Abstract
This paper addresses intention and trajectory prediction of exo-vehicles in an urban driving environment. Urban environments pose challenging scenarios for self-driving cars, specifically pertaining to traffic light detection, negotiating paths at the intersections and sometimes even overtaking illegally parked cars in narrow streets. This complex task of autonomously driving while considering anomalous situations make urban driving conditions unique when compared to highway driving. In order to overcome these challenges, we propose to use road contextual information to predict driving intentions and trajectories of surrounding vehicles. The intention prediction is obtained using a recurrent neural network and the trajectory is predicted using a polynomial model fitting of the past lateral and longitudinal components of the vehicle poses and road contextual information. The integrated process of intention and trajectory prediction is performed in real-time by deploying and testing on a self-driving car in a real urban environment.
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References
Verma, S., Eng, Y.H., Kong, H.X., Anderson, H., Meghjani, M., Leong, W.K., Shen, X., Zhang, C., Ang, M.H., Rus, D.: Vehicle detection, tracking and behavior analysis in urban driving environments using road context. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2018)
Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1(1), 33–55 (2016)
Patel, S., Griffin, B., Kusano, K., Corso, J.J.: Predicting future lane changes of other highway vehicles using RNN-based deep models. arXiv preprint arXiv:1801.04340 (2018)
Altché, F., De La Fortelle, A.: An LSTM network for highway trajectory prediction. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 353–359. IEEE (2017)
Ding, C., Wang, W., Wang, X., Baumann, M.: A neural network model for driver’s lane-changing trajectory prediction in urban traffic flow. Math. Probl. Eng. 2013, 8 (2013)
Li, X., Sun, Z., Cao, D., He, Z., Zhu, Q.: Real-time trajectory planning for autonomous urban driving: framework, algorithms, and verifications. IEEE/ASME Trans. Mechatron. 21(2), 740–753 (2016)
Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D.: Intention-aware motion planning. In: Algorithmic Foundations of Robotics X, pp. 475–491. Springer (2013)
Sezer, V., Bandyopadhyay, T., Rus, D., Frazzoli, E., Hsu, D.: Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3578–3585. IEEE (2015)
Galceran, E., Cunningham, A.G., Eustice, R.M., Olson, E.: Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: theory and experiment. Auton. Robots 41(6), 1367–1382 (2017)
Houenou, A., Bonnifait, P., Cherfaoui, V., Yao, W.: Vehicle trajectory prediction based on motion model and maneuver recognition. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4363–4369. IEEE (2013)
OpenStreetMap contributors. Planet dump. https://planet.osm.org, https://www.openstreetmap.org (2017)
Meghjani, M., Luo, Y., Ho, Q.H., Cai, P., Verma, S., Rus, D., Hsu, D.: Context and intention aware planning for urban driving. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019)
Acknowledgment
This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its CREATE programme, Singapore-MIT Alliance for Research and Technology (SMART) Future Urban Mobility (FM) IRG. We would also like to acknowledge the support of NVIDIA Corporation through its NVAIL program and NUS-NVIDIA MoU.
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Meghjani, M., Verma, S., Eng, Y.H., Ho, Q.H., Rus, D., Ang, M.H. (2020). Context-Aware Intention and Trajectory Prediction for Urban Driving Environment. In: Xiao, J., Kröger, T., Khatib, O. (eds) Proceedings of the 2018 International Symposium on Experimental Robotics. ISER 2018. Springer Proceedings in Advanced Robotics, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-33950-0_30
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DOI: https://doi.org/10.1007/978-3-030-33950-0_30
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