehicle 102 within a coverage area via sensors (e.g., image sensors, RADAR, LIDAR, etc.); or roadside unit 101 detects whether autonomous vehicle 102 is within the coverage area of roadside unit 101 by communicating with autonomous vehicle 102 (e.g., by transmitting and/or receiving data between roadside unit 101 and the OBU of autonomous vehicle 102); or the autonomous vehicle 102 is equipped with components, such as tags (e.g., RFID tags), labels, designs, etc., that the roadside unit 101 and/or the CAVH system can identify the autonomous vehicle 102 information. Next, the roadside unit 101 receives data from the autonomous vehicle 102, including: receiving sensor data from autonomous vehicle 102, satellite navigation data from autonomous vehicle 102, and/or other data from autonomous vehicle 102, wherein roadside unit 101 includes a memory component that stores information describing the exact location of roadside unit 101 and/or adjacent roads. Once again, the roadside unit 101 processes and/or analyzes the data received from the autonomous vehicle 102 and/or the data describing the roadside unit's precise location information from the roadside unit storage component, calculates the location of the autonomous vehicle 102 and sends the location to the autonomous vehicle 102. Thus, in active vehicle positioning, position information, sensor information, satellite navigation information, and the like are received, processed, and analyzed by the roadside unit 101, the roadside unit 101 determines the location of the autonomous vehicle 102, and transmits the position information to the autonomous vehicle 102.
Further optimizing the solution, the localized artificial intelligence system further comprises a reference point for determining a location of the autonomous vehicle; the reference points comprise a vehicle reference point arranged on the automatic driving vehicle, a roadside reference point arranged on the roadside, and/or a road reference point arranged on the road;
the vehicle reference point comprises an onboard tag, a Radio Frequency Identification Device (RFID), and/or a visual marker; the visual marker is mounted on the roof of the vehicle; each of said visual markers including a pattern for identifying the vehicle on which said visual marker is mounted; the visual indicia comprises light;
the roadside reference point and the road reference point are fixed structures and are used for broadcasting the positions of the fixed structures to the automatic driving vehicle to assist the automatic driving vehicle in determining the positions; the height of the fixing structure is higher than that of the snow line, and the fixing structure has reflectivity; the roadside reference point having an accurate known position, the roadside reference point and its position being broadcast to visual markers of an autonomous vehicle; the road reference point comprises a magnetic mark arranged underground and/or a mark arranged on a road surface;
the roadside reference points include lights and/or other beacons, and the roadside reference points also include roadside units and/or RFIDs. The roadside reference point may reflect electromagnetic radiation (e.g., radio waves, light, invisible light, microwaves, etc.); the roadside reference point includes a storage component that stores precise and accurate reference point location information. The position of the center point of the roadside reference point relative to the local road segment is measured in advance and stored in the roadside unit and/or the RFID on the roadside reference point; the height of the center of the roadside reference point from the sidewalk, and the distance from the pole bottom of the roadside reference point to the center line of a lane in the road are measured in advance and stored in the RFID on the roadside unit and/or the roadside reference point; the roadside reference point is used for broadcasting the position information thereof; the roadside reference point contains its height above the snow line, such as a reflective member (e.g., a reflective plate) and/or a light (e.g., an LED light) that is visible under high snow conditions; signals emitted by the vehicle are reflected from the roadside reference point (e.g., a reflective pole) and the reflected signals are received by the vehicle, which uses the reflected signals to determine the roadside reference point and/or the position of the vehicle.
Further optimizing the scheme, the localized artificial intelligence system also comprises a map service unit, and the map service unit is used for providing map service; the map service unit provides a high-resolution map in the coverage area of the road side unit; the high resolution map is capable of providing real-time locations of vehicles, objects, pedestrians; the high resolution map is updated with real time data provided by the road side unit within its coverage area.
Further, according to the optimized scheme, the localized artificial intelligence system can also identify high-risk positions; the roadside unit is used for identifying a high risk location; the high risk location comprises an animal, a pedestrian, an accident, an unsafe sidewalk, and/or adverse weather; the roadside unit sends high risk location information to the autonomous vehicle and/or other roadside units.
According to the further optimization scheme, the localized artificial intelligence system can also sense the environment and/or the road in real time through the sensor so as to acquire real-time environment and/or road data; comparing, by the computing unit, the real-time environmental and/or road data collected by the sensors with historical environmental and/or road data stored in the database, and performing machine learning by the real-time environmental and/or road data, the historical environmental and/or road data to improve models and/or algorithms for identifying and predicting vehicle and object motion.
In a further optimization scheme, the localized artificial intelligence system can also predict road and/or environmental conditions through historical data stored in the database, real-time background, vehicle, traffic, object and/or environmental data collected by the sensors; the road condition comprises road resistance coefficient, road surface condition, road slope angle, and movement of objects and/or obstacles on the road; the localized artificial intelligence system can also predict pedestrian movement, traffic accidents, weather, natural disasters, and/or communication failures.
Further optimizing the scheme, the sensing unit further comprises a radar-based sensor and/or a vision-based sensor; the vision-based sensor, radar-based sensor for sensing driving environment and vehicle attribute data; the radar-based sensor comprises a laser radar, a microwave radar, an ultrasonic radar or a millimeter wave radar; the vision-based sensor comprises a camera, an infrared camera, or a thermal camera; the camera is a color camera.
In a further optimization scheme, the sensing unit further comprises a satellite-based navigation system and/or an inertial navigation system; the satellite-based navigation system comprises a Differential Global Positioning System (DGPS), a Beidou navigation satellite system (BDS) system, or a GLONASS global navigation satellite system; the inertial navigation system is used for providing vehicle position data; the inertial navigation system includes an inertial reference unit.
In a further optimization scheme, the sensing unit further comprises a vehicle identification device; the vehicle identification device includes RFID, Bluetooth, Wi-fi (IEEE 802.11), or cellular network radio, such as a 4G or 5G cellular network radio.
Further optimizing the scheme, the localized artificial intelligence system further comprises a cloud platform unit, and the cloud platform unit can share real-time environment and/or road data, historical environment and/or road data; the localized artificial intelligence system also pre-processes real-time environment and/or road data, the pre-processing being through computer vision.
In a further optimization scheme, the localized artificial intelligence system can also detect roadside objects and/or objects on the road; the on-road object comprises a vehicle and/or a road hazard; the vehicle comprises a car, bus, truck and/or bicycle; the road hazards include rocks, debris, and/or potholes; the roadside object comprises a static and/or dynamic roadside object comprising a pedestrian, an animal, a bicycle, and/or an obstacle; the specific method for detecting the roadside object by the localized artificial intelligence system comprises the following steps: collecting roadside and environmental data in real time; the computing unit compares the real-time roadside and environmental data collected by the sensor with historical roadside and environmental data stored in a database to complete the identification of roadside objects; the roadside and environmental data is provided by a roadside unit.
The embodiment provides a roadside object and/or an object detection method on a road, the road comprises a motorway 3101 and a non-motorway 3102, the roadside comprises a roadside tunnel 3103 and a roadside unit 101, wherein the detection range of the roadside unit is 3105; the autonomous vehicle includes a truck 3108, a car 3104; the truck 3108 and the car 3104 are provided with an on-board unit 3107; the on-board unit 3107 transmits real-time data to one or more of the rsus 101 (e.g., to the nearest rsu) via the communication system 3106; the roadside unit 101 collects data from the motorways 3101, the non-motorways 3102, the roadside lanes 3103, and receives data from other roadside units 101, on-board units 3107, navigation satellites, etc. for providing data of objects (e.g., trucks 3108, cars 3104, and other objects) on the motorways 3101, the non-motorways 3102, and/or the roadside lanes 3103 within the roadside unit detection range 3105.
Further, according to the optimization scheme, the localized artificial intelligence system can also predict the behaviors of roadside objects and/or on-road objects; the on-road object comprises a vehicle and/or a bicycle; the roadside object includes a pedestrian or an abnormally moving object (generally, a static roadside object).
Further optimizing the scheme, the localization artificial intelligence system further comprises a safety system, and the safety system comprises safety hardware and/or safety software; the safety system is used for reducing the frequency and severity of collisions; the security system can also provide an active security method, a passive security method; the active security method provides preventive measures before an event occurs by predicting time and estimating risk; the activity safety method is used for rapidly detecting accidents, and before the accidents cause injuries, preventive measures are provided through the activity safety method; the passive safety method is used to eliminate and/or minimize injuries and losses caused by an accident after the accident has occurred.
In a further optimization scheme, the localized artificial intelligence system is further used for transmitting local data from the road side unit to other road side units and/or traffic control units so as to improve the performance and efficiency of the intelligent road infrastructure system; where the local data includes local hardware and/or software configurations, learning algorithms, algorithm parameters, raw data, aggregated data, and data patterns.
Further, according to the optimization scheme, the localized artificial intelligence system can also provide intelligent cooperation between the road side unit and the automatic driving vehicle in an intelligent distribution mode, wherein the automatic driving vehicle is connected with the road side unit so as to improve the performance and robustness of the localized artificial intelligence system, and the intelligent cooperation comprises the use of a crowd-sourcing model; the localized artificial intelligence system can also carry out self-organization control and decentralized system control; the localized artificial intelligence system can also carry out division and distribution tasks on each component of the intelligent road infrastructure system; the localized artificial intelligence system can also provide intelligent collaboration through direct and indirect interactions between the intelligent roadway infrastructure system components.
Further optimizing the scheme, the localized artificial intelligence system further comprises a smart city application interface and/or a third-party system and an application program, and the smart city application is managed by a city; the smart city application is capable of providing information to a hospital, a police department, and/or a fire department; the third party can retrieve and/or transmit data in the artificial intelligence system database.
Further optimizing the scheme, the localization artificial intelligence system is also used for local data sharing; data from a number of data sources and/or a number of sensors is collected and the collected data is shared among the components of the intelligent road infrastructure system, for example, provided to a roadside unit. The data provided by the localized artificial intelligence system to the rsu (e.g., weather conditions, geometric design and/or layout of roads, traffic data, vehicle type distribution) is specific to the location of the rsu (e.g., the data provided to the rsu is specific to the rsu's coverage area). Accordingly, the rsu is provided with data describing the weather conditions within the rsu or the area covered by nearby rsus, the geometric design and/or layout of the road, traffic data, the distribution of vehicle types.
Further optimizing the scheme, wherein the localized artificial intelligence system is also used for transmitting a learning method of model positioning; the localized artificial intelligence system is also capable of obtaining heuristic parameters from a local traffic control center and/or traffic control units through which training of the model is performed to provide an improved model for the relevant task.
Further optimization, the roadside units are deployed at fixed locations near road infrastructure, the fixed installations including near highway curbs, highway merge ramps, highway diversion ramps, intersections, bridges, tunnels, toll booths, or unmanned vehicles at key locations; the roadside unit can also be deployed on a mobile assembly; the roadside unit can be also deployed on an unmanned vehicle, an Unmanned Aerial Vehicle (UAV), a traffic jam road section, a traffic accident high-rise point, an expressway on-building road section and an extreme weather easy-rise road section at key positions; the roadside units are further positioned according to road geometry, heavy vehicle dimensions, heavy vehicle dynamics, heavy vehicle density, and/or heavy vehicle blind areas; the roadside units can also be mounted on a rack (e.g., overhead components, such as highway signs or signal posts); the roadside unit can also be mounted using a single cantilever or a double cantilever support.
In describing the present invention, it is to be understood that the terms "about", "substantially" and "significantly" are understood by those of ordinary skill in the art and will vary to some extent in the context in which they are used. If there is a use of such terms that would not be clear to one of ordinary skill in the art given the context of use, "about" and "about" mean less than or equal to 10% "substantially" and "significantly" of the meaning of the particular term, meaning that greater than 10% is within the scope of the meaning of the particular term. The term "support" when used in reference to one or more components of a CAVH system to provide support and/or support for other one or more other components of the CAVH system refers to the exchange of information and/or data, for example, when sending and/or receiving instructions between components and/or levels of the CAVH system, and/or the functionality of other interactive information exchanges, such as data transmission, messaging and/or alerts, etc., between components and/or different levels of system composition of the CAVH system. The term "IRIS system component" individually and/or collectively refers to one or more of OBU, RSU, TCC, TCU, TCC/TCU, TOC, and/or CAVH cloud components. The term "autonomous vehicle" or "AV" refers to an autonomous vehicle, for example, at any level of automation (e.g., as defined by SAE international standard J3016(2014), incorporated herein by reference). The term "data fusion" refers to the integrated fusion of multiple data sources to provide more consistent, accurate and useful information (e.g., fused data) than any single one of the multiple data sources. The term "background" generally refers to static objects and their characteristics in the road, roadside and road environment, which objects do not change position or change position more slowly than the vehicle position and/or traffic changes. The "background" is substantially and/or substantially invariant over time with respect to changes in vehicle and traffic location as a function of time. The term "local area" refers to an area smaller than the area served by the CAVH system. In some examples, a "local area" refers to a relevant area of a covered road segment or road served by a single rsu, or by the rsu and a single rsu adjacent to the rsu. The term "snow line" refers to a height above the historical average snowfall of an area. In some examples, the "snow line" of an area is 2 to 10 times higher (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 times higher) than the historical average snow depth. A "system" refers to a plurality of real and/or abstract components that operate together for a common purpose. In some examples, a "system" is an integrated component of hardware and/or software components. In some examples, each component of the system interacts with and/or is related to one or more other components. In some examples, a system refers to a combination of some components and software for controlling the indication. The term "coverage area" refers to an area where signals can be detected and/or data recorded; the area where the system provides services (e.g., communications, data, information, and/or control instructions). For example, a "coverage area" of a rsu is the area that the rsu sensor monitors, and from which the rsu (e.g., rsu sensor) can receive signals describing the area; and/or the "coverage area" of the road side unit is the area for which the road side unit is capable of providing data, information and/or control instructions (e.g., to vehicles within the coverage area). In some examples, a "coverage area" of a roadside unit refers to a collection of locations with which an OBU may communicate. Coverage areas may overlap; thus, a location may be in more than one coverage area. In addition, coverage areas may vary, for example, depending on weather, resources, time of day, system requirements, roadside unit deployment, etc. The term "location" refers to a position in space (e.g., a three-dimensional space, a two-dimensional space, and/or a pseudo two-dimensional space (e.g., a curved surface of a block of the earth's surface that actually and/or substantially exhibits a two-dimensional distribution (such as a representation on a two-dimensional map))). In some examples, the "location" (e.g., longitude and latitude) is described using coordinates relative to the earth or a map. In some examples, the "location" is described using coordinates in a coordinate system established by the CAVH system.
The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention, and are not intended to indicate or imply that the referenced devices or elements must be in a particular orientation, constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (10)
1. A localized artificial intelligence system for an intelligent road infrastructure system, the intelligent road infrastructure system comprising a road side unit, an autonomous vehicle, characterized in that the localized artificial intelligence system comprises a database, a sensing unit, a calculation unit; the sensing unit is connected with the computing unit, and the database is connected with the computing unit;
the database is used for storing accumulated historical data, wherein the accumulated historical data comprises compiled sensing data, background of local areas, vehicles, traffic, objects and/or environment data;
the sensing unit is used for providing real-time data, the sensing unit comprises a sensor, the sensor is used for acquiring the real-time data, and the real-time data comprises background, vehicle, traffic, object and/or environment data of a local area;
the computing unit constructs and trains models for identifying and/or detecting vehicles, animals and other objects on the road and/or road side by comparing the real-time data with the accumulated historical data stored in the database, and provides perception, behavior prediction and management, decision making and vehicle control for the intelligent road infrastructure system.
2. The localized artificial intelligence system for intelligent road infrastructure system of claim 1, wherein the localized artificial intelligence system is embedded in one or a group of roadside units; the localization artificial intelligence system also comprises a communication interface, and the communication interface is used for the localization artificial intelligence system to communicate with each component of the intelligent road infrastructure system, the intelligent city and/or a third-party system; the third party can also retrieve and/or transmit data in the artificial intelligence system database.
3. The localized artificial intelligence system for an intelligent road infrastructure system of claim 1, further comprising a reference point for determining a location of an autonomous vehicle; the reference points comprise a vehicle reference point arranged on the automatic driving vehicle, a roadside reference point arranged on the roadside, and/or a road reference point arranged on the road;
the vehicle reference point comprises a vehicle-mounted tag, a Radio Frequency Identification Device (RFID) and/or a visual marker;
the roadside reference point and the road reference point are fixed structures and are used for broadcasting the positions of the fixed structures to the automatic driving vehicle to assist the automatic driving vehicle in determining the positions; the height of the fixing structure is higher than that of the snow line, and the fixing structure is reflective.
4. The localized artificial intelligence system for intelligent roadway infrastructure system of claim 3, wherein the localized artificial intelligence system is further configured to improve the positioning accuracy of the autonomous vehicle; the specific method for positioning the automatic driving vehicle comprises active positioning and/or passive positioning.
5. The localized artificial intelligence system for intelligent road infrastructure system of claim 1, wherein the localized artificial intelligence system is further capable of performing risk location identification and transmitting risk locations to autonomous vehicles and/or roadside units.
6. The localized artificial intelligence system for an intelligent road infrastructure system of claim 1, wherein the localized artificial intelligence system is capable of improving models and/or algorithms for identifying and predicting vehicle and target motion, and also capable of predicting road and/or environmental conditions, and also capable of predicting pedestrian motion, traffic accidents, weather, natural disasters and/or communication failures, and also capable of predicting roadside objects and/or on-road object behaviors by comparing real-time data and accumulated historical data, and by machine learning.
7. The localized artificial intelligence system for intelligent road infrastructure system of claim 1, wherein the perception unit further comprises radar-based sensors and/or vision-based sensors, navigation systems, vehicle identification means; the navigation system comprises a satellite based navigation system and/or an inertial navigation system.
8. The localized artificial intelligence system for an intelligent road infrastructure system of claim 1, further comprising a cloud platform unit capable of sharing real-time environmental and/or road data, historical environmental and/or road data.
9. The localized artificial intelligence system for an intelligent roadway infrastructure system of claim 1, wherein the localized artificial intelligence system further comprises a safety system for reducing impact frequency and severity; the security system can also provide an active security method, a passive security method; the active security method is used to provide precautionary measures before an event occurs; the activity safety method is used for providing preventive measures before an accident causes injury; the passive safety method is used to eliminate and/or reduce injuries and losses caused by an accident after the accident has occurred.
10. The localized artificial intelligence system for an intelligent road infrastructure system of claim 1, wherein the localized artificial intelligence system further provides intelligent collaboration between the roadside unit and the autonomous vehicle, the intelligent collaboration including use of a crowd-sourcing model; the localized artificial intelligence system can also carry out self-organization control and decentralized system control; the localized artificial intelligence system can also perform work division and distribution tasks on the components of the intelligent road infrastructure system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010299071.1A CN111383456B (en) | 2020-04-16 | 2020-04-16 | Localized artificial intelligence system for intelligent road infrastructure system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010299071.1A CN111383456B (en) | 2020-04-16 | 2020-04-16 | Localized artificial intelligence system for intelligent road infrastructure system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111383456A true CN111383456A (en) | 2020-07-07 |
| CN111383456B CN111383456B (en) | 2022-09-27 |
Family
ID=71222909
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010299071.1A Active CN111383456B (en) | 2020-04-16 | 2020-04-16 | Localized artificial intelligence system for intelligent road infrastructure system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111383456B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114061645A (en) * | 2020-07-29 | 2022-02-18 | 丰田自动车株式会社 | Anomaly detection method, infrastructure sensor device, system, and readable medium |
| SE2050960A1 (en) * | 2020-08-19 | 2022-02-20 | Elonroad Ab | An electrical road track module |
| US20220252404A1 (en) * | 2021-02-10 | 2022-08-11 | Ford Global Technologies, Llc | Self-correcting vehicle localization |
| CN115115165A (en) * | 2021-03-18 | 2022-09-27 | 西克股份公司 | System comprising at least one installation system |
| US20220340162A1 (en) * | 2021-04-27 | 2022-10-27 | Toyota Motor Engineering & Manufacturing North America, Inc. | Method and System for On-Demand Roadside AI Service |
| CN115440041A (en) * | 2022-09-02 | 2022-12-06 | 东南大学 | Method for predicting driving behavior of key vehicle under road side view angle |
| WO2023220373A3 (en) * | 2022-05-12 | 2023-12-21 | Lunewave Inc. | Radar identification devices, systems, and methods |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180061230A1 (en) * | 2016-08-29 | 2018-03-01 | Allstate Insurance Company | Electrical Data Processing System for Monitoring or Affecting Movement of a Vehicle Using a Traffic Device |
| WO2018132378A2 (en) * | 2017-01-10 | 2018-07-19 | Cavh Llc | Connected automated vehicle highway systems and methods |
| CN108447291A (en) * | 2018-04-03 | 2018-08-24 | 南京锦和佳鑫信息科技有限公司 | A kind of Intelligent road facility system and control method |
| CN109074737A (en) * | 2016-04-28 | 2018-12-21 | 住友电气工业株式会社 | Safe driving assistant system, server, vehicle and program |
| CN109147370A (en) * | 2018-08-31 | 2019-01-04 | 南京锦和佳鑫信息科技有限公司 | A kind of freeway control system and particular path method of servicing of intelligent network connection vehicle |
| US20190096238A1 (en) * | 2017-06-20 | 2019-03-28 | Cavh Llc | Intelligent road infrastructure system (iris): systems and methods |
| US20190164422A1 (en) * | 2017-11-28 | 2019-05-30 | Honda Motor Co., Ltd. | System and method for providing an infrastructure based safety alert associated with at least one roadway |
| US20190244521A1 (en) * | 2018-02-06 | 2019-08-08 | Cavh Llc | Intelligent road infrastructure system (iris): systems and methods |
| CN110296713A (en) * | 2019-06-17 | 2019-10-01 | 深圳数翔科技有限公司 | Trackside automatic driving vehicle Position Fixing Navigation System and single, multiple vehicle positioning and navigation methods |
| US20190311616A1 (en) * | 2018-04-10 | 2019-10-10 | Cavh Llc | Connected and automated vehicle systems and methods for the entire roadway network |
| CN110874945A (en) * | 2018-08-31 | 2020-03-10 | 百度在线网络技术（北京）有限公司 | Roadside sensing system based on vehicle-road cooperation and vehicle control method thereof |
| CN110895877A (en) * | 2018-08-24 | 2020-03-20 | 南京锦和佳鑫信息科技有限公司 | Intelligent distribution system and method for vehicle road driving tasks |
| CN110930747A (en) * | 2018-09-20 | 2020-03-27 | 南京锦和佳鑫信息科技有限公司 | Intelligent internet traffic service system based on cloud computing technology |
| CN110969833A (en) * | 2018-09-30 | 2020-04-07 | 南京锦和佳鑫信息科技有限公司 | Fixed path service system of intelligent network traffic system |
-
2020
- 2020-04-16 CN CN202010299071.1A patent/CN111383456B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109074737A (en) * | 2016-04-28 | 2018-12-21 | 住友电气工业株式会社 | Safe driving assistant system, server, vehicle and program |
| US20180061230A1 (en) * | 2016-08-29 | 2018-03-01 | Allstate Insurance Company | Electrical Data Processing System for Monitoring or Affecting Movement of a Vehicle Using a Traffic Device |
| WO2018132378A2 (en) * | 2017-01-10 | 2018-07-19 | Cavh Llc | Connected automated vehicle highway systems and methods |
| US20190096238A1 (en) * | 2017-06-20 | 2019-03-28 | Cavh Llc | Intelligent road infrastructure system (iris): systems and methods |
| US20190164422A1 (en) * | 2017-11-28 | 2019-05-30 | Honda Motor Co., Ltd. | System and method for providing an infrastructure based safety alert associated with at least one roadway |
| US20190244521A1 (en) * | 2018-02-06 | 2019-08-08 | Cavh Llc | Intelligent road infrastructure system (iris): systems and methods |
| CN108447291A (en) * | 2018-04-03 | 2018-08-24 | 南京锦和佳鑫信息科技有限公司 | A kind of Intelligent road facility system and control method |
| US20190311616A1 (en) * | 2018-04-10 | 2019-10-10 | Cavh Llc | Connected and automated vehicle systems and methods for the entire roadway network |
| CN110895877A (en) * | 2018-08-24 | 2020-03-20 | 南京锦和佳鑫信息科技有限公司 | Intelligent distribution system and method for vehicle road driving tasks |
| CN109147370A (en) * | 2018-08-31 | 2019-01-04 | 南京锦和佳鑫信息科技有限公司 | A kind of freeway control system and particular path method of servicing of intelligent network connection vehicle |
| CN110874945A (en) * | 2018-08-31 | 2020-03-10 | 百度在线网络技术（北京）有限公司 | Roadside sensing system based on vehicle-road cooperation and vehicle control method thereof |
| CN110930747A (en) * | 2018-09-20 | 2020-03-27 | 南京锦和佳鑫信息科技有限公司 | Intelligent internet traffic service system based on cloud computing technology |
| CN110969833A (en) * | 2018-09-30 | 2020-04-07 | 南京锦和佳鑫信息科技有限公司 | Fixed path service system of intelligent network traffic system |
| CN110296713A (en) * | 2019-06-17 | 2019-10-01 | 深圳数翔科技有限公司 | Trackside automatic driving vehicle Position Fixing Navigation System and single, multiple vehicle positioning and navigation methods |
Non-Patent Citations (1)
| Title |
|---|
| 冉斌等: "智能网联交通技术发展现状及趋势", 《汽车安全与节能学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114061645A (en) * | 2020-07-29 | 2022-02-18 | 丰田自动车株式会社 | Anomaly detection method, infrastructure sensor device, system, and readable medium |
| CN114061645B (en) * | 2020-07-29 | 2024-04-09 | 丰田自动车株式会社 | Anomaly detection method, infrastructure sensor device, system, and readable medium |
| SE2050960A1 (en) * | 2020-08-19 | 2022-02-20 | Elonroad Ab | An electrical road track module |
| SE545436C2 (en) * | 2020-08-19 | 2023-09-12 | Elonroad Ab | An electrical road track module |
| US20220252404A1 (en) * | 2021-02-10 | 2022-08-11 | Ford Global Technologies, Llc | Self-correcting vehicle localization |
| CN115115165A (en) * | 2021-03-18 | 2022-09-27 | 西克股份公司 | System comprising at least one installation system |
| US20220340162A1 (en) * | 2021-04-27 | 2022-10-27 | Toyota Motor Engineering & Manufacturing North America, Inc. | Method and System for On-Demand Roadside AI Service |
| US11661077B2 (en) | 2021-04-27 | 2023-05-30 | Toyota Motor Engineering & Manufacturing North America. Inc. | Method and system for on-demand roadside AI service |
| WO2023220373A3 (en) * | 2022-05-12 | 2023-12-21 | Lunewave Inc. | Radar identification devices, systems, and methods |
| CN115440041A (en) * | 2022-09-02 | 2022-12-06 | 东南大学 | Method for predicting driving behavior of key vehicle under road side view angle |
| CN115440041B (en) * | 2022-09-02 | 2023-05-30 | 东南大学 | Method for predicting key vehicle driving behavior under road side view angle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111383456B (en) | 2022-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111383456B (en) | Localized artificial intelligence system for intelligent road infrastructure system | |
| US11685360B2 (en) | Planning for unknown objects by an autonomous vehicle | |
| US12002361B2 (en) | Localized artificial intelligence for intelligent road infrastructure | |
| US11400925B2 (en) | Planning for unknown objects by an autonomous vehicle | |
| CN111292540B (en) | Method, control device and system for determining specific state information | |
| US11920948B2 (en) | Vehicle-side device, method, and non-transitory computer-readable storage medium for uploading map data | |
| CN113002396B (en) | A environmental perception system and mining vehicle for automatic driving mining vehicle | |
| US11874119B2 (en) | Traffic boundary mapping | |
| US10234864B2 (en) | Planning for unknown objects by an autonomous vehicle | |
| US8903640B2 (en) | Communication based vehicle-pedestrian collision warning system | |
| US12037015B2 (en) | Vehicle control device and vehicle control method | |
| CN114360269A (en) | Automatic driving cooperative control system and method under intelligent network connection road support | |
| DE10149206A1 (en) | Method and device for mapping a road and accident prevention system | |
| CN104572065A (en) | Remote vehicle monitoring | |
| US20210383686A1 (en) | Roadside computing system for predicting road user trajectory and assessing travel risk | |
| CN111781933A (en) | High-speed automatic driving vehicle implementation system and method based on edge calculation and spatial intelligence | |
| CN110296708B (en) | Operation route planning method, device and storage medium | |
| US20210294331A1 (en) | Object identification for autonomous road vehicles | |
| US20230260393A1 (en) | Traffic management device, traffic management system, traffic information system, starting module that can be retrofitted and method for managing traffic | |
| US20230222908A1 (en) | Roadway information detection system consists of sensors on the autonomous vehicles and devices for the road | |
| CN110869703A (en) | Navigation method and navigation equipment | |
| Xu et al. | High-resolution micro traffic data from roadside LiDAR sensors for connected-vehicles and new traffic applications | |
| CN111688688A (en) | Implementation of rollback at a traffic node for a previously traveling vehicle | |
| CN116142178A (en) | Vehicle auxiliary driving method, system, medium and electronic equipment | |
| WO2018165199A1 (en) | Planning for unknown objects by an autonomous vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |