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Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments

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Abstract

This paper presents a trajectory planning framework to deal with the highly dynamic environments for on-road driving. The trajectory optimization problem with parameterized curvature control was formulated to reach the goal state with the vehicle model and its dynamic constraints considered. This in contrast to existing curve fitting techniques guarantees the dynamic feasibility of the planned trajectory. With generation of multiple trajectory candidates along the Frenet frame, the vehicle is reactive to other road users or obstacles encountered. Additionally, to deal with more complex driving scenarios, its seamless interaction with an upper behavior planning layer was considered by having longitudinal motion planning responsive to the desired goal state. The trajectory evaluation and selection methodologies, along with the low-level tracking control, were also developed under this framework. The potential of the proposed trajectory planning framework was demonstrated under different dynamic driving scenarios such as lane-changing or merging with surrounding vehicles with its computation efficiency proven in real-time simulations.

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Acknowledgements

The author would like to thank the Smart Campus organization of the Ohio State University and Smart Columbus for partial support of the work presented here.

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Authors and Affiliations

  1. Automated Driving Laboratory and Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH, 43212, USA

    Sheng Zhu & Bilin Aksun-Guvenc

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  1. Sheng Zhu
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  2. Bilin Aksun-Guvenc
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Correspondence to Sheng Zhu.

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Zhu, S., Aksun-Guvenc, B. Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments. J Intell Robot Syst 100, 1055–1067 (2020). https://doi.org/10.1007/s10846-020-01215-y

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