Traffic Flow Modeling and Simulation using Micro Intelligent Vehicles

Review Article

Austin J Robot & Autom. 2014;1(1): 6.

Traffic Flow Modeling and Simulation using Micro Intelligent Vehicles

Lindong G1, Zhengchen L1, Ming Y1* and Chunxiang W2

1Department of Automation, Shanghai Jiao Tong University, China

2Research Institute of Robotics, Shanghai Jiao Tong University, China

*Corresponding author: Ming Y, Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

Received: October 29, 2014; Accepted: November 28, 2014; Published: December 02, 2014


Traffic flow experiments using Micro Intelligent Vehicle (MicroIV) are more efficient and easier to achieve than experiments in actual traffic environment. In this paper, a MicroIV based traffic flow modeling and simulation method is proposed. Firstly, the framework of MicroIV is presented aiming at satisfying the requirements of multi-task and potential extended functions in traffic flow simulation. Secondly, a global path planning approach using time division based hierarchical topology maps, which takes specific traffic rules into consideration, is developed to overcome the difficulties of navigation in micro scale and using hierarchical topology maps for global path planning. Thirdly, in the part of environmental perception, an active vision approach based on improved visual selection attention is proposed to realize more efficient and robust detection of traffic light, and an improved spatial-bag-of-features method is used to make traffic sign recognition more adaptable. Experimental results show that the MicroIV is proved to be suitable for traffic flow simulation.

Keywords: Micro Intelligent Vehicle; Traffic flow simulation; Environmental perception


The significance of autonomous driving experiments as well as traffic flow experiments in actual traffic environment have caused widespread concern in recent years [1-3]. With the consideration of many other factors like weather, local laws and safety, however, the implementation of such experiments would lead to a long development period and high research cost. Furthermore, it is unrealistic for multi-vehicle collaboration researches which need multiple intelligent vehicles to participate in. If the experiment could be implemented in a micro scale as those researches which use micro scale models instead of actual objects, these difficulties would be overcame. Thus, a Micro Intelligent Vehicle (MicroIV) used for traffic flow simulation and intelligent vehicle system development is proposed based on this idea. Meanwhile, the experimental platform of micro traffic environment would be carried out indoor. With analysis of urban traffic in detail, micro urban traffic environments is established including different kinds of complex traffic scenes, in which various styles of lanes and intersections are involved. It is more convenient for intelligent traffic system and cooperative vehicle infrastructure system to be tested in such platform where wireless network communications and other measurement techniques are more easily to be installed.

Firstly, as a primary cause of using MicroIV for traffic flow simulation, the hardware configuration must be restricted to limit the cost. As a result of that, advanced sensors like laser scanners, which are standard configuration on a real intelligent vehicle, are not suitable to be deployed on the MicroIV. And in consideration of the size of MicroIV, there is no enough space for a high performance computing platform. But the expected functions of MicroIV must be integrated enough to accomplish the tasks of intelligent vehicles in real traffic situation such as autonomous driving, collision avoidance and traffic light recognition. So a reasonable and reliable hardware configuration is needed. Also the software architecture should be designed as efficiently as possible to optimize computing resources and take the full advantages of the visual sensors.

Secondly, there are still quite a number of difficulties in the navigation and global path planning in traffic flow simulation using Micro Intelligent Vehicles when taken traffic rules into consideration. On one hand, MicroIV could not get a global position through GPS devices because of the application scenario as well as the problem of accuracy. On the other hand, with the complex traffic rules of local intersections and lanes taken into consideration to evaluate the different traffic resource configuration, the micro traffic environment will result in a complex dynamic topology structure which will be quite difficult for global path planning. In order to overcome these problems, Time Division Based Hierarchical Topology Maps are defined in this paper to help locate the position without global position information. Furthermore, a global path planning method is developed on the basis of these maps to acquire a permitted optimal global path for traffic flow simulation that contains time-dependent traffic rules.

Thirdly, traffic light and traffic sign recognition is also one of the key issues among intelligent vehicle researches. Numbers of approaches proposed on traffic sign recognition focused on passive vision model [4-7], which could achieve a good result in local region of image, but difficult to search the relevant ROI rapidly because of lacking an analysis of global statistics of image. But it is obviously that human visual perception can locate ROI rapidly through active vision, and a series of the approximate computation models of active vision have been presented in [8-12]. Among these approaches, an active vision approach called VSA (visual selective attention) proposed in [8-10] could satisfy the real-time and robust requirements of MicroIV. In this paper, a method based on improved selective visual attention approach is employed to optimize the location of ROI which can reduce the computation cost of the detection of traffic sign and traffic light greatly. Then the recognition methods only need to be applied on certain ROI. An improved spatial-bag-of-feature is also proposed to make traffic sign recognition more efficient and adaptable. So traffic sign recognition method employed on MicroIV will be introduced as a whole.

The following paper is organized as followed. The system framework with both hardware and software will be proposed in section II. The time division based hierarchical topology maps for global path planning will be discussed in section III. And the environmental perception approach will be introduced in section IV. Experimental results and conclusions are given in section V and section VI.


The implementation of MicroIV is still complicated because of the following reasons. MicroIV should be competent for enough functions as same as real vehicles have, and traffic flow simulation needs such a number of vehicles driving in the micro traffic environment that the stability and cost-effective should be ensured. As a consequence of these facts, a robust and strong framework is necessary for both hardware and software.

Hardware framework

The hardware configuration of MicroIV includes two layers of architecture. The first layer plays a role as a vehicle which is able to realize the steering and speed control according to the instructions it has received. Modules of the first layer includes: engine and steering driver module, speed feedback module, communication module, infrared sensors module, human machine interaction module and power supply module. All these modules are connected and controlled by a micro controller. Relatively, the second layer plays a role as a driver that can give steering and speed instruction based on the vision sensors. The hardware of the second layer is mainly a single-board computer connected with web cameras. The communication between the first layer and the second layer is realized through serial port (Figure 1).