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.

翻译：预测周围车辆的运动是确保自动驾驶安全性的关键，尤其是在缺乏先验信息的非结构化环境中。本文提出了一种仅基于运动观测即可精确预测周围车辆占据集合的新型在线方法。该方法分为两个阶段：首先，利用扩展卡尔曼滤波器和线性规划问题估计控制动作的紧凑Zonotopic集合；随后，通过可达性分析传播该集合以预测未来占据区域。该方法在城市环境仿真中验证了有效性，结果表明，在不依赖先验假设或训练数据的情况下，能够实现精确且紧凑的预测。