A Brief Introduction of CAVTest

已有 1591 次阅读 2018-9-8 20:20 |系统分类:科研笔记

 A Brief Introduction of CAVTest

     Chang'an University Automated Vehicle Test Field (CAVTest) is based on the Automotive Comprehensive Performance Test Field in Weishui campus of Chang’an University, which is the unique A level Test Field in Chinese Colleges and Universities. Moreover, CAVTest is improved to be electronic, information-based and intelligent one. 

    In 2010, the vice-president of Chang’an University, Professor Xiangmo Zhao, has proposed an idea with great vision to build a Vehicle-Infrastructure Cooperative Test Field. He hopes that the Test Field is going to be an important platform for education and research, which has an advanced conception, multiple functions, high intelligence and high influence in the intelligent transportation field of the world. In recent years, Professor Xiangmo Zhao and his research team aimed at key issues in the intelligent transportation field and addressed technical difficulties in the Vehicle-Infrastructure Cooperative System by using modularized solutions. In 2018, CAVTest is certificated by China's Ministry of Transport as one of 3 government designated close-course self-driving test bases. (

    Currently, the prototype of the Test Field has been built and a serial of achievements have been obtained.CAVTest includes a 2.4 km long, high-speed circular test road with 2 lanes and an extra 1.1 km long, straight 4-lane test track, with 4 kinds of pavements (asphalt, concrete, bricks, and dirt). It is a comprehensive and closed environment for testing various connected and automated vehicle (CAV) applications.

    An automated vehicle testing track (AVTT) is built on CAVTest, which was designed to provide a 1 km long track, with nine typical driving scenarios (see Table 1), which were marked “S1” to “S9”, as shown in Fig. 3. S1 (pedestrian collision avoiding) and S9 (going through a snake-shape barrier) are illustrated in Fig. 4.


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Selected Publications on CAVTest


Li, X., Xu, Z., Wang, R., Min, H., & Zhao, X. (2016). CU-CVIS test bed: a test bed of cooperative vehicle-infrastructure system in chang’an university. DEStech Transactions on Engineering and Technology Research, (ictim). 

Xu, Z., Zhang, K., Min, H., Wang, Z., Zhao, X., & Liu, P*. (2018). What drives people to accept autonomous vehicles? Findings from a field experiment. Transportation research-Part C, Emerging Technology,95(10),2018,320-334.

X. Zhao, P. Sun, Z. Xu*, H. Min, Z. Wang, and Y. An, (2018). “A Fast and Robust Object Detection and Identification Method by Fusing 3D LIDAR and Camera Sensors,” IEEE Trans. Intell. Tranportation Syst.(Accepted)

X. Zhao, X. Li, Z. Xu*, & T. Chen.(2018). “An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance,” IEEE Intell. Trasnsportation Syst. Magzine, no. C, pp. 1–12, 2018. 

R. Wang,Z. Xu*,X. Zhao,and J. Hu,A V2V-based Method for The Detection of Road Traffic Congestion,IET Intelligent Transportation Systems,(Accepted)

X. Zhao, H. Min, Z. Xu*, and W. Wang.(2018). “A New Video-based Vehicle Ego-positioning Method and Its Application on Indoor Parking Guidance,” Transp. Res. Part C Emerg. Technol., pp. 1–23. (Under Review)

Xu, Z., Wei, T., Easa, S., Zhao, X., & Qu, X*. (2018). Modelling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles. International Journal of Computer-Aid Civil and Infrastructure Engineering, 2018,1,1–15.

Xu, Z., Li, X., Zhao, X., Zhang, M. H., & Wang, Z. (2017). DSRC versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance. Journal of Advanced Transportation, 2017,1-10.

Xu, Z., Wang, M., Zhang, F., Jin, S., Zhang, J., & Zhao, X. (2017). PaTAVTT: A Hardware-in-the-loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking. Journal of Advanced Transportation, 2017, 1–11.

Zhao, X., Xu, Z*., Song, H., Zhao, Z., & Wang, W. (2012). Modeling and Development of Novel Giant Magnetostrictive Transducer for Large Block Concrete Testing. International Journal on Information, 15(6), 2613–2622.

Zhao, X., Min, H., Xu, Z*., Li, X., & Sun, P. (2018). Image Anti-blurring and Statistic Filter of Feature Space Displacement: Application to Visual Odometry for Outdoor Ground Vehicle. Journal of Sensors,Volume 2018,1-14.

Min, H., Zhao, X., Xu, Z., & Zhang, L. (2017). Robust Features and Accurate Inliers Detection Framework: Application to Stereo Ego-motion Estimation. KSII Transactions on Internet & Information Systems. 11(1), 302–320.



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