Abstract
We propose a motion planning method for automated vehicles (AVs) to complete driving tasks in dynamic traffic scenes. The proposed method aims to generate motion trajectories for an AV after obtaining the surrounding dynamic information and making a preliminary driving decision. The method generates a reference line by interpolating the original waypoints and generates optional trajectories with costs in a prediction interval containing three dimensions (lateral distance, time, and velocity) in the Frenet frame, and filters the optimal trajectory by a series of threshold checks. When calculating the feasibility of optional trajectories, the cost of all optional trajectories after removing obstacle interference shows obvious axisymmetric regularity concerning the reference line. Based on this regularity, we apply the constrained Simulated Annealing Algorithm (SAA) to improve the process of searching for the optimal trajectories. Experiments in three different simulated driving scenarios (speed maintaining, lane changing, and car following) show that the proposed method can efficiently generate safe and comfortable motion trajectories for AVs in dynamic environments. Compared with the method of traversing sampling points in discrete space, the improved motion planning method saves 70.23% of the computation time, and overcomes the limitation of the spatial sampling interval.
Highlights
Received: 14 December 2021As an important aspect of profiling the evolution of human civilization, transportation modalities are rapidly moving towards automation and interconnection
The purpose of motion planning is to generate a trajectory that satisfies the constraints of vehicle dynamics, driving safety, comfort, and efficiency, after the automated vehicles (AVs) makes the initial driving decision based on dynamic environment information and its state
This paper proposes a safe and efficient motion planning method for dynamic traffic scenes by combining the sampling-based method in the Frenet frame and the optimal trajectory searching method improved by Simulated Annealing Algorithm (SAA)
Summary
As an important aspect of profiling the evolution of human civilization, transportation modalities are rapidly moving towards automation and interconnection. It is widely accepted that transportation systems will become much more efficient and safer when the task of vehicle driving shifts from a manual process to an automatic process. The current process of achieving autonomous driving consists of environment perception and localization, behavior decisions, motion planning, and trajectory tracking. The purpose of motion planning is to generate a trajectory that satisfies the constraints of vehicle dynamics, driving safety, comfort, and efficiency, after the AV makes the initial driving decision based on dynamic environment information and its state. Motion planning is a key part of the autonomous driving technology process and a critical factor in drivers’ experience and safety.
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