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

Hierarchical Trajectory Planning for Autonomous Driving in Low-speed Driving Scenarios Based on RRT and Optimization