用区间体在线预测障碍物占据区域，无需先验知识。
Behavior-Aware Online Prediction of Obstacle Occupancy using Zonotopes
- 通过扩展卡尔曼滤波与线性规划估计控制动作的紧凑区间体
- 基于可达性分析实现未来占据区域的准确预测
- 适合无先验信息的复杂城市环境，无需训练数据
在缺乏先验信息的非结构化环境中，预测周边车辆运动是保障自动驾驶安全的关键。本文提出一种新颖的在线方法，仅基于运动观测即可准确预测周边车辆的占据集合。该方法分为两阶段：首先利用扩展卡尔曼滤波和线性规划（LP）问题，估计出紧凑的区间体形式的控制动作集；随后通过可达性分析将该集合向前传播，预测未来占据区域。在城市环境仿真中验证了该方法的有效性，结果表明其能在不依赖先验假设或训练数据的情况下，实现准确且紧凑的预测。
原文摘要 · Abstract (English)
Predicting the motion of surrounding vehicles is key to safe autonomous driving, especially in unstructured environments without prior information. This paper proposes a novel online method to accurately predict the occupancy sets of surrounding vehicles based solely on motion observations. The approach is divided into two stages: first, an Extended Kalman Filter and a Linear Programming (LP) problem are used to estimate a compact zonotopic set of control actions; then, a reachability analysis propagates this set to predict future occupancy. The effectiveness of the method has been validated through simulations in an urban environment, showing accurate and compact predictions without relying on prior assumptions or prior training data.
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