
A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain
Deyi Li, Hongbo Gao
Engineering ›› 2018, Vol. 4 ›› Issue (4) : 464-470.
A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain
The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules. The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information. The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard. Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the output result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed. The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architecture, which makes the software architecture portable on different intelligent driving hardware platforms.
Driving brain / Intelligent driving / Hardware platform framework
[1] |
Gage D.W.. UGV History 101: a brief history of unmanned ground vehicle (UGV) development efforts. Unmanned Syst Mag. 1970; 13(3): 9-32.
|
[2] |
Luo X., Deng J., Wang W.P., Wang J.H., Zhao W.B.. A quantized kernel learning algorithm using a minimum kernel risk-sensitive loss criterion and bilateral gradient technique. Entropy. 2017; 19(7): 365.
|
[3] |
Kanade T., Thorpe C.. CMU strategic computing vision project report: 1984 to 1985.
|
[4] |
Williams M.. PROMETHEUS-the European research programme for optimising the road transport system in Europe. In: Proceedings of IEE Colloquium International Conference on Driver Information; 1988 Dec 1, London, UK. 1988. p. 1-9.
|
[5] |
Wang S.L., Zhao Y.P., Shu Y., Yuan H.N., Geng J., Wang S.P.. Fast search local extremum for maximal information coefficient (MIC). J Comput Appl Math. 2018; 327: 372-387.
|
[6] |
Tsugawa S., Aoki M., Hosaka A., Seki K.. A survey of present IVHS activities in Japan. Control Eng Pract. 1997; 5(11): 1591-1597.
|
[7] |
Luo X., Zhang D.D., Yang L.T., Liu J., Chang X.H., Ning H.S.. A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems. Future Gener Comput Syst. 2016; 61: 85-96.
|
[8] |
Gao H.B., Cheng B., Wang J.Q., Li K.Q., Zhao J.H., Li D.Y.. Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Trans Ind Inform. 2018; 99: 1.
|
[9] |
Gao H.B., Zhang X.Y., Zhang T.L., Liu Y.C., Li D.Y.. Research of intelligent vehicle variable granularity evaluation based on cloud model. Acta Electron Sin. 2016; 44(2): 365-373.
|
[10] |
Bertozzi M., Broggi A., Fascioli A.. VisLab and the evolution of vision-based UGVs. Comput. 2006; 39(12): 31-38.
|
[11] |
Kolski S., Ferguson D., Bellino M., Siegwart R.. Autonomous driving in structured and unstructured environments. In: Proceedings of 2006 IEEE Intelligent Vehicles Symposium; 2006 Jun 13–15; Tokyo, Japan. 2006. p. 558-563.
|
[12] |
Yuan H.N., Wang S.L., Geng J., Yu Y., Zhong M.. Robust clustering with distance and density. Int J Data Wareh Min. 2017; 13(2): 63-74.
|
[13] |
Guizzo E. How Google’s self-driving car works [Internet]. New York: IEEE Spectrum; c2018 [updated 2011 Oct 18; cited 2017 Jul 30]. Available from: https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works.
|
[14] |
Luo X., Luo H., Chang X.H.. Online optimization of collaborative web service QoS prediction based on approximate dynamic programming. Int J Distrib Sens Netw. 2015; 11(8): 4524921.
|
[15] |
Bayerl S.F.X., Luettel T., Wuensche H.J.. Following dirt roads at night-time: sensors and features for lane recognition and tracking.
|
[16] |
Luo X., Deng J., Liu J., Wang W., Ban X., Wang J.H.. A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis. China Commun. 2017; 14(7): 127-136.
|
[17] |
Dissanayake M.W.M.G., Newman P., Clark S., Durrant-Whyte H.F., Csorba M.. A solution to the simultaneous localization and map building problem. IEEE Trans Robot Autom. 2001; 17(3): 229-241.
|
[18] |
Zou R., Wang M., Wang S.L., Li S., Zhang C., Deng L.,
|
[19] |
Zhang X.Y., Gao H.B., Guo M., Li G.P., Liu Y.C., Li D.Y.. A study on key technologies of unmanned driving. CAAI Trans Intell Technol. 2016; 1(1): 4-13.
|
[20] |
Luo X., Liu J., Zhang D.D., Chang X.. A large-scale web QoS prediction scheme for the industrial internet of things based on a kernel machine learning algorithm. Comput Netw. 2016; 101: 81-89.
|
[21] |
Su M.H.. BYD SuRei 2013 china intelligent vehicle future challenge. Consum Guide. 2013; 2013(11): 76. Chinese
|
[22] |
Yu Z.X., Xue Y.F., Zhu Y., Sun X.S.. Army Military Transportation University intelligent vehicle team won the first two places in the Sixth China Intelligent Vehicle Future Challenge. Auto Appl. 2015; 1: F0002. Chinese
|
[23] |
Tang C.J.. Unmanned bus made in Henan is under road test in an endless stream on Zhengkai expressway. Aug 31;Sect. A09. Chinese
|
[24] |
Ma M., Wang L., Zhang K.. Whether 3D printing can change the manufacturing industry or not—interview with Academician Bingheng Lu of Chinese Academy of Engineering. High Technol Ind. 2013; 9(4): 38-43. Chinese
|
This work was supported by China Postdoctoral Science Foundation Special Funded Projects (2018T110095), project funded by China Postdoctoral Science Foundation (2017M620765), National Key Research and Development Program of China (2017YFB0102603), and Junior Fellowships for Advanced Innovation Think-tank Program of China Association for Science and Technology (DXB-ZKQN-2017-035).
Deyi Li and Hongbo Gao declare that they have no conflict of interest or financial conflicts to disclose.
/
〈 |
|
〉 |