Abstract
Autonomous driving systems aim to reduce traffic related accidents; hence, lane changing models need to be developed to accurately represent the driving behaviour during this complex manoeuvre. In this work, extensive pre-processing of real-world detailed vehicle trajectory information from the Next Generation Simulation (NGSIM) dataset was performed in order to derive a suitable feature space to be used as the input data to train and test a lane changing model. State-of-the-art algorithms use the relative velocities and gaps of vehicles in adjacent lanes to derive accurate prediction of the intention to move from one lane to another. Two new feature spaces were developed based on these data and four classification models were implemented by using Logistic Regression, Adaptive Boosting and Extreme Boosting algorithms obtaining accuracies up to 98%.
Supported by the Intelligent Systems PhD program at UDLAP.
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References
Sen, B., Smith, J.D., Najm, W.G.: Analysis of Lane Change Crashes. Technical report, U.S. Department of Transportation National Highway Traffic Safety Administration, DOT-NHTSA Cambridge, MA (2003). https://www.nhtsa.gov/sites/nhtsa.gov/files/doths809571.pdf
Ahmed, K.I.: Modeling drivers’ acceleration and lane changing behavior - PhD Thesis, Massachusetts Institute of Technology (1999). https://dspace.mit.edu/handle/1721.1/9662
Choudhury, C., Ben-Akiva, M., Toledo, T., Rao, A., Lee, G.: NGSIM Cooperative Lane Changing and Forced Merging Model. Technical report, US DOT Federal Highway Administration, Washington, DC (2006). https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj
Choudhury, C., Toledo, T., Ben-Akiva, M.: NGSIM Freeway Lane Selection Model. Technical report, US DOT Federal Highway Administration, Washington, DC, December 2004. https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj
Liu, Y., Wang, X., Li, L., Cheng, S., Chen, Z.: A novel lane change decision-making model of autonomous vehicle based on support vector machine. IEEE Access 7, 26543–26550 (2019). https://doi.org/10.1109/ACCESS.2019.2900416
Tang, J., Liu, F., Zhang, W., Ke, R., Zou, Y.: Lane-changes prediction based on adaptive fuzzy neural network. Expert Syst. Appl. 91, 452–463 (2018). https://doi.org/10.1016/j.eswa.2017.09.025
Zhang, Y., Lin, Q., Wang, J., Verwer, S., Dolan, J.: Lane-change intention estimation for car-following control in autonomous driving. IEEE Trans. Intell. Veh. (2018). https://doi.org/10.1109/TIV.2018.2843178
USDOT: U.S. Department of Transportation Federal Highway Administration. Next Generation Simulation (NGSIM) Vehicle Trajectories and Supporting Data. [Dataset]. Provided by ITS DataHub through Data.transportation.gov (2016). https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj
Ben-Akiva, M., et al.: Traffic simulation with MITSIMLab. In: Barceló, J. (eds.) Fundamentals of Traffic Simulation. International Series in Operations Research & Management Science, vol. 145, pp. 233–268. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6142-6_6
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning. STS, vol. 103. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-7138-7
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
XGBoost: XGBoost: Scalable and Flexible Gradient Boosting. https://xgboost.ai/
RDocumentation: adaboost function - RDocumentation. https://www.rdocumentation.org/packages/JOUSBoost/versions/2.1.0/topics/adaboost
Valencia-Rosado, L.O., Rojas-Velazquez, D., Etcheverry, G.: Driver intent data analysis and classification. In: 28th International Conference on Electronics, Communications and Computers, CONIELECOMP 2018, pp. 207–211, March 2018. https://doi.org/10.1109/CONIELECOMP.2018.8327200
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Martínez-Vera, E., Bañuelos-Sánchez, P., Etcheverry, G. (2022). Lane Changing Model from NGSIM Dataset. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_3
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