GPU-Enabled Parallel Trajectory Optimization Framework for Safe Motion Planning of Autonomous Vehicles

Engineering notes

Key topics: autonomous driving, autonomous vehicle, motion planning, planning, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This letter presents a GPU-enabled parallel trajectory optimization framework for model predictive control (MPC) in complex urban environments. It fuses the advantages of sampling-based MPC, which can cope with nonconvex costmaps through random sampling of trajectories, with the advantages of gradient-based MPC, which can generate smooth trajectories. In addition, we leverage a generalized safety-embedded MPC problem definition with a discrete barrier state (DBaS). The proposed framework has three steps: 1) a costmap builder to generate the barrier function map, 2) a seed trajectory generator to choose randomly generated trajectories to send to the optimizers, and 3) a batch trajectory optimizer to optimize each of the seed trajectories and select the best trajectory. Experiments with real-time simulations compare the effectiveness of the proposed framework, sampling-based MPC, and gradient-based MPC, which optimizes a single trajectory. The experiments also compare the application of two different control sequence sampling schemes to the proposed framework. The results show that the proposed framework performs gradient-based optimization but can plan a better trajectory even in complex environments by providing various initial guesses. We also show that the proposed framework can perform more accurate control actions than sampling-based MPC.

Links and sources

• Official / arXiv page

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