A Real-Time Warning System for Rear-End Collision Based on Random Forest Classifier

Document Type : Regular Article


Department of Computer Science, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran


Rear-end collision warning system has a great role to enhance driving safety. In this system, some measures are used to evaluate the safety and in the case of dangerous, the system warns drivers. This system should be executed in real-time, to remain enough time to avoid collision with the front vehicle. To this end, in this paper, a new system is developed by using a random forest classifier to extract knowledge about warning and safe situations. This knowledge can be extracted from accidents and vehicle trajectory data. Since the data of these situations are imbalanced, a combination of cost-sensitive learning and classification methods was used to improve the sensitivity, specificity, and processing time of classification. To evaluate the performance of this system, vehicle-trajectory-data of 100 cars that have been provided by Virginia tech transportation institute, are used. The comparison results are given in terms of accuracy and processing time. By using TOPSIS multi-criteria selection method, it is shown that the implemented classifier is better than different classifiers including Bayesian network, Naive Bayes, MLP neural network, support vector machine, k-nearest neighbor, rule-based methods and decision tree. The implemented random forest gets 88.4% accuracy for detection of the dangerous situations and 94.7% for detection of the safe situations. Also, the proposed system is more robust compared with the perceptual-based and kinematic-based algorithms.


Google Scholar


Main Subjects

[1]     X. Yan, E. Radwan, and M. Abdel-Aty, “Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model,” Accident Analysis & Prevention, vol. 37, no. 6, pp. 983–995, 2005.
[2]     J. Liang, L. Chen, X. Cheng, and X. Chen, “Multi-agent and driving behavior based rear-end collision alarm modeling and simulating,” Simulation Modelling Practice and Theory, vol. 18, no. 8, pp. 1092–1103, 2010.
[3]     A. Jarašūniene and G. Jakubauskas, “Improvement of road safety using passive and active intelligent vehicle safety systems,” Transport, vol. 22, no. 4, pp. 284–289, 2007.
[4]     O. Gietelink, J. Ploeg, B. De Schutter, and M. Verhaegen, “Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations,” Vehicle System Dynamics, vol. 44, no. 7, pp. 569–590, 2006.
[5]     N. H. T. S. Administration, “Traffic safety facts, 2012 data: Pedestrians,” Annals of Emergency Medicine, vol. 65, no. 4, p. 452, 2015.
[6]     J. D. Lee, D. V. McGehee, T. L. Brown, and M. L. Reyes, “Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator,” Human factors, vol. 44, no. 2, pp. 314–334, 2002.
[7]     X. Zhao, S. Jing, F. Hui, R. Liu, and A. J. Khattak, “DSRC-based rear-end collision warning system–An error-component safety distance model and field test,” Transportation Research Part C: Emerging Technologies, vol. 107, pp. 92–104, 2019.
[8]     Y. Fu, C. Li, T. H. Luan, Y. Zhang, and F. R. Yu, “Graded Warning for Rear-End Collision: An Artificial Intelligence-Aided Algorithm,” IEEE Transactions On Intelligent Transportation Systems, 2019.
[9]     T. Chen, K. Liu, Z. Wang, G. Deng, and B. Chen, “Vehicle forward collision warning algorithm based on road friction,” Transportation research part D: transport and environment, vol. 66, pp. 49–57, 2019.
[10]    Y. Xiang, S. Huang, M. Li, J. Li, and W. Wang, “Rear-End Collision Avoidance-Based on Multi-Channel Detection,” IEEE Transactions on Intelligent Transportation Systems, 2019.
[11]    D. V. McGehee, T. L. Brown, T. B. Wilson, and M. Burns, “Examination of drivers’ collision avoidance behavior in a lead vehicle stopped scenario using a front-to-rear-end collision warning system,” Contract DTNH22-93-C-07326) Washington, DC: National Highway Traffic safety Administration, 1998.
[12]    P. Seiler, B. Song, and J. K. Hedrick, “Development of a collision avoidance system,” SAE transactions, pp. 1334–1340, 1998.
[13]    T. L. Brown, J. D. Lee, and D. V. McGehee, “Human performance models and rear-end collision avoidance algorithms,” Human Factors, vol. 43, no. 3, pp. 462–482, 2001.
[14]    T.-H. Chang, C.-S. Hsu, C. Wang, and L.-K. Yang, “Onboard measurement and warning module for irregular vehicle behavior,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 501–513, 2008.
[15]    F. Bella and R. Russo, “A collision warning system for rear-end collision: a driving simulator study,” Procedia-social and behavioral sciences, vol. 20, pp. 676–686, 2011.
[16]    F. Benedetto, A. Calvi, F. D’Amico, and G. Giunta, “Applying telecommunications methodology to road safety for rear-end collision avoidance,” Transportation research part C: emerging technologies, vol. 50, pp. 150–159, 2015.
[17]    K. Vogel, “A comparison of headway and time to collision as safety indicators,” Accident analysis & prevention, vol. 35, no. 3, pp. 427–433, 2003.
[18]    J. E. Naranjo, C. González, R. García, and T. De Pedro, “Cooperative Throttle and Brake Fuzzy Control for ACC $+ $ Stop&Go Maneuvers,” IEEE Transactions on Vehicular Technology, vol. 56, no. 4, pp. 1623–1630, 2007.
[19]    P. Fancher, Z. Bareket, and R. Ervin, “Human-centered design of an ACC-with-braking and forward-crash-warning system,” Vehicle System Dynamics, vol. 36, no. 2–3, pp. 203–223, 2001.
[20]    Y. Fujita, K. Akuzawa, and M. Sato, “Radar brake system,” Jsae Review, vol. 1, no. 16, p. 113, 1995.
[21]    Y. I. Noy, Ergonomics and safety of intelligent driver interfaces. CRC Press, 1997.
[22]    S.-Y. Kim et al., “An intelligent and integrated driver assistance system for increased safety and convenience based on all-around sensing,” Journal of intelligent and robotic systems, vol. 51, no. 3, pp. 261–287, 2008.
[23]    E. Dagan, O. Mano, G. P. Stein, and A. Shashua, “Forward collision warning with a single camera,” in IEEE Intelligent Vehicles Symposium, 2004, 2004, pp. 37–42.
[24]    K. D. Kusano and H. C. Gabler, “Safety benefits of forward collision warning, brake assist, and autonomous braking systems in rear-end collisions,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1546–1555, 2012.
[25]    C. Oh and T. Kim, “Estimation of rear-end crash potential using vehicle trajectory data,” Accident Analysis & Prevention, vol. 42, no. 6, pp. 1888–1893, 2010.
[26]    S. Moon, I. Moon, and K. Yi, “Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance,” Control Engineering Practice, vol. 17, no. 4, pp. 442–455, 2009.
[27]    V. Milanés, J. Pérez, J. Godoy, and E. Onieva, “A fuzzy aid rear-end collision warning/avoidance system,” Expert Systems with Applications, vol. 39, no. 10, pp. 9097–9107, 2012.
[28]    A. H. Jamson, F. C. Lai, and O. M. Carsten, “Potential benefits of an adaptive forward collision warning system,” Transportation research part C: emerging technologies, vol. 16, no. 4, pp. 471–484, 2008.
[29]    O. Ararat, E. Kural, and B. A. Guvenc, “Development of a collision warning system for adaptive cruise control vehicles using a comparison analysis of recent algorithms,” in 2006 IEEE Intelligent Vehicles Symposium, 2006, pp. 194–199.
[30]    J.-F. Liu, Y.-F. Su, M.-K. Ko, and P.-N. Yu, “Development of a vision-based driver assistance system with lane departure warning and forward collision warning functions,” in 2008 Digital Image Computing: Techniques and Applications, 2008, pp. 480–485.
[31]    M. Akhlaq, T. R. Sheltami, B. Helgeson, and E. M. Shakshuki, “Designing an integrated driver assistance system using image sensors,” Journal of Intelligent Manufacturing, vol. 23, no. 6, pp. 2109–2132, 2012.
[32]    C. Oh, S. Park, and S. G. Ritchie, “A method for identifying rear-end collision risks using inductive loop detectors,” Accident Analysis & Prevention, vol. 38, no. 2, pp. 295–301, 2006.
[33]    S. González, S. García, S.-T. Li, and F. Herrera, “Chain based sampling for monotonic imbalanced classification,” Information Sciences, vol. 474, pp. 187–204, 2019.
[34]    X. Jia, W. Li, and L. Shang, “A multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model,” Information Sciences, vol. 485, pp. 248–262, 2019.
[35]    F. Min, F.-L. Liu, L.-Y. Wen, and Z.-H. Zhang, “Tri-partition cost-sensitive active learning through kNN,” Soft Computing, vol. 23, no. 5, pp. 1557–1572, 2019.
[36]    J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
[37]    K. Custer, “100-Car Data.” VTTI, 15-Oct-2019, doi: 10.15787/VTT1/CEU6RB.
[38]    N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study,” Intelligent data analysis, vol. 6, no. 5, pp. 429–449, 2002.
[39]    C. X. Ling and V. S. Sheng, Class Imbalance Problem. 2010.
[40]    V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 93–104, 2012.
[41]    R. Dutta, T. G. Rao, and A. Sharma, “Application of random forest regression in the Prediction of ultimate bearing capacity of strip footing resting on dense sand overlying loose sand deposit,” Journal of Soft Computing in Civil Engineering, vol. 3, no. 4, pp. 28–40, 2019.
[42]    N. Dogru and A. Subasi, “Traffic accident detection using random forest classifier,” in 2018 15th learning and technology conference (L&T), 2018, pp. 40–45.
[43]    M. Hossain and Y. Muromachi, “A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways,” Accident Analysis & Prevention, vol. 45, pp. 373–381, 2012.
[44]    D. M. Farid, L. Zhang, C. M. Rahman, M. A. Hossain, and R. Strachan, “Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks,” Expert systems with applications, vol. 41, no. 4, pp. 1937–1946, 2014.
[45]    A. Wefky, F. Espinosa, A. Prieto, J. J. Garcia, and C. Barrios, “Comparison of neural classifiers for vehicles gear estimation,” Applied Soft Computing, vol. 11, no. 4, pp. 3580–3599, 2011.
[46]    V. Chandanshive and A. R. Kambekar, “Estimation of building construction cost using artificial neural networks,” Journal of Soft Computing in Civil Engineering, vol. 3, no. 1, pp. 91–107, 2019.
[47]    L. J. Cao et al., “Parallel sequential minimal optimization for the training of support vector machines,” IEEE Trans. Neural Networks, vol. 17, no. 4, pp. 1039–1049, 2006.
[48]    M. Aci, C. İnan, and M. Avci, “A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm,” Expert Systems with Applications, vol. 37, no. 7, pp. 5061–5067, 2010.
[49]    W. W. Cohen, “Fast effective rule induction,” in Machine learning proceedings 1995, Elsevier, 1995, pp. 115–123.
[50]    J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.
[51]    S. Ruggieri, “Efficient C4. 5 [classification algorithm],” IEEE transactions on knowledge and data engineering, vol. 14, no. 2, pp. 438–444, 2002.
[52]    T. A. Dingus et al., “The 100-car naturalistic driving study, Phase II-results of the 100-car field experiment,” United States. Department of Transportation. National Highway Traffic Safety …, 2006.
[53]    G.-H. Tzeng and J.-J. Huang, Multiple attribute decision making: methods and applications. CRC press, 2011.
[54]    K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural networks, vol. 4, no. 2, pp. 251–257, 1991.
[55]    J. H. Hogema and W. H. Janssen, “Effects of intelligent cruise control on driving behaviour: a simulator study,” TNO, 1996.
[56]    R. V. D. Horst, “Time-to-collision as a cue for decision-making in braking,” Vision in Vehicles–III, 1991.
[57]    M. Saffarzadeh, N. Nadimi, S. Naseralavi, and A. R. Mamdoohi, “A general formulation for time-to-collision safety indicator,” in Proceedings of the Institution of Civil Engineers-Transport, 2013, vol. 166, no. 5, pp. 294–304.
[58]    T.-W. Lin, S.-L. Hwang, and P. A. Green, “Effects of time-gap settings of adaptive cruise control (ACC) on driving performance and subjective acceptance in a bus driving simulator,” Safety science, vol. 47, no. 5, pp. 620–625, 2009.
[59]    J. Trnros, L. Nilsson, J. Ostlund, and A. Kircher, “Effects of ACC on driver behaviour, workload and acceptance in relation to minimum time headway,” in 9th World Congress on Intelligent Transport SystemsITS America, ITS Japan, ERTICO (Intelligent Transport Systems and Services-Europe), 2002.
[60]    P. Zheng and M. McDonald, “Manual vs. adaptive cruise control–Can driver’s expectation be matched?,” Transportation Research Part C: Emerging Technologies, vol. 13, no. 5–6, pp. 421–431, 2005.
[61]    P. Fancher, “Intelligent cruise control field operational test. Final report. Volume II: appendices A-F,” 1998.
[62]    G. Reichart, R. Haller, and K. Naab, “Driver assistance: BMW solutions for the future of individual mobility,” in Intelligent Transportation: Realizing the Future. Abstracts of the Third World Congress on Intelligent Transport SystemsITS America, 1996.
[63]    A. M. Noori, R. Mikaeil, M. Mokhtarian, S. S. Haghshenas, and M. Foroughi, “Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM,” Geotechnical and Geological Engineering, pp. 1–19, 2020.