Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections (2401.14879v2)
Abstract: Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of way, is often handled implicitly in the prediction. However, an infrastructure-based maneuver planning can assign artificial priorities between cooperative vehicles, so it needs to evaluate many more potential scenarios. Additionally, the prediction horizon has to be long enough to assess the impact of a maneuver. We, therefore, present a novel long-term prediction approach handling the gap acceptance estimation and the velocity prediction in two separate stages. Thereby, the behavior of regular vehicles as well as priority assignments of cooperative vehicles can be considered. We train both stages on real-world traffic observations to achieve realistic prediction results. Our method has a competitive accuracy and is fast enough to predict a multitude of scenarios in a short time, making it suitable to be used in a maneuver planning framework.
- M. Buchholz, J. C. Müller, M. Herrmann, J. Strohbeck, B. Völz, M. Maier, J. Paczia, O. Stein, H. Rehborn, and R.-W. Henn, “Handling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensors,” IEEE Intell. Transp. Syst. Magazine, pp. 2–16, 2021.
- K. Dresner and P. Stone, “Multiagent traffic management: a reservation-based intersection control mechanism,” in Proc. of the Third Int. Joint Conf. on Autonomous Agents and Multiagent Syst., 2004. AAMAS 2004., 2004, pp. 530–537.
- M. B. Mertens, J. Müller, and M. Buchholz, “Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions,” in 2022 IEEE Intell. Veh. Symp. (IV), 2022, pp. 1174–1180.
- M. Klimke, B. Völz, and M. Buchholz, “Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks,” in preprint, accepted to 2023 IEEE Intell. Veh. Symp., 2023, arXiv:2301.12717.
- J. Strohbeck, V. Belagiannis, J. Müller, M. Schreiber, M. Herrmann, D. Wolf, and M. Buchholz, “Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks,” in 2020 IEEE/RSJ Int. Conf. on Intell. Robots and Syst. (IROS), 2020, pp. 1992–1998.
- J. Bock, R. Krajewski, T. Moers, S. Runde, L. Vater, and L. Eckstein, “The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections,” in 2020 IEEE Intell. Veh. Symp. (IV), 2020, pp. 1929–1934.
- M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical Review E, vol. 62, no. 2, pp. 1805–1824, 2000.
- S. Lefèvre, C. Sun, R. Bajcsy, and C. Laugier, “Comparison of parametric and non-parametric approaches for vehicle speed prediction,” in 2014 American Control Conf., 2014, pp. 3494–3499.
- J. Erdmann and D. Krajzewicz, “SUMO’s Road Intersection Model,” in Simulation of Urban Mobility, M. Behrisch, D. Krajzewicz, and M. Weber, Eds. Springer Berlin Heidelberg, 2014, pp. 3–17.
- Abhishek, M. A. A. Boon, and M. Mandjes, “Generalized gap acceptance models for unsignalized intersections,” Mathematical Methods of Operations Research, vol. 89, no. 3, pp. 385–409, 2019.
- Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking Deep Reinforcement Learning for Continuous Control,” 2016, arXiv:1604.06778.
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” 2017, arXiv:1707.06347.
- A. Kuefler, J. Morton, T. Wheeler, and M. Kochenderfer, “Imitating driver behavior with generative adversarial networks,” in 2017 IEEE Intell. Veh. Symp. (IV), 2017, pp. 204–211.
- M. Sackmann, H. Bey, U. Hofmann, and J. Thielecke, “Modeling Driver Behavior using Adversarial Inverse Reinforcement Learning,” in 2022 IEEE Intell. Veh. Symp. (IV), 2022, pp. 1683–1690.
- J. Ho and S. Ermon, “Generative Adversarial Imitation Learning,” 2016, arXiv:1606.03476.
- J. Strohbeck, J. Müller, A. Holzbock, and M. Buchholz, “DeepSIL: A Software-in-the-Loop Framework for Evaluating Motion Planning Schemes Using Multiple Trajectory Prediction Networks,” in 2021 IEEE/RSJ Int. Conf. on Intell. Robots and Syst. (IROS), 2021, pp. 7075–7081.
- K. Kurzer, C. Zhou, and J. Marius Zöllner, “Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search,” in 2018 IEEE Intell. Veh. Symp. (IV), 2018, pp. 529–536.
- M. Sackmann, H. Bey, U. Hofmann, and J. Thielecke, “Classification of Driver Intentions at Roundabouts,” in Proc. of the 6th Int. Conf. on Veh. Technol. and Intell. Transport Syst. SCITEPRESS - Sci. and Technol. Publications, 2020, pp. 301–311.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.