Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving
Abstract
:1. Introduction
2. IoV Simulation Technology
2.1. Traffic Simulations
2.1.1. Traffic Flow Model
2.1.2. Vehicle Trajectory Model
2.2. Network Simulation
2.3. IoV Simulation Platform
2.3.1. Joint Simulation Platform
2.3.2. Integrated Simulation Platform
3. Autonomous Driving Simulation Technology
3.1. Autonomous Driving Simulation Development Steps
3.2. Autonomous Driving Simulation Platform
3.2.1. Prescan
3.2.2. AirSim
3.2.3. VTD
3.2.4. Vissim
3.2.5. CarSim
4. Simulation Experiment and Results
4.1. Experimental Purpose
4.2. Experimental Steps
4.2.1. Define the Experimental Objectives
4.2.2. Establish Simulation Road Network
4.3. Experimental Scheme and Result Analysis
4.3.1. Simulation Scene Effect Analysis
4.3.2. Car-Following Model, Lane Changing Model, and Model Spacing
4.3.3. Vehicle Behaviors at Intersection with Yellow Light
4.3.4. Bus Stops and Bus Priority
5. Conclusions
- With the change in the global environment and the increase in extreme weather, the topic of environmental protection has been given more and more attention in many countries. While an building intelligent transportation system, vehicle environmental protection has also become one of the vehicle performance indicators of concern among researchers. After comparing the vehicle networking simulation software involved in this paper, Veins simulation software supports the creation and simulation of a vehicle emission model. Therefore, when the required research involves emission problems, this paper recommends using Veins simulation software for simulation.
- Secondly, 3D technology is currently a popular technology, which is sought after and utilized in many fields. Having a scene interface with a good 3D effect is the configuration of the current mainstream traffic simulation software, which can enable users to create simulation scenes more intuitively and stereoscopically. Therefore, when 3D models need to be considered during modeling, Corsim software cannot be selected because it does not support a 3D interface. On the contrary, TransModeler, a simulation software, not only supports 3D modeling, but also has an excellent 3D effect.
- The operation law of domestic traffic flow is different from that of foreign countries in many aspects, especially when the signal light is yellow, as the driver’s response and vehicle operation are different. In real life, when a driver encounters a yellow light, he is more inclined to accelerate through the intersection rather than slow down and stop. Therefore, many foreign simulation software programs do not consider this point, such as Corsim, while a few software programs consider this point but do not set a variable driver response model, and the simulation effect is quite different from the actual situation, such as in Vissim and Prescan.
- One major difference between intelligent vehicles and other traditional vehicles is that intelligent vehicles can avoid collisions independently. The vehicle collects the surrounding environment and vehicle information through the on-board sensor, uploads the original data to MEC for calculation and processing through 5G communication technology, and then transmits the processed auxiliary information back to the vehicle end to form a closed loop. Among the common vehicle networking simulation software, Vissim has a better effect than other simulation software in two aspects: a wireless obstacle model and vehicle lane changing model. Therefore, when the vehicle collision avoidance problem needs to be studied and designed, Vissim simulation software is recommended in this paper.
- Prescan simulation software adopts a simple vehicle dynamics model in vehicle dynamics, which cannot accurately control the intelligent vehicle in the vertical and horizontal directions, nor can it reflect the dynamic characteristics of the vehicle in the vertical direction. When the required research involves vehicle dynamics, CarSim simulation software is recommended in this paper.
Author Contributions
Funding
Conflicts of Interest
References
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Simulation Platform | Veins | Corsim | TraNS | VSimRTI | TransModeler | NCTUns |
---|---|---|---|---|---|---|
Open source | Yes | No | Yes | Yes | Yes | Yes |
Real map | Support | No Support | Support | Support | Support | Support |
Occlusion model | Support | Support | No support | Support | Support | Support |
Emission model | No support | No Support | No support | Support | No Support | No support |
Channel simulation | Support | No support | Support | No support | No Support | No support |
Large-scale simulation | Support | Support | No support | Support | Support | Support |
Bit-level simulation | No | No | Yes | No | Yes | No |
Wireless network communication protocol | LIE/DSRC WAVE IEE.802.11p | WiMAX IEEE.802.11p | IEEE.802.11p | LIE IEEE.802.11p | IEEE.802.11p | WiMAX/DSRC WAVE IEEE.802.11p |
Level | Name | Narrative Definition | DDT | DDT Fallback | ODD | |
---|---|---|---|---|---|---|
Sustained Lateral and Longitudinal Vehicle Motion Control | OEDR | |||||
Driver performs part or all of the DDT | ||||||
0 | No Driving Automation | The performance by the driver of the entire DDT, even when enhanced by active safety systems. | Driver | Driver | Driver | n/a |
1 | Driver Assistance | The sustained and ODD-specific execution by a driving automation system of either the lateral or the longitudinal vehicle motion control subtask of the DDT (but not both simultaneously) with the expectation that the driver performs the remainder of the DDT. | Driver and System | Driver | Driver | Limited |
2 | Partial Driving Automation | The sustained and ODD-specific execution by a driving automation system of both the lateral and longitudinal vehicle motion control subtask of the DDT with the expectation that the driver completes the OEDR subtask and supervises the driving automation system. | System | Driver | Driver | Limited |
ADS (“System”) performs the entire DDT (while engaged) | System | System | Fallback-ready user (becomes the driver during fallback) | Limited | ||
3 | Conditional Driving Automation | The sustained and ODD-specific performance by an ADS of the entire DDT with the expectation that the DDT fallback-ready user is receptive to ADS-issued requests to intervene, as well as to DDT performance-relevant system failures in other vehicle systems, and will respond appropriately. | ||||
4 | High Driving Automation | The sustained and ODD-specific performance by an ADS of the entire DDT and DDT fallback without any expectation that a user will respond to a request to intervene. | System | System | System | Limited |
5 | Full Driving Automation | The sustained and unconditional (i.e., not ODD-specific) performance by an ADS of the entire DDT and DDT fallback without any expectation that a user will respond to a request to intervene. | System | System | System | Unlimited |
Level | Level 0 | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 |
---|---|---|---|---|---|---|
Name | Emergency assistance | Partial driving assistance | Combined driving assistance | Conditional automation | High automation | Full automation |
Narrative Definition | The driving automation system cannot continuously perform the lateral or longitudinal motion control of the vehicle in the dynamic driving task, but it has the ability to continuously perform the detection and response of some targets and events in the dynamic driving task | The driving automation system continuously performs vehicle lateral or longitudinal motion control in dynamic driving tasks within its design operating conditions, and it has the ability to detect and respond to some targets and events that are compatible with the executed vehicle lateral or longitudinal motion control | The driving automation system continuously performs vehicle lateral and longitudinal motion control in dynamic driving tasks within its design operating conditions, and it has the ability to detect and respond to some targets and events that are compatible with the executed vehicle lateral and longitudinal motion control | The driving automation system continuously performs all dynamic driving tasks within its design operating conditions | The driving automation system continuously performs all dynamic driving tasks and performs dynamic driving task takeover within its design operating conditions | The driving automation system continuously performs all dynamic driving tasks and performs dynamic task takeover under any diving conditions |
Driving operation | Human Driver + System | System |
Name | Prescan | AirSim | Vissim | VTD | CarSim |
---|---|---|---|---|---|
Open source | Business | Open source | Business | Business | Business |
Visualization | Excellent graphical interactive, providing a variety of visual models | High reduction and rendering ability, three-dimensional environment, real light and shadow effects | 2D/3D scene map, general picture effect | High-precision real-time image rendering capability | Intuitive graphical user interface, results can be presented with 3D animation |
Vehicle dynamics | Simple | Simple | Simple | Simple | Real reaction |
Autopilot algorithm verification suitability | Suitable | Suitable | Non-suitable | Suitable | Non-suitable |
Professional degree simulation | Ordinary | Moderate | Ordinary | Ordinary | Major |
Complexity | Simple | Simple | Simple | Simple | Complex |
Difficulty of getting started | Easy | Moderate | Moderate | Easy | Harder |
Name | Vissim | Prescan | TransModeler | Corsim |
---|---|---|---|---|
3D visualization | General 3D effect, GIS-T graphic display | There are many 3D visual modes, but the scene rendering effect is general, with GIS-T graphic display | Excellent 3D effect, GIS-T graphic display | No 3D effect, no GIS graphic display |
Road network structure | The “line-connection” structure description is adopted, and the road network layout is fine | Modular road network is adopted to build the road network, and IBEO scanning data can also be used to automatically convert it into a simulation scene. The road network layout is relatively fine | The “node-arc segment” structure is adopted to provide an accurate network collection editing function, and the network layout is relatively fine | The “node-arc segment” structure is used to describe the road network, and the road geometry is edited by defining the curvature. The road network layout is relatively fine |
Name | Vissim | Prescan | TransModeler | Corsim |
---|---|---|---|---|
Distance between front and rear vehicles when following | 2 m–8 m | 3 m–10 m | 2 m–8 m | 4 m |
Distance between front and rear vehicles when changing lanes | 40 m | 35 m | 40 m | 40 m |
Name | Vissim | Prescan | TransModeler | Corsim |
---|---|---|---|---|
0 s | 0 | 0 | 0 | 0 |
2 s | 1 | 1 | 2 | 0 |
5 s | 2 | 2 | 3 | 0 |
Name | Vissim | Prescan | TransModeler | Corsim |
---|---|---|---|---|
Number of vehicles queuing at the rear when leaving the station | 3 | 6 | 3 | 1 |
Implementation of bus priority strategy | Can be achieved, the process is simple, and the implementation effect is general | Not possible | Can be achieved, the process is simple, and the implementation effect is good | It can be realized, but the process is cumbersome and the amount of code is huge. Poor implementation effect |
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Ding, Z.; Xiang, J. Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving. World Electr. Veh. J. 2021, 12, 222. https://doi.org/10.3390/wevj12040222
Ding Z, Xiang J. Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving. World Electric Vehicle Journal. 2021; 12(4):222. https://doi.org/10.3390/wevj12040222
Chicago/Turabian StyleDing, Zirui, and Junping Xiang. 2021. "Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving" World Electric Vehicle Journal 12, no. 4: 222. https://doi.org/10.3390/wevj12040222