提出首个通用凸障碍物避让方法，提升自动驾驶实时性。
A Convex Obstacle Avoidance Formulation
- 用新逻辑融合方法构建凸优化框架，支持快速求解
- 即使障碍物在预测窗口外仍有效，可缩短预测时长
- 在非凸场景下性能优于或等同于主流方法，适合实车部署
自动驾驶需要在动态环境中可靠避障。非线性模型预测控制（NMPC）虽适用，但在高频率、时间敏感场景中效率不足。现有简化手段如线性化、缩小时域或减少时间节点，均影响精度与可靠性。本文首次提出通用凸障碍物避让公式，基于新颖的逻辑集成方法，使避障可融入凸型模型预测控制，显著提升计算效率。关键优势在于：即使障碍物位于预测时域之外，避障依然有效，支持更短时域实现实时部署。在非凸情形不可避免时，本方法性能达到或超过典型非凸方案。在高度非线性的自动驾驶系统中验证了该方法的有效性。
原文摘要 · Abstract (English)
Autonomous driving requires reliable collision avoidance in dynamic environments. Nonlinear Model Predictive Controllers (NMPCs) are suitable for this task, but struggle in time-critical scenarios requiring high frequency. To meet this demand, optimization problems are often simplified via linearization, narrowing the horizon window, or reduced temporal nodes, each compromising accuracy or reliability. This work presents the first general convex obstacle avoidance formulation, enabled by a novel approach to integrating logic. This facilitates the incorporation of an obstacle avoidance formulation into convex MPC schemes, enabling a convex optimization framework with substantially improved computational efficiency relative to conventional nonconvex methods. A key property of the formulation is that obstacle avoidance remains effective even when obstacles lie outside the prediction horizon, allowing shorter horizons for real-time deployment. In scenarios where nonconvex formulations are unavoidable, the proposed method meets or exceeds the performance of representative nonconvex alternatives. The method is evaluated in autonomous vehicle applications, where system dynamics are highly nonlinear.
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