Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment

Abstract

The decision-making under complex urban environment become one of the key issues that restricts the rapid development of the autonomous vehicles. The difficulty in making timely and accurate decisions like human beings under highly dynamic traffic environment is a major challenge for autonomous driving. Car-following has been regarded as the simplest but essential driving behavior among driving tasks and has received extensive attention from researchers around the world. This work addresses this problem and proposes a novel method RSAN (rough-set artificial neural network) to learn the decisions from excellent human drivers. A virtual urban traffic environment was built by PreScan and driving simulation was conducted to obtain a broad set of relevant data such as experienced drivers’ behavior data and surrounding vehicles’ motion data. Then, rough set was used to preprocess these data to extract the key influential factors on decision and reduce the impact of uncertain data and noise data. And the car-following decision was learned by neural network in which key factor was the input and acceleration was the output. The result shows the better convergence speed and the better decision accuracy of RSAN than ANN. Findings of this work contributes to the empirical understanding of driver’s decision-making process and it provides a theoretical basis for the study of car-following decision-making under complex and dynamic environment.

Keywords

autonomous vehicle / car-following / decision-making / rough set (RS) / artificial neural network (ANN) / PreScan

References

[1] GoodallN. Ethical decision making during automated vehicle crashes [J]. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2424: 58-65

[2] KoutsopoulosH N, FarahH. Latent class model for car following behavior [J]. Transportation Research Part B: Methodological, 2012, 46(5): 563-578

[3] ChandlerR E, HermanR, MontrollE W. Traffic dynamics: Studies in car following [J]. Operations Research, 1958, 6(2): 165-184

[4] MichaelsR M. Perceptual factors in car following [C]//. Proceedings of the 2nd International Symposium on the Theory of Road Traffic Flow, 1963

[5] HavlakF, CampbellM. Discrete and continuous, probabilistic anticipation for autonomous robots in urban environments [J]. IEEE Transactions on Robotics, 2014, 30(2): 461-474

[6] ChenX M, TiangG, ChanC Y, MiaoY S, GongJ W, JiangY. Bionic lane driving decision-making analysis for autonomous vehicle under complex urban environment [J]. Transportation Research Record: Journal of the Transportation Research Record, 2016, 2559: 120-130

[7] ChenX M, GaoL, WangX, WangZ H, ZhangZ H, LiaoZ G. The research on the data mining technology in the active demand management [C]//. Internet Computing & Information Services (ICICIS), 2011 International Conference on, 2011481484

[8] BaiT, MengH, YaoJ. A forecasting method of forest pests based on the rough set and PSO-BP neural network [J]. Neural Computing and Applications, 2014, 25(78): 1699-1707

[9] LiR, WangZ. Mining classification rules using rough sets and neural networks [J]. European Journal of Operational Research, 2004, 157(2): 439-448

[10] BazanJ G, SkowronA, SynakP. Dynamic reducts as a tool for extracting laws from decisions tables [C]//. International Symposium on Methodologies for Intelligent Systems, 1994, New York, Berlin, Heidelberg, Springer: 346355

[11] CravenM W, ShavlikJ W. Using neural networks for data mining [J]. Future Generation Computer Systems, 1997, 13(23): 211-229

[12] LuH, SetionoR, LiuH. Effective data mining using neural networks [J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 957-961

[13] KarA K. A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network [J]. Journal of Computational Science, 2015, 6: 23-33

[14] ThangavelK, PethalakshmiA. Dimensionality reduction based on rough set theory: A review [J]. Applied Soft Computing, 2009, 9(1): 1-12

[15] AffonsoC, SassiR J, BarreirosR M. Biological image classification using rough-fuzzy artificial neural network [J]. Expert Systems with Applications, 2015, 42(24): 9482-9488

[16] SwiniarskiR W, HargisL. Rough sets as a front end of neural-networks texture classifiers [J]. Neurocomputing, 2001, 36(1): 85-102

[17] AhnB S, ChoS S, KimC Y. The integrated methodology of rough set theory and artificial neural network for business failure prediction [J]. Expert Systems with Applications, 2000, 18(2): 65-74

[18] PreScan overview [EB/OL]. [2017-04-26]. https://www.tassinternational.com/prescan-overview.

[19] JiangF, SuiY. A novel approach for discretization of continuous attributes in rough set theory [J]. Knowledge-Based Systems, 2015, 73: 324-334