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  4. Vol 4, Issue 3, 2023

A Probabilistic Method for Behavior Prediction of Intelligent and Connected Vehicles in Freeway

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DOI: 10.23977/autml.2023.040303 | Downloads: 23 | Views: 304

Author(s)

Xiong Lu 1,2, Guan Yizhuo 1,2, Leng Bo 1,2, Li Zhuoren 1,2

Affiliation(s)

1 School of Automotive Studies, Tongji University, Shanghai, China
2 Intelligent Automotive Research Institute, New Clean Energy Automotive Engineering Centre, Tongji University, Shanghai, China

Corresponding Author

Leng Bo

ABSTRACT

The rapid developing technology of mobile communication and autonomous driving makes the intelligent and connected vehicle a new hotspot nowadays. How to improve the vehicle’s ability to understand the driving environment itself is an important issue in recent years. In this paper, a Bayesian prediction model based on human driving cognitive process model is proposed for freeways with special structures, which can inference the driving intention and predict the trajectory of the target vehicle. On the basis of considering the historical trajectory of the target vehicle, the motion states of the surrounding vehicles and values reflecting the characteristic of the road structure which are discretized by Chi-Merge algorithm improved the inference performance. The experimental results show that, compared with Naive Bayes Classifier, the Kalman Filter and the LSTM network, the accuracy of the maneuver reasoning results is significantly improved, and the RMSE value of the trajectory prediction results of the prediction model we propose is significantly reduced.

KEYWORDS

Behavior Prediction, Cognitional Process Model of Human Driver, Bayesian Network, Trajectory Generation, Chi-Merge Algorithm

CITE THIS PAPER

Xiong Lu, Guan Yizhuo, Leng Bo, Li Zhuoren, A Probabilistic Method for Behavior Prediction of Intelligent and Connected Vehicles in Freeway. Automation and Machine Learning (2023) Vol. 4: 17-28. DOI: http://dx.doi.org/10.23977/autml.2023.040303.

REFERENCES

[1] Li Keqiang, Dai Yifan, Li Shengbo, et al. (2017) State-of-the-art and technical trends of intelligent and connected vehicles. Journal of Automotive Safety and Energy, 8(01), 1-14.
[2] Lef`ever S, Vasquez D, Laugier C. (2014) A survey on motion prediction and risk assessment for intelligent vehicle. Robomech Journal, 1(1), 1-14.
[3] Jo K, Lee M, Kim J, et al. (2016) Tracking and behavior reasoning of moving vehicles based on roadway geometry constraints. IEEE Transactions on Intelligent Transportation Systems, 2016, 1-17.
[4] Ammoun S, Nashashibi F. (2009) Real time trajectory prediction for collision risk estimation between vehicles. IEEE International Conference on Intelligent Computer Communication & Processing, 2009, 417-422
[5] Hillenbrand J, Spieker A M, Kroschel K. (2006) A multilevel collision mitigation approach-its situation assessment, decision making, and performance tradeoffs. IEEE Transactions on Intelligent Transportation Systems, 7(4), 528-540.
[6] Gindele T, Brechtel S, Dillmann R. (2010) A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. 13th International IEEE Conference on Intelligent Transportation Systems, 2010, 1625-1631.
[7] Schreier M, Willert V, Adamy J. (2014) Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems. 2014 17th IEEE International Conference on Intelligent Transportation Systems, 2014, 334-341
[8] Zhang Jinhui, Li Keqiang, Luo Yugong, et al. (2019) Prediction of Preceding Car Motion Under Car-following Scenario in the Internet of Vehicle Based on Bayesian Network. Automotive Engineering, 41(03), 245-251+274.
[9] Qiu Xiaoping, Liu Yalong, Ma Lina, et al. (2015) A Lane Change Model Based on Bayesian Networks [J]. Journal of Transportation Systems Engineering and Information Technology, 15(05), 67-73+95.
[10] He Yanxia, Yin Huilin, Xia Pengfei. (2018) Decesion-making Mechanism of Autonomous Lane-change for Intelligent Vehicles Based on Environment Situation Assessment. Automotive Engineering, 40(09), 1048-1053.
[11] Xiao Xianqiang, Ren Chunyan, Wang Qidong. (2013) Research on Driving Behavior Prediction Method Based on HMM [J]. China Mechanical Engineering, 24(21), 2972-2976.
[12] He Ganglei, Li Xin, Lv Ying. (2019) Probabilistic intention prediction and trajectory generation based on dynamic bayesian networks. 2019 Chinese Automation Congress, 2019, 2646-2651.
[13] Su Shuang, Muelling K, Dolan J, et al. (2018) Learning vehicle surrounding-aware lane-changing behavior from observed trajectories. 2018 IEEE Intelligent Vehicles Symposium, 2018, 1412-1417.
[14] Nachiket D, Trivedi M M. (2018) Convolutional social pooling for vehicle trajectory prediction. 31th Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018, 1549-1557.
[15] Kerber R. (1992) Chi merge discretization of numeric attributes. Proceedings Tenth National Conference on Artificial Intelligence. AAAI, 1992, 123-128
[16] Wang Xuesong, Yang Minming. (2018) Cut-in Behavior Analyses Based on Naturalistic Driving Data. Journal of Tongji University (Natural Science), 46(08), 1057-1063.
[17] Zhang Yingda, Shao Fuchun, Li Huixuan, et al. (2015) Microscopic Characteristics of Lane-Change Maneuvers Based on NGSIM [J]. Journal of Transport Information and Safety, 33(06), 19-24+32.
[18] Ding Wenchao, Chen Jing, Shen Shaojie. (2019) Predicting vehicle behaviors over an extended horizon using behavior interaction network. 2019 International Conference on Robotics and Automation, 2019, 8634-8640.

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