AU2020102543A4 - Iot based trust modeling approach for cooperative autonomous driving and intelligent transport systems (its) - Google Patents
Iot based trust modeling approach for cooperative autonomous driving and intelligent transport systems (its) Download PDFInfo
- Publication number
- AU2020102543A4 AU2020102543A4 AU2020102543A AU2020102543A AU2020102543A4 AU 2020102543 A4 AU2020102543 A4 AU 2020102543A4 AU 2020102543 A AU2020102543 A AU 2020102543A AU 2020102543 A AU2020102543 A AU 2020102543A AU 2020102543 A4 AU2020102543 A4 AU 2020102543A4
- Authority
- AU
- Australia
- Prior art keywords
- learning
- block
- autonomous driving
- vehicle
- federated
- 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.)
- Ceased
Links
- 238000013459 approach Methods 0.000 title abstract description 22
- 238000005516 engineering process Methods 0.000 abstract description 17
- 238000013526 transfer learning Methods 0.000 abstract description 11
- 238000012546 transfer Methods 0.000 abstract description 7
- 241001465754 Metazoa Species 0.000 abstract description 6
- 230000003044 adaptive effect Effects 0.000 abstract description 6
- 238000000034 method Methods 0.000 abstract description 6
- 238000011160 research Methods 0.000 abstract description 2
- 238000005065 mining Methods 0.000 description 10
- 238000010200 validation analysis Methods 0.000 description 8
- 230000002776 aggregation Effects 0.000 description 4
- 238000004220 aggregation Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
- G06F21/645—Protecting data integrity, e.g. using checksums, certificates or signatures using a third party
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6272—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database by registering files or documents with a third party
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
- G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of 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
- G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- Bioethics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
IOT BASED TRUST MODELING APPROACH FOR COOPERATIVE
AUTONOMOUS DRIVING AND INTELLIGENT TRANSPORT SYSTEMS
(ITS)
ABSTRACT
As every action is performed autonomously, the research is carried over vehicle to
operate it autonomously using Intelligent Traffic System (ITS). The learning is
happened using federated learning scheme. Based on the technique each and every
edge devices are connected with the server. Based on the data from the server it is
shared to all other local devices which are connected to it. The approaches of
learning are Horizontal federation learning in which the features are the same but
the samples are different. Vertical federative learning in which the samples are the
same but the features are different. Federated transfer learning which transfer the
learning. The vehicles are categorized as V2X domain to prevent accidents and In
vehicle domain. By this approach they can know the traffic rate, distance of the
neighboring active vehicle etc with the help of Electronic Control Unit (ECU). There
are also certain factors which affect the autonomous driving. They are electronic
stability control, automatic braking and adaptive cruise control. The other
technologies used for maintaining it properly. The obstacles are identified using ultra
sonic sensor. LiDaR or radar is used to prevent accidents. Cameras with high
definition are used to detect the pedestrians and animals. Global Navigation Satellite
System receivers are used to provide position accuracy of the vehicle. V2X
technology is used to communicate with the neighbor. It is also essential to provide
security as the details are shared among the vehicles. For maintaining security block
chain is used. Block chain is an emerging technology which is transferring crypto
currencies. The format of the block is a quadruple which contains the parameters
like (Block ID, Transactions contained in that Block, Hash of the previous approved
block on the chain, hash of the whole Block). Thus the trust modeling is
implemented.
1|Page
IOT BASED TRUST MODELING APPROACH FOR COOPERATIVE
AUTONOMOUS DRIVING AND INTELLIGENT TRANSPORT SYSTEMS
(ITS)
Drawings
local new global local
update} m updates
oc data ta locamu data
next-~wo~rdIalOfln~
ocal data
Figure 1: Federated learning layout
11 P a g e
Description
Drawings
local new global local update} m updates
oc data ta locamu data
next-~wo~rdIalOfln~ ocal data
Figure 1: Federated learning layout
11 P a g e
Description
Field of the Invention:
This invention relates to providing a trust modeling approach for cooperative autonomous driving and intelligent transport systems. As every action is performed autonomously, the research is carried over vehicle to operate it autonomously using Intelligent Traffic System (ITS). The learning is happened using federated learning scheme. Based on the technique each edge device is connected with the server. Based on the data from the server it is shared to all other local devices which are connected to it. The approaches of learning are Horizontal federation learning in which the features are the same, but the samples are different. Vertical federative learning in which the samples are the same, but the features are different. Federated transfer learning which transfer the learning. The vehicles are categorized as V2X domain to prevent accidents and In-vehicle domain. By this approach they can know the traffic rate, distance of the neighboring active vehicle etc with the help of Electronic Control Unit (ECU). There are also certain factors which affect the autonomous driving. They are electronic stability control, automatic braking, and adaptive cruise control. The other technologies used for maintaining it properly. The obstacles are identified using ultra sonic sensor. LiDaR or radar is used to prevent accidents. Cameras with high definition are used to detect the pedestrians and animals. Global Navigation Satellite System receivers are used to provide position accuracy of the vehicle. V2X technology is used to communicate with the neighbor. It is also essential to provide security as the details are shared among the vehicles. For maintaining security block chain is used. Block chain is an emerging technology which is transferring crypto currencies. The format of the block is a quadruple which contains the parameters like (Block ID, Transactions contained in that Block, Hash of the previous approved block on the chain, hash of the whole Block). Thus, the trust modeling is implemented.
11 P a g e
Background of the invention:
Zhaoyang Du et al., suggests the federated learning scheme for the intelligent transport system. Through which it identified deeply about the vehicle. The approaches of learning were found to fall in three categories namely: horizontal federation learning, Vertical federative learning, and federated transfer learning. The horizontal learning means the features are the same, but the samples are different. The Vertical federative learning is vice versa. The federated transfer learning will transfer the learning which took place. The points to consider are client selection, model dissemination, distributed learning, client feedback, aggregation, model testing and model update. For maintaining security block chain is used.
Anitha Chepuru et al., suggests the IOT application for intelligent transport system. It categorizes the vehicles into two categories namely V2X domain and In-vehicle domain. By this approach they can know the traffic rate, distance of the neighbouring active vehicle etc with the help of Electronic Control Unit (ECU)
Sergey Chuprov et al., suggest two approaches for the intelligent system. Based on which the trust and reputation. Truth is a character which recognizes correctness. The reputation is the score based on the trust value.
Elyes Ben Hamida et al., suggests various factors affecting the autonomous driving in the intelligent transport system are electronic stability control, automatic braking, and adaptive cruise control. The obstacles are identified using ultra sonic sensor. LiDaR or radar are used to prevent accidents. Cameras with high definition are used to detect the pedestrians and animals. Global Navigation Satellite System receivers are used to provide position accuracy of the vehicle. V2X technology is used to communicate with the neighbor.
Tsutomu Yoshinaga et al., suggests the federated learning scheme for the intelligent transport system. Through which it identified deeply about the vehicle. The approaches of learning were found to fall in three categories namely: horizontal federation learning, Vertical federative learning and federated transfer learning. The horizontal learning means the features are the same but the samples are different. The Vertical federative learning is vice versa. The federated transfer learning will
2 1P a g e transfer the learning which took place. The points to consider are client selection, model dissemination, distributed learning, client feedback, aggregation, model testing and model update. For maintaining security block chain is used.
Bigi Varghese Philip et al., suggests how the autonomous vehicles take their own decision without interference to cross in the IoT. Also the same was optimized.
Seng W. Loke suggests a cooperative automated vehicle. By using this technology, the other vehicles can know all the other vehicle information and they will predict.
Objects of the Invention:
• To provide learning using federated learning scheme • Analyze the approaches of learning • Categorizing of vehicles into V2X domain and In-vehicle domain to prevent accidents • Identifying the factors influencing the autonomous vehicles • Technologies used to provide • Maintaining security using block chain
Summary of the Invention
The federated learning scheme for the intelligent transport system is used. Based on the technique each and every edge devices are connected with the server. Based on the data from the server it is shared to all other local devices which are connected to it.
Through which it identified deeply about the vehicle. The approaches of learning were found to fall in three categories namely: horizontal federation learning, Vertical federative learning and federated transfer learning. The horizontal learning means the features are the same but the samples are different. For example the mobile phone is an end device and the feature in it are the same but the data in it may vary according to the owner of that device.
The Vertical federative learning is vice versa. The federated transfer learning will transfer the learning which took place. The points to consider are client selection,
3 1P a g e model dissemination, distributed learning, client feedback, aggregation, model testing and model update.
For maintaining security block chain is used. Block chain is an emerging technology which is transferring crypto currencies. As it requires high security the same is encrypted through the validation. In which each and every system contains the block chain which is connected in a collaborative mining network which in turn connected to the edge cloud, as it need to store huge amount of data. The remedy is carried out by proposing a novel designing a collaborative mining network to execute computationally intense mining of tasks in the mobile block chain. This is done in an offline manner to manage the resources when the resources are insufficient.
Also in block chain the sellers will try to mine the data from the block chain. Miners will validate the transactions. The validations are processed in a batch. This Batch is known as a Block. For each and every block, the size is approximately equal to 1MB. The format of the block is a quadruple which contains the parameters like (Block ID, Transactions contained in that Block, Hash of the previous approved block on the chain, hash of the whole Block). Each and every blocks are connected as a chain as like a linked list. The first block is known as Genesis Block.
The Miners are always validates the transaction of a block. Validation process contains many rules for the transaction named as Transactional consensus. Once the mining finished after validation in the transactions, the block was formed. They won't broadcast the block at once to the user because the validated user needs to frame a Proof-of-Work. This is a computation-intensive task. This needs to be attached in every validated block.
After finding the solution, the broadcasting of the block happens and the other miners will accept the block and they will move on with the child block. When the acceptance ratio of the miners is more than 50 percentages then it is said to be the finally mining stage and the others will move on with the newly created child block as they accepted this.
This Proof-of-work is a very important stage as it helps in making the whole Block chain immutable. This is susceptible to frauds as they may know the hash and they may change any of a number in a block and they may try to own it. In such a situation that miner wants to redo all the collaborations within the other miner completes the
41Page block. Unless the miner in the group accepts more than 51 percentages the fraud may change it. Thus it is more secure.
The vehicles are classified into two categories namely V2X domain and In-vehicle domain. By this approach they can know the traffic rate, distance of the neighboring active vehicle etc with the help of Electronic Control Unit (ECU)
V2X domain is used to prevent accidents. It will prevent accident by implementing the same. By using so it will check the pedestrian or object or network or device which is hindering. In addition to preventing accidents it also effectively manages the traffic flows and also improves the energy.
In-vehicle domain is used to add the local device which is checked in the network. So that the other entire vehicle in the network will know the same and vice versa.
The various factors affecting the autonomous driving in the intelligent transport system are electronic stability control, automatic braking, and adaptive cruise control. The obstacles are identified using ultra sonic sensor. LiDaR or radar is used to prevent accidents. Cameras with high definition are used to detect the pedestrians and animals. Global Navigation Satellite System receivers are used to provide position accuracy of the vehicle. V2X technology is used to communicate with the neighbor.
Detailed Description of the Invention:
The federated learning scheme as shown in figure 1 for the intelligent transport system is used. Based on the technique each and every edge devices are connected with the server. Based on the data from the server it is shared to all other local devices which are connected to it.
Through which it identified deeply about the vehicle. The approaches of learning were found to fall in three categories namely: horizontal federation learning, Vertical federative learning and federated transfer learning. The horizontal learning means the features are the same but the samples are different. For example the mobile phone is an end device and the feature in it are the same but the data in it may vary according to the owner of that device.
51Page
The Vertical federative learning is vice versa. The federated transfer learning will transfer the learning which took place. The points to consider are client selection, model dissemination, distributed learning, client feedback, aggregation, model testing and model update.
For maintaining security block chain is used. Block chain is an emerging technology which is transferring crypto currencies. As it requires high security the same is encrypted through the validation. In which each and every system contains the block chain which is connected in a collaborative mining network which in turn connected to the edge cloud, as it need to store huge amount of data. The remedy is carried out by proposing a novel designing a collaborative mining network to execute computationally intense mining of tasks in the mobile block chain. This is done in an offline manner to manage the resources when the resources are insufficient.
Also in block chain the sellers will try to mine the data from the block chain. Miners will validate the transactions. The validations are processed in a batch. This Batch is known as a Block. For each and every block, the size is approximately equal to 1MB. The format of the block is a quadruple which contains the parameters like (Block ID, Transactions contained in that Block, Hash of the previous approved block on the chain, hash of the whole Block). Each and every blocks are connected as a chain as like a linked list. The first block is known as Genesis Block.
The Miners are always validates the transaction of a block. Validation process contains many rules for the transaction named as Transactional consensus. Once the mining finished after validation in the transactions, the block was formed. They won't broadcast the block at once to the user because the validated user needs to frame a Proof-of-Work. This is a computation-intensive task. This needs to be attached in every validated block.
After finding the solution, the broadcasting of the block happens and the other miners will accept the block and they will move on with the child block. When the acceptance ratio of the miners is more than 50 percentages then it is said to be the finally mining stage and the others will move on with the newly created child block as they accepted this.
This Proof-of-work is a very important stage as it helps in making the whole Block chain immutable. This is susceptible to frauds as they may know the hash and they
61Page may change any of a number in a block and they may try to own it. In such a situation that miner wants to redo all the collaborations within the other miner completes the block. Unless the miner in the group accepts more than 51 percentages the fraud may change it. Thus it is more secure.
The vehicles are classified into two categories namely V2X domain depicted in figure 2 and In-vehicle domain. By this approach they can know the traffic rate, distance of the neighboring active vehicle etc with the help of Electronic Control Unit (ECU)
V2X domain is used to prevent accidents. It will prevent accident by implementing the same. So it will check the pedestrian or object or network or device which is hindering. In addition to preventing accidents it also effectively manages the traffic flows and also improves the energy.
In-vehicle domain is used to add the local device which is checked in the network. Thus the other entire vehicle in the network will know the same and vice versa.
The various factors affecting the autonomous driving in the intelligent transport system are electronic stability control, automatic braking, and adaptive cruise control. The obstacles are identified using ultra sonic sensor. LiDaR or radar is used to prevent accidents. Cameras with high definition are used to detect the pedestrians and animals. Global Navigation Satellite System receivers are used to provide position accuracy of the vehicle. V2X technology is used to communicate with the neighbor.
71Page
The proposed method is capable of:
1. Providing Learning: •Using federated learning scheme •Based on the technique each and every edge devices are connected with the server. • Based on the data from the server it is shared to all other local devices which are connected to it. 2. Approaches of learning: • Horizontal federation learning: the features are the same but the samples are different. • Vertical federative learning: the samples are the same but the features are different. • Federated transfer learning: transfer the learning which took place. 3.Security: • For maintaining security block chain is used. • Block chain is an emerging technology which is transferring crypto currencies. • The format of the block is a quadruple which contains the parameters like (Block ID, Transactions contained in that Block, Hash of the previous approved block on the chain, hash of the whole Block). 4. Categories of vehicles: • V2X domain: prevent accidents • In-vehicle domain. • By this approach they can know the traffic rate, distance of the neighboring active vehicle etc with the help of Electronic Control Unit (ECU) 5. Factors affecting the autonomous driving: • Electronic stability control
1| P a g e
• Automatic braking • Adaptive cruise control. 6. Technologies used: o The obstacles are identified using ultra sonic sensor. o LiDaR or radar is used to prevent accidents. o Cameras with high definition are used to detect the pedestrians and animals. o Global Navigation Satellite System receivers are used to provide position accuracy of the vehicle. o V2X technology is used to communicate with the neighbor.
21Page
Drawings 2020102543
Figure 1: Federated learning layout
1|Page
Figure 2: V2X technology
2|Page
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2020102543A AU2020102543A4 (en) | 2020-09-30 | 2020-09-30 | Iot based trust modeling approach for cooperative autonomous driving and intelligent transport systems (its) |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2020102543A AU2020102543A4 (en) | 2020-09-30 | 2020-09-30 | Iot based trust modeling approach for cooperative autonomous driving and intelligent transport systems (its) |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2020102543A4 true AU2020102543A4 (en) | 2020-11-19 |
Family
ID=73249730
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2020102543A Ceased AU2020102543A4 (en) | 2020-09-30 | 2020-09-30 | Iot based trust modeling approach for cooperative autonomous driving and intelligent transport systems (its) |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2020102543A4 (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112511553A (en) * | 2020-12-08 | 2021-03-16 | 清华大学 | Hierarchical Internet trust degree sharing method |
| CN112650220A (en) * | 2020-12-04 | 2021-04-13 | 东风汽车集团有限公司 | Automatic vehicle driving method, vehicle-mounted controller and system |
| CN112863175A (en) * | 2020-12-31 | 2021-05-28 | 平安科技（深圳）有限公司 | Automobile road monitoring data processing method, device, equipment and storage medium |
| CN113313264A (en) * | 2021-06-02 | 2021-08-27 | 河南大学 | Efficient federal learning method in Internet of vehicles scene |
| CN114281887A (en) * | 2021-04-07 | 2022-04-05 | 暗链科技(深圳)有限公司 | Data storage method based on block distributed block chain and electronic equipment |
| CN114629821A (en) * | 2020-12-10 | 2022-06-14 | 新智云数据服务有限公司 | Internet of things usage data generation method, device, equipment and medium |
| CN116866963A (en) * | 2023-09-04 | 2023-10-10 | 中汽研（天津）汽车工程研究院有限公司 | Virtual-real fusion V2X expected functional safety robustness testing method and system |
-
2020
- 2020-09-30 AU AU2020102543A patent/AU2020102543A4/en not_active Ceased
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112650220A (en) * | 2020-12-04 | 2021-04-13 | 东风汽车集团有限公司 | Automatic vehicle driving method, vehicle-mounted controller and system |
| CN112511553A (en) * | 2020-12-08 | 2021-03-16 | 清华大学 | Hierarchical Internet trust degree sharing method |
| CN114629821A (en) * | 2020-12-10 | 2022-06-14 | 新智云数据服务有限公司 | Internet of things usage data generation method, device, equipment and medium |
| CN114629821B (en) * | 2020-12-10 | 2023-11-10 | 新智云数据服务有限公司 | Internet of things consumption data generation method, device, equipment and medium |
| CN112863175A (en) * | 2020-12-31 | 2021-05-28 | 平安科技（深圳）有限公司 | Automobile road monitoring data processing method, device, equipment and storage medium |
| CN112863175B (en) * | 2020-12-31 | 2022-11-22 | 平安科技（深圳）有限公司 | Automobile road monitoring data processing method, device, equipment and storage medium |
| CN114281887A (en) * | 2021-04-07 | 2022-04-05 | 暗链科技(深圳)有限公司 | Data storage method based on block distributed block chain and electronic equipment |
| CN114281887B (en) * | 2021-04-07 | 2022-08-05 | 暗链科技(深圳)有限公司 | Data storage method based on block distributed block chain and electronic equipment |
| CN113313264A (en) * | 2021-06-02 | 2021-08-27 | 河南大学 | Efficient federal learning method in Internet of vehicles scene |
| CN113313264B (en) * | 2021-06-02 | 2022-08-12 | 河南大学 | Efficient federal learning method in Internet of vehicles scene |
| CN116866963A (en) * | 2023-09-04 | 2023-10-10 | 中汽研（天津）汽车工程研究院有限公司 | Virtual-real fusion V2X expected functional safety robustness testing method and system |
| CN116866963B (en) * | 2023-09-04 | 2023-12-08 | 中汽研（天津）汽车工程研究院有限公司 | Virtual-real fusion V2X expected functional safety robustness testing method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2020102543A4 (en) | Iot based trust modeling approach for cooperative autonomous driving and intelligent transport systems (its) | |
| Parekh et al. | A review on autonomous vehicles: Progress, methods and challenges | |
| DE112020001663T5 (en) | Autonomous Vehicle System | |
| Althoff et al. | Set-based prediction of traffic participants on arbitrary road networks | |
| CN109714421A (en) | Intelligent network based on bus or train route collaboration joins automobilism system | |
| Driss et al. | A federated learning framework for cyberattack detection in vehicular sensor networks | |
| Wang et al. | A review of intelligent connected vehicle cooperative driving development | |
| Mo et al. | A method of vehicle-infrastructure cooperative perception based vehicle state information fusion using improved kalman filter | |
| Wang et al. | A new era of intelligent vehicles and intelligent transportation systems: Digital twins and parallel intelligence | |
| Mo et al. | Simulation and analysis on overtaking safety assistance system based on vehicle-to-vehicle communication | |
| CN113312760B (en) | Network online motor vehicle right-turn track planning method and device based on traffic simulation | |
| CN112418696A (en) | Method and device for constructing urban traffic dynamic knowledge map | |
| Hahanov et al. | Cyber physical system-smart cloud traffic control | |
| Fiosina | Explainable Federated Learning for Taxi Travel Time Prediction. | |
| CN112249009A (en) | Vehicle speed control method, device and system and electronic equipment | |
| Cheng et al. | Behavexplor: Behavior diversity guided testing for autonomous driving systems | |
| Bai et al. | Vinet: Lightweight, scalable, and heterogeneous cooperative perception for 3d object detection | |
| Wei et al. | Planning a Continuous Vehicle Trajectory for an Automated Lane Change Maneuver by Nonlinear Programming considering Car‐Following Rule and Curved Roads | |
| Cheng et al. | Safety assessment of vehicle behaviour based on the improved D–S evidence theory | |
| Jiao | [Retracted] Intelligent Research Based on Deep Learning Recognition Method in Vehicle‐Road Cooperative Information Interaction System | |
| Lee et al. | A framework for detecting vehicle occupancy based on the occupant labeling method | |
| Liu et al. | A Vehicle Detection Model Based on 5G‐V2X for Smart City Security Perception | |
| Xu et al. | [Retracted] Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles | |
| Wang et al. | A 64‐Line Lidar‐Based Road Obstacle Sensing Algorithm for Intelligent Vehicles | |
| CN114440914A (en) | Path planning method, device, electronic equipment, storage medium and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |