Abstract
Smart vehicles, capable of exchanging necessary information with each other and transportation infrastructure, have gained much attraction in automotive research. The data are gathered from location, velocity, acceleration and heading measurements, allowing vehicles to make smart decisions regarding safety and other applications. Specifically, accurate prediction of vehicle location measurement is considered crucial for making prompt decisions in emergency situations. These connected vehicles, equipped with advanced technologies, tend to improve driver safety and mobility radically. Still, most of the current vehicular safety applications rely on sensor measurements and uncertainty associated with them. In this paper, we have calculated the uncertainty of measurement for the deep learning-based long short-term memory model developed to estimate future location for smart vehicles. The prediction is effectively performed by exploiting the data retrieved from the past trajectory of the vehicle. Most of the available models, designed to predict a vehicle's location, do not provide any information about uncertainty in their measurements. This research aims to evaluate the uncertainty of measurement in prediction error and validation loss related to location prediction, enabling the system to make reliable decisions in the context of safety applications.
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References
S. Hochreiter and J. J. Urgen Schmidhuber, “Long short-term memory,” 1997. Accessed: Feb. 06, 2021. [Online]. Available: https://www.idsia.ch/juergen.
S. Ammoun and F. Nashashibi, “Real time trajectory prediction for collision risk estimation between vehicles.” Accessed: Feb. 06, 2021. [Online]. Available: https://hal.inria.fr/inria-00438624.
C. Barrios and Y. Motai, Improving estimation of vehicle’s trajectory using the latest global positioning system with Kalman filtering. IEEE Trans. Instrum. Meas., 60 (2011) 3747–3755. https://doi.org/10.1109/TIM.2011.2147670.
Q. Tran, J. F.-2014 I. I. V. Symposium, and undefined 2014, “Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression,” ieeexplore.ieee.org, Accessed: Feb. 06, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6856480/.
C. Laugier, I. Paromtchik, M. P.-I. I., and undefined 2011, “Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety,” ieeexplore.ieee.org, Accessed: Feb. 06, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6025208/?casa_token=11zzw3iqfI8AAAAA:hwpgAnxaB4wO7_sGZhWczBdEf-poTpvp1p09SMfgr1ozNaotm825_pMZ4YwX75faPUsMgWy9Gf_I.
J. Wiest, M. Höffken, U. K.-2012 I. I., and undefined 2012, “Probabilistic trajectory prediction with Gaussian mixture models,” ieeexplore.ieee.org, Accessed: Feb. 06, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6232277/.
T. Gindele, S. Brechtel, R. D.-I. Intelligent, and undefined 2015, “Learning driver behavior models from traffic observations for decision making and planning,” ieeexplore.ieee.org, Accessed: Feb. 06, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7014400/?casa_token=fe6BtHKbmxQAAAAA:3g0Ej_LHydfPkVN8bjQBFRohdaJUfbhHj_Gwe7KCzDlzfejhFoZGwwTzEgyYpBG8rL7IL8EBOrH_Tg.
Z. C. Lipton, J. Berkowitz, and C. Elkan, “A Critical Review of Recurrent Neural Networks for Sequence Learning,” May 2015, Accessed: Feb. 06, 2021. [Online]. Available: http://arxiv.org/abs/1506.00019.
B. Kim, C. Kang, J. Kim, S. L.-2017 I. 20th, and undefined 2017, “Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network,” ieeexplore.ieee.org, Accessed: Feb. 06, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8317943/?casa_token=pR_kwehpTnQAAAAA:krJHEWN7sdyDzyZuJQuLJhKDxfkBXMvORnW4Qobn6m6oSHmzvYgOekwOxrI3qoHkl5RL7dUK6C1X.
S. H. Park, B. Kim, C. M. Kang, C. C. Chung, and J. W. Choi, “Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture,” IEEE Intell. Veh. Symp. Proc., vol. 2018-June, pp. 1672–1678, 2018, https://doi.org/10.1109/IVS.2018.8500658.
C. Fu et al., “Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application,” Front. Neurorobotics www.frontiersin.org, vol. 1, p. 12, 2019, doi: https://doi.org/10.3389/fnbot.2019.00012.
S. Jawed, E. Boumaiza, J. Grabocka, and L. Schmidt-Thieme, “Data-Driven Vehicle Trajectory Forecasting.” Accessed: Feb. 06, 2021. [Online]. Available: https://arxiv.org/abs/1902.05400.
N. Nikhil and B. T. Morris, “Convolutional Neural Network for Trajectory Prediction.” Accessed: Feb. 06, 2021. [Online]. Available: https://openaccess.thecvf.com/content_eccv_2018_workshops/w15/html/Nikhil_Convolutional_Neural_Network_for_Trajectory_Prediction_ECCVW_2018_paper.html.
P. Rathore, D. Kumar, S. Rajasegarar, M. Palaniswami and J.C. Bezdek, A Scalable Framework for Trajectory Prediction. IEEE Trans. Intell. Transp. Syst. (2019). https://doi.org/10.1109/TITS.2019.2899179.
P. Zhang, W. Ouyang, P. Zhang, J. Xue, and N. Zheng, “SR-LSTM: State refinement for lstm towards pedestrian trajectory prediction,” 2019, https://doi.org/10.1109/CVPR.2019.01236.
L.-M. Kieu, N. Malleson and A. Heppenstall, Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions. R. Soc. Open Sci., 7 (2020) 191074. https://doi.org/10.1098/rsos.191074.
G. A. Hollinger, A. A. Pereira, and G. S. Sukhatme, “Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles,” in 2013 IEEE International Conference on Robotics and Automation, May 2013, pp. 5593–5599, doi: https://doi.org/10.1109/ICRA.2013.6631380.
M. Baek, D. Jeong, D. Choi, and S. Lee, “Vehicle trajectory prediction and collision warning via fusion of multisensors and wireless vehicular communications,” Sensors (Switzerland), vol. 20, no. 1, Jan. 2020, https://doi.org/10.3390/s20010288.
J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, Y. Huang. T-drive: driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '10, pp. 99–108, New York, NY, 2010.
ISO, “Evaluation of measurement data—Guide to the expression of uncertainty in measurement,” Int. Organ. Stand. Geneva ISBN, vol. 50, no. September, p. 134, 2008, [Online]. Available: http://www.bipm.org/en/publications/guides/gum.html.
M. Solaguren-Beascoa Fernández, V. Ortega López and R. Serrano López, On the Uncertainty Evaluation for Repeated Measurements. Mapan J. Metrol. Soc. India, 29 (2014) 19–28. https://doi.org/10.1007/s12647-013-0057-x.
P. Banerjee and P.P. Thorat, Evaluation of performance of GPS receiver in CRRI network survey vehicle. Mapan J. Metrol. Soc. India, 24 (2009) 233–239. https://doi.org/10.1007/s12647-009-0028-4.
A. Asahara, K. Maruyama, A. Sato, and K. Seto, “Pedestrian-movement prediction based on mixed Markov-chain model,” in GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 2011, pp. 25–33, https://doi.org/10.1145/2093973.2093979.
A. Kharrat, I. S. Popa, K. Zeitouni, and S. Faiz, “Clustering algorithm for network constraint trajectories,” in Lecture Notes in Geoinformation and Cartography, 2008, pp. 631–647, https://doi.org/10.1007/978-3-540-68566-1_36.
F. Altche and A. De La Fortelle, “An LSTM network for highway trajectory prediction,” IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC, vol. 2018-March, pp. 353–359, 2018, https://doi.org/10.1109/ITSC.2017.8317913.
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Pahal, S., Rathee, N. Evaluating Uncertainty of Measurement While Predicting Location in Smart Vehicles. MAPAN 36, 377–388 (2021). https://doi.org/10.1007/s12647-021-00458-w
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DOI: https://doi.org/10.1007/s12647-021-00458-w