Lane-Changing Recognition of Urban Expressway Exit Using Natural Driving Data
Source abstract
Abstract
The traffic environment at the exit of the urban expressway is complex, and vehicle lane-changing behavior occurs frequently, making it prone to traffic conflict and congestion. To study the traffic conditions at the exit of the urban expressway and improve the road operation capacity, this paper analyzes the characteristics of lane-changing behaviors at the exit, adds driving style into the influencing factors of lane-changing, and recognizes one’s lane-changing intention based on driving data. A UAV (unmanned aerial vehicle) is used to collect the natural driving track data of the urban expressway diverge area, the track segments of vehicle lane-changing that meet the standards are extracted, and 374 lane-changing segments are obtained. K-means++ is used to cluster the driving style of the lane-changing segments which is grouped into three clusters, corresponding to “ordinary”, “radical”, and “conservative”. Through the random forest model used to identify and predict driving style, the accuracy reaches 93%. Considering the characteristics of a single time point and the characteristics of the historical time window, XGBoost, LightGBM, and the Stacking fusion model are established to recognize one’s lane-changing intention. The results show that the models can well recognize the lane-changing intention of drivers. The Stacking fusion model has the highest accuracy, while the LightGBM model takes less time; the model considering the characteristics of the historical time window performs better than the other one, which can better improve the prediction accuracy of lane-changing behavior.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Autonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Transportation Engineering, Chang’an University, The Middle Section of the Second Ring Road, Xi’an 710064, Shaanxi, China
Institute of Intelligent Traffic in Shanxi Province Co., Ltd., Taiyuan 30053, Shanxi, China
Funding
National Natural Science Foundation of China · Funds for Central Universities and Colleges of Chang’an University · Major scientific and technological innovation projects of Shandong Province · Xi’an scientific and technological projects
References (29)
Show source-provided references
1. National Highway Traffic Safety Administration (2017). Traffic Safety Facts 2015: A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System, National Highway Traffic Safety Administration.
2. A lane-changing risk profile analysis method based on time-series clustering10.1016/j.physa.2020.125567 ↗
3. A model for the structure of lane-changing decision10.1016/0191-2615(86)90012-3 ↗
4. Ahmed, K.I. (1999). Modeling Drivers’ Acceleration and Lane Changing Behavior. [Ph.D. Dissertation, Department of Civil and Environmental Engineering, MIT].
5. Modelling lane changing and merging in microscopic traffic simulation10.1016/S0968-090X(02)00026-8 ↗
6. A comparative analysis of currently used microscopic and macroscopic traffic simulation software
7. Zyner, A., Worrall, S., Ward, J., and Nebot, J. (2017, January 11–14). Long short term memory for driver intent prediction. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.10.1109/IVS.2017.7995919 ↗
8. Phillips, D.J., Wheeler, T.A., and Kochenderfer, M.J. (2017, January 11–14). Generalizable intention prediction of human drivers at intersections. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.10.1109/IVS.2017.7995948 ↗
9. Kim, I.H., Bong, J.H., Park, J., and Park, S. (2017). Prediction of driver’s intention of lane change by augmenting sensor information using machine learning techniques. Sensors, 17.10.3390/s17061350 ↗
10. Driver lane change intention recognition in the connected environment10.1016/j.physa.2021.126057 ↗
11. Lane changing intention recognition method based on an LSTM Network
12. A Framework for Turning Behavior Classification at Intersections Using 3D LIDAR10.1109/TVT.2019.2926787 ↗
13. Lane changing intention recognition based on speech recognition models10.1016/j.trc.2015.11.007 ↗
14. Modeling Mandatory Lane Changing Using Bayes Classifier and Decision Trees10.1109/TITS.2013.2285337 ↗
15. He, P., Wu, A., Huang, X., Rangarajan, A., and Ranka, S. (2022). Machine Learning-Based Highway Truck Commodity Classification Using Logo Data. Appl. Sci., 12.10.3390/app12042075 ↗
16. Sultonov, F., Park, J.H., Yun, S., Lim, D.-W., and Kang, J.-M. (2022). Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery. Appl. Sci., 12.10.3390/app12041953 ↗
17. Naturalistic driver intention and path prediction using recurrent neural networks10.1109/TITS.2019.2913166 ↗
18. Dou, Y., Yan, F., and Feng, D. (2016, January 12–15). Lane changing prediction at highway lane drops using support vector machine and artificial neural network classifiers. Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Banff, AB, Canada.10.1109/AIM.2016.7576883 ↗
19. Multi-parameter prediction of drivers’ lane-changing behaviour with neural network model10.1016/j.apergo.2015.03.017 ↗
20. Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps10.1016/j.physa.2020.125691 ↗
21. Mammeri, A., Zhao, Y., Boukerche, A., Siddiqui, A.J., and Pekilis, B. (2019, January 16–18). Design of a semi-supervised learning strategy based on convolutional neural network for vehicle maneuver classification. Proceedings of the 2019 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), Ottawa, ON, Canada.10.1109/WiSEE.2019.8920301 ↗
22. Decision Model of Vehicle Lane-changing Based on Bayesian Network
23. Driver Behavior Recognition Based on Support Vector Machine
24. Lane-change detection using a computational driver model10.1518/001872007X200157 ↗
25. Lane change intention awareness for assisted and automated driving on highways10.1109/TIV.2019.2904386 ↗
26. Ahmad, I., and Pothuganti, K. (2020, January 20–22). Design & implementation of real time autonomous car by using image processing & IoT. Proceedings of the 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.10.1109/ICSSIT48917.2020.9214125 ↗
27. Widaa, A.H.A., and Talha, W.A. (2017, January 16–18). Design of Fuzzy-based autonomous car control system. Proceedings of the 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), Khartoum, Sudan.10.1109/ICCCCEE.2017.7867640 ↗
28. Van dana Kaur, M. (2022, September 25). An Improved K-Means Based Text Document Clustering Using Artificial Bee Colony with Support Vector Machine. JETIR. Available online: www.jetir.org.
29. Kapoor, A., and Singhal, A. (2017, January 9–10). A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms. Proceedings of the 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, India.10.1109/CIACT.2017.7977272 ↗