Date of Original Version
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-08338-4_19
Abstract or Description
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.
Advances in Intelligent Systems and Computing, 302, 247-261.