Abstract
In order to achieve smooth autonomous driving in real-life urban and highway environments, a motion planner must generate trajectories that are locally smooth and responsive (reactive), and at the same time, far-sighted and intelligent (deliberative). Prior approaches achieved both planning qualities for full-speed-range operations at a high computational cost. Moreover, the planning formulations were mostly a trajectory search problem based on a single weighted cost, which became hard to tune and highly scenario-constrained due to overfitting. In this paper, a pipelined (phased) framework with tunable planning modules is proposed for general on-road motion planning to reduce the computational overhead and improve the tunability of the planner.
This work was supported by General Motors.
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- 1.
In our terminology, traffic may refer to static obstacles and moving objects like pedestrians, bicyclists or surrounding vehicles.
- 2.
If traffic-based reference planning fails, it simply passes through the traffic-free reference, which results in collision.
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Gu, T., Dolan, J.M., Lee, JW. (2016). On-Road Trajectory Planning for General Autonomous Driving with Enhanced Tunability. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_19
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DOI: https://doi.org/10.1007/978-3-319-08338-4_19
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