  Though great effort has been put into the study of path planning on urban
roads and highways, few works have studied the driving strategy and trajectory
planning in low-speed driving scenarios, e.g., driving on a university campus
or driving through a housing or industrial estate. The study of planning in
these scenarios is crucial as these environments often cover the first or the
last one kilometer of a daily travel or logistic system. Additionally, it is
essential to treat these scenarios differently as, in most cases, the driving
environment is narrow, dynamic, and rich with obstacles, which also causes the
planning in such environments to continue to be a challenging task. This paper
proposes a hierarchical planning approach that separates the path planning and
the temporal planning. A path that satisfies the kinematic constraints is
generated through a modified bidirectional rapidly exploring random tree
(bi-RRT) approach. Following that, the timestamp of each node of the path is
optimized through sequential quadratic programming (SQP) with the feasible
searching bounds defined by safe intervals (SIs). Simulations and real tests in
different driving scenarios prove the effectiveness of this method.

Robotics