稀疏规划器：一种基于条件变分自编码器的高效采样混合规划器

中文摘要 AI 辅助

轨迹规划是自动驾驶系统的核心组成部分，其实时性能和解决方案质量直接影响安全性和可靠性。基于采样的运动规划（SBMP）因其能够通过参数空间采样逼近近最优解而被广泛采用。然而，获得高质量轨迹通常需要密集采样，这导致在复杂交通场景中产生大量计算开销和显著的运行时变异性。为解决这一局限，我们提出了一种稀疏规划器（SP），通过使用条件变分自编码器（CVAE）学习场景上下文与有效轨迹参数之间的条件关系来提高采样效率。通过建模高质量采样分布的结构，SP直接在参数空间中生成成本效益高的样本，显著降低了所需采样密度，同时保持了解决方案质量。实验结果表明，SP在仅使用八分之一采样密度的情况下，实现了比最先进的FISS+规划器更低的轨迹成本。此外，SP在障碍物密集场景中展示了改进的距离保持能力，并维持了更低且更稳定的运行时特性，表明其增强了计算效率并具有可预测的运行时行为。

英文摘要

Trajectory planning is a core component of autonomous driving systems, where real-time performance and solution quality directly affect safety and reliability. Sample-Based Motion Planning (SBMP) is widely adopted for its ability to approximate near-optimal solutions through parameter space sampling. However, achieving high-quality trajectories typically requires dense sampling, leading to substantial computational overhead and significant runtime variability in complex traffic scenarios. To address this limitation, we propose a Sparse Planner (SP) that improves sampling efficiency by learning the conditional relationship between scene context and effective trajectory parameters using a Conditional Variational Autoencoder (CVAE). By modeling the structure of high-quality sampling distributions, SP directly generates cost-effective samples in the parameter space, significantly reducing the required sampling density while preserving solution quality. Experimental results show that SP achieves lower trajectory cost than the state-of-the-art FISS+ planner while using only one-eighth of the sampling density. In addition, SP demonstrates improved distance-keeping capability in obstacle-rich scenarios and maintains reduced and more stable runtime characteristics, indicating enhanced computational efficiency and predictable runtime behavior.

发表机构

• Technische Hochschule Ingolstadt (THI)（英戈尔施塔特应用技术大学）
• German Aerospace Center (DLR)（德国航空航天中心）
• German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）

机构由 AI 辅助整理，请以论文原文为准。