IEEE/CAA Journal of Automatica Sinica
Citation: | X. L. Zhu, W. Hu, Z. J. Deng, J. W. Zhang, F. Q. Hu, R. Zhou, K. Q. Li, and F.-Y. Wang, “Interaction-aware cut-in trajectory prediction and risk assessment in mixed traffic,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1752–1762, Oct. 2022. doi: 10.1109/JAS.2022.105866 |
[1] |
J. W. Zhang, G. F. Li, Z. J. Deng, H. L. Yu, J. P. Huissoon, and D. P. Cao, “Interaction-aware cut-in behavior prediction and risk assessment for autonomous driving,” IFAC-PapersOnLine, vol. 53, no. 5, pp. 656–663, Jan. 2020. doi: 10.1016/j.ifacol.2021.04.156
|
[2] |
J. W. Zhang, “An interaction-aware approach for online cut-in behavior prediction and risk assessment for autonomous driving,” M.S. thesis, Mech. Mechatron. Eng., Univ. Waterloo, Waterloo, USA, 2021.
|
[3] |
X. S. Wang, M. M. Yang, and D. Hurwitz, “Analysis of cut-in behavior based on naturalistic driving data,” Accid. Anal. Prev., vol. 124, pp. 127–137, Mar. 2019. doi: 10.1016/j.aap.2019.01.006
|
[4] |
C. Y. Zu, C. Yang, J. Wang, W. B. Gao, D. P. Cao, and F. Y. Wang, “Simulation and field testing of multiple vehicles collision avoidance algorithms,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045–1063, Jul. 2020. doi: 10.1109/JAS.2020.1003246
|
[5] |
F. Remmen, I. Cara, E. de Gelder, and D. Willemsen, “Cut-in scenario prediction for automated vehicles,” in Proc. IEEE Int. Conf. Vehicular Electronics and Safety, Madrid, Spain, 2018, pp. 1–7.
|
[6] |
N. Lyu, J. Q. Wen, Z. C. Duan, and C. Z. Wu, “Vehicle trajectory prediction and cut-in collision warning model in a connected vehicle environment,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 2, pp. 966–981, Feb. 2020.
|
[7] |
Y. M. Chen, C. Hu, and J. M. Wang, “Human-centered trajectory tracking control for autonomous vehicles with driver cut-in behavior prediction,” IEEE Trans. Veh. Technol., vol. 68, no. 9, pp. 8461–8471, Sep. 2019. doi: 10.1109/TVT.2019.2927242
|
[8] |
C. Q. Zhao, S. P. Li, F. G. Liu, W. S. Wang, and J. W. Gong, “Influence analysis of autonomous cars’ cut-in behavior on human drivers in a driving simulator,” in Proc. IEEE Intelligent Vehicles Symp., Changshu, China, 2018, pp. 85–90.
|
[9] |
M. Aramrattana, T. Larsson, C. Englund, J. Jansson, and A. Nåbo, “A novel risk indicator for cut-in situations,” in Proc. IEEE 23rd Int. Conf. Intelligent Transportation Systems, Rhodes, Greece, 2020, pp. 1–6.
|
[10] |
G. T. Xie, H. M. Qin, M. J. Hu, D. H. Ni, and J. Q. Wang, “Modeling discretionary cut-in risks using naturalistic driving data,” Transp. Res. Part F: Traffic Psychol. Behav., vol. 65, pp. 685–698, Aug. 2019. doi: 10.1016/j.trf.2017.11.022
|
[11] |
T. Zhang, W. J. Song, M. Y. Fu, Y. Yang, and M. L. Wang, “Vehicle motion prediction at intersections based on the turning intention and prior trajectories model,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1657–1666, Oct. 2021. doi: 10.1109/JAS.2021.1003952
|
[12] |
M. Al-Sharman, D. Murdoch, D. P. Cao, C. Lv, Y. Zweiri, D. Rayside, and W. Melek, “A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 169–178, Jan. 2021. doi: 10.1109/JAS.2020.1003474
|
[13] |
S. Lefèvre, D. Vasquez, and C. Laugier, “A survey on motion prediction and risk assessment for intelligent vehicles,” ROBOMECH J., vol. 1, no. 1, pp. 1–14, Jul. 2014. doi: 10.1186/s40648-014-0001-z
|
[14] |
H. F. Li, J. W. Huang, M. C. Zhou, Q. S. Shi, and Q. Fei, “Self-attention pooling-based long-term temporal network for action recognition,” IEEE Trans. Cogn. Dev. Syst., 2022, DOI: 10.1109/TCDS.2022.3145839.
|
[15] |
L. Y. Zhang, H. Han, M. C. Zhou, Y. Al-Turki, and A. Abusorrah, “An improved discriminative model prediction approach to real-time tracking of objects with camera as sensors,” IEEE Sens. J., vol. 21, no. 15, pp. 17308–17317, Aug. 2021. doi: 10.1109/JSEN.2021.3079214
|
[16] |
Y. F. Ma, Z. Y. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: A survey,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 315–329, Mar. 2020. doi: 10.1109/JAS.2020.1003021
|
[17] |
J. Schulz, K. Hirsenkorn, J. Löchner, M. Werling, and D. Burschka, “Estimation of collective maneuvers through cooperative multi-agent planning,” in Proc. IEEE Intelligent Vehicles Symp., Los Angeles, USA, 2017, pp. 624–631.
|
[18] |
X. X. Xiong, L. Chen, and J. Liang, “A new framework of vehicle collision prediction by combining SVM and HMM,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 3, pp. 699–710, Mar. 2018. doi: 10.1109/TITS.2017.2699191
|
[19] |
Y. Yoon, C. Kim, J. Lee, and K. Yi, “Interaction-aware probabilistic trajectory prediction of cut-in vehicles using Gaussian process for proactive control of autonomous vehicles,” IEEE Access, vol. 9, pp. 63440–63455, Jan. 2021. doi: 10.1109/ACCESS.2021.3075677
|
[20] |
S. Z. Dai, L. Li, and Z. H. Li, “Modeling vehicle interactions via modified LSTM models for trajectory prediction,” IEEE Access, vol. 7, pp. 38287–38296, Jan. 2019. doi: 10.1109/ACCESS.2019.2907000
|
[21] |
G. T. Xie, H. B. Gao, L. J. Qian, B. Huang, K. Q. Li, and J. Q. Wang, “Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5999–6008, Jul. 2018. doi: 10.1109/TIE.2017.2782236
|
[22] |
Y. Li, Y. Q. Zheng, B. Morys, S. Y. Pan, and J. Q. Wang, “Threat assessment techniques in intelligent vehicles: A comparative survey,” IEEE Intell. Transp. Syst. Mag., vol. 13, no. 4, pp. 71–91, 2021. doi: 10.1109/MITS.2019.2907633
|
[23] |
C. Xu, W. Z. Zhao, and C. Y. Wang, “An integrated threat assessment algorithm for decision-making of autonomous driving vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 6, pp. 2510–2521, Jun. 2020. doi: 10.1109/TITS.2019.2919865
|
[24] |
M. Althoff and A. Mergel, “Comparison of Markov chain abstraction and Monte Carlo simulation for the safety assessment of autonomous cars,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1237–1247, Dec. 2011. doi: 10.1109/TITS.2011.2157342
|
[25] |
C. Katrakazas, M. Quddus, and W. H. Chen, “A new integrated collision risk assessment methodology for autonomous vehicles,” Accid. Anal. Prev., vol. 127, pp. 61–79, Jun. 2019. doi: 10.1016/j.aap.2019.01.029
|
[26] |
J. Q. Wang, J. Wu, and Y. Li, “The driving safety field based on driver-vehicle-road interactions,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 4, pp. 2203–2214, Aug. 2015. doi: 10.1109/TITS.2015.2401837
|
[27] |
S. Kolekar, J. de Winter, and D. Abbink, “Human-like driving behaviour emerges from a risk-based driver model,” Nat. Commun., vol. 11, no. 1, pp. 1–13, Sep. 2020. doi: 10.1038/s41467-020-18353-4
|
[28] |
J. Wiest, M. Höffken, U. Kreßel, and K. Dietmayer, “Probabilistic trajectory prediction with Gaussian mixture models,” in Proc. IEEE Intelligent Vehicles Symp., Madrid, Spain, 2012, pp. 141–146.
|
[29] |
A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model MOBIL for car-following models,” Transp. Res. Rec.: J. Transp. Res. Board, vol. 1999, no. 1, pp. 86–94, Jan. 2007. doi: 10.3141/1999-10
|
[30] |
R Krajewski, J Bock, L Kloeker, and L. Eckstein, “The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems,” in Proc. IEEE 21st Int. Conf. Intelligent Transportation Systems, Maui, USA, 2018, pp. 2118–2125.
|