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Analysis of Deep Learning Model for Trajectory Prediction of Vehicle

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Power Engineering and Intelligent Systems (PEIS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1098))

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Abstract

In this research, Long Short-Term Memory (LSTM) model and Gated Recurrent Unit Model (GRU) have been applied to the open-source HighD dataset to check the trajectory prediction of surrounding vehicles. The LSTM model has given better results as compared to the GRU model with an accuracy of 86%. The Mean Square Error (MSE) method is used to understand the learning effect of the models. Prediction errors were calculated and measured in the longitudinal direction to get the accuracy of the learning models in more detail. If the vehicle will change its path or lane in the longitudinal direction then will get the trajectory of that vehicle.

This research work is supported and funded by REVA Technologies, Navi Mumbai, India. https://revatech-ai.com.

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References

  1. Lefevre, Vasquez D, Laugier C (2014) A survey on motion prediction and risk assessment for intelligent vehicles. Robomech J 1(1):1

    Google Scholar 

  2. Deo N, Trivedi MM (2018) Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. Proc IEEE Intell Veh Symp (IV), pp 1179–1184

    Google Scholar 

  3. Deo N, Rangesh A, Trivedi MM (2018) How would surround vehicles move? A unified framework for maneuver classification and motion prediction. IEEE Trans Intell Veh 3(2):129–140 Jun

    Article  Google Scholar 

  4. Fujii R, Vongkulbhisal J, Hachiuma R, Saito H (2021) A two-block RNN-based trajectory prediction from incomplete trajectory. IEEE Access 9:56140–56151

    Article  Google Scholar 

  5. Li L, Zhao W, Xu C, Wang C, Chen Q, Dai S (2021) LaneChange intention inference based on RNN for autonomous driving on highways. IEEE Trans Veh Technol 70(6):5499–5510

    Google Scholar 

  6. Altche F, de La Fortelle A (2017) An LSTM network for highway trajectory prediction. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), 2017, pp 353–359

    Google Scholar 

  7. Zhang N, Zhang N, Zheng Q et al (2022) Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network. Acta Geotech 17:1167–1182. https://doi.org/10.1007/s11440-021-01319-1

  8. Shih C-S, Huang P-W, Yen E-T, Tsung P-K (2019) Vehicle speed prediction with RNN and attention model under multiple scenarios. In: IEEE Intelligent transportation systems conference (ITSC). Auckland, New Zealand 2019, pp 369–375. https://doi.org/10.1109/ITSC.2019.8917479

  9. Colyar J, Halkias J (2007) US highway 101 dataset. Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-07-030

    Google Scholar 

  10. Colyar J, Halkias J (2007) US highway 80 dataset. Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-07-030

    Google Scholar 

  11. Deo N, Trivedi MM (2017) Learning and predicting on-road pedestrian behavior around vehicles. In: 2017 IEEE 20th International conference on intelligent transportation systems

    Google Scholar 

  12. Messaoud K, Deo N, Trivedi MM, Nashashibi F (2020) Multi-head attention with joint agent-map representation for trajectory prediction in autonomous driving. arXiv:2005.02545

  13. Boulton FA, Grigore EC, Wolff EM (2020) Motion prediction using trajectory sets and self-driving domain knowledge. arXiv:2006.04767

  14. Dai S, Li L, Li Z (2019) Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access 7:38287–38296. https://doi.org/10.1109/ACCESS.2019.2907000

    Article  Google Scholar 

  15. Greer R, Deo N, Trivedi M (2021) Trajectory prediction in autonomous driving with a lane heading auxiliary loss. IEEE Robot Autom Lett 3

    Google Scholar 

  16. Qin X, Li Z, Zhang K, Mao F, Jin X (2023) Vehicle trajectory prediction via urban network modeling. Sensors 23:4893. https://doi.org/10.3390/s23104893

  17. Boulton FA, Grigore EC, Wolff EM (2020) Motion prediction using trajectory sets and self-driving domain knowledge. arXiv:2006.04767

  18. Ridel D, Rehder E, Lauer M, Stiller C, Wolf D (2018) A literature review on the prediction of pedestrian behavior in urban scenarios. Proc IEEE 21st Int Conf Intell Transp Syst, pp 3105–3112

    Google Scholar 

  19. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR. abs/1512.03385

    Google Scholar 

  20. He Y, Jiang J (2021) Review of situation cognition-taking trajectory prediction as an example. IEEE Int Conf Unmanned Syst (ICUS) 2021:703–709. https://doi.org/10.1109/ICUS52573.2021.9641431

    Article  Google Scholar 

  21. Messaoud K, Yahiaoui I, Verroust-Blondet A, Nashashibi F (2021) Attention based vehicle trajectory prediction. IEEE Trans Intell Veh 6(1):175–185. https://doi.org/10.1109/TIV.2020.2991952. March

    Article  Google Scholar 

  22. Wang C, Ma L, Li R, Durrani TS, Zhang H (2019) Exploring trajectory prediction through machine learning methods. IEEE Access 7:101441–101452. https://doi.org/10.1109/ACCESS.2019.2929430

    Article  Google Scholar 

  23. Bhujel N, Teoh EK, Yau W-Y (2019) Pedestrian trajectory prediction using RNN encoder-decoder with spatio-temporal attentions. In: 2019 IEEE 5th International conference on mechatronics system and robots (ICMSR), Singapore, 2019, pp 110–114. https://doi.org/10.1109/ICMSR.2019.8835478

  24. Gomez-Gonzalez S, Prokudin S, Scholkopf B, Peters J (2020) Real time trajectory prediction using deep conditional generative models. IEEE Robot Autom Lett 5(2):970–976. https://doi.org/10.1109/LRA.2020.2966390. April

    Article  Google Scholar 

  25. Li Z, Du X, Cao Y (2020) DAT-RNN: trajectory prediction with diverse attention. In: 2020 19th IEEE International conference on machine learning and applications (ICMLA), 2020, Miami, FL, USA, pp 1512–1518. https://doi.org/10.1109/ICMLA51294.2020.00233

  26. Houenou A, Bonnifait P, Cherfaoui V, Yao W (2013) Vehicle trajectory prediction based on motion model and maneuver recognition. IEEE/RSJ Int Conf Intell Robots Syst 2013:4363–4369. https://doi.org/10.1109/IROS.2013.6696982

    Article  Google Scholar 

  27. Lefkopoulos V, Menner M, Domahidi A, Zeilinger MN (2021) Interaction-aware motion prediction for autonomous driving: a multiple model Kalman filtering scheme. IEEE Robot Autom Lett 6(1):80–87. https://doi.org/10.1109/LRA.2020.3032079. Jan

    Article  Google Scholar 

  28. Wang J, Wang P, Zhang C, Su K, Li J (2021) F-Net: fusion neural network for vehicle trajectory prediction in autonomous driving. In: ICASSP 2021—2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2021, Toronto, ON, Canada, pp 4095–4099. https://doi.org/10.1109/ICASSP39728.2021.9413881

  29. Guan H, Guo P (2023) Research on pedestrian trajectory prediction by GAN model based on LSTM. In: 2023 IEEE 3rd International conference on power, electronics and computer applications (ICPECA), 2023, Shenyang, China, pp 1400–1405. https://doi.org/10.1109/ICPECA56706.2023.10076086

  30. Dai S, Li L, Li Z (2019) Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access 7:38287–38296. https://doi.org/10.1109/ACCESS.2019.2907000

    Article  Google Scholar 

  31. Dai S, Li L, Li Z (2019) Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access 7:38287–38296. https://doi.org/10.1109/ACCESS.2019.2907000

    Article  Google Scholar 

  32. Becker S, Hug R, Huebner W, Arens M, Morris BT (2022) Generating versatile training samples for UAV trajectory prediction. In: Communications in computer and information science, vol 1667. Springer, pp 1–11

    Google Scholar 

  33. Zhu Y, Liu J, Guo C, Song P, Zhang J, Zhu J (2020) Prediction of battlefield target trajectory based on LSTM. In: 2020 IEEE 16th International conference on control & automation (ICCA), 2020, Singapore, pp 725–730. https://doi.org/10.1109/ICCA51439.2020.9264521

  34. Bahra N, Pierre S (2020) RNN-based user trajectory prediction using a preprocessed dataset. In: 2020 16th International conference on wireless and mobile computing, networking and communications (WiMob), Thessaloniki, Greece, 2020, pp 1–6. https://doi.org/10.1109/WiMob50308.2020.9253403

  35. Jia Y, Cai C, Görges D (2020) An LSTM-based speed predictor based on traffic simulation data for improving the performance of energy-optimal adaptive cruise control. In: 2020 IEEE 23rd international conference on intelligent transportation systems (ITSC), Rhodes, Greece, 2020, pp 1–7. https://doi.org/10.1109/ITSC45102.2020.9294285

  36. Tsao L-W, Wang Y-K, Lin H-S, Shuai H-H, Wong L-K, Cheng W-H (2022) Social-SSL: self-supervised cross-sequence representation learning based on transformers for multi-agent trajectory prediction. Lecture Notes in Computer Science, vol 13682. Presented at the conference

    Google Scholar 

  37. Krajewski R, Bock J, Kloeker L, Eckstein L (2018) The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: 2018 21st International conference on intelligent transportation systems (ITSC), Maui, HI, USA, 2018, pp 2118–2125. https://doi.org/10.1109/ITSC.2018.8569552

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Acknowledgements

This research work is supported and funded by REVA Technologies, Mumbai, India under its research and development center.

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Authors and Affiliations

  1. Department of Computer Engineering, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, India

    Sushila Umesh Ratre & Bharti Joshi

Authors
  1. Sushila Umesh Ratre
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  2. Bharti Joshi
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Correspondence to Sushila Umesh Ratre .

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Editors and Affiliations

  1. National Institute of Technology Delhi, New Delhi, Delhi, India

    Vivek Shrivastava

  2. Department of Applied Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Ratre, S.U., Joshi, B. (2024). Analysis of Deep Learning Model for Trajectory Prediction of Vehicle. In: Shrivastava, V., Bansal, J.C., Panigrahi, B.K. (eds) Power Engineering and Intelligent Systems. PEIS 2023. Lecture Notes in Electrical Engineering, vol 1098. Springer, Singapore. https://doi.org/10.1007/978-981-99-7383-5_17

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  • DOI: https://doi.org/10.1007/978-981-99-7383-5_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7382-8

  • Online ISBN: 978-981-99-7383-5

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