Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
复制标题
具有不确定性量化的碰撞预测的意图感知概率轨迹估计
DOI：
10.1109/cdc40024.2019.9029215
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
N. Hovakimyan
中科院分区：
文献类型：
--
作者：
Andrew Patterson;Arun Lakshmanan;N. Hovakimyan
动态和未知环境中的碰撞预测依赖于对环境如何变化的了解。许多碰撞预测方法依赖于障碍物在环境中如何移动的确定性知识。然而，通常无法获得关于障碍物运动的完整确定性知识。这项工作提出了一种基于高斯过程的预测方法，用概率知识取代了每个障碍物未来行为的确定性知识的假设，以允许考虑更大类别的障碍物。该方法仅依靠位置和速度测量来预测与动态障碍物的碰撞。我们表明，障碍物位置的不确定性区域可以用高斯过程回归生成的多项式组合来表示。为了控制任意时间范围内不确定性的增长，概率障碍物意图被假设为障碍物位置和速度的分布，这可以自然地包含在高斯过程框架中。我们的方法在一个案例研究中得到了证明，其中障碍超越了代理。在此模拟中，我们表明，尽管对障碍物行为的了解有限，但仍可以预测碰撞。
Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge of how obstacles are moving in the environment. However, complete deterministic knowledge of the obstacles’ motion is often unavailable. This work proposes a Gaussian process based prediction method that replaces the assumption of deterministic knowledge of each obstacle’s future behavior with probabilistic knowledge, to allow a larger class of obstacles to be considered. The method solely relies on position and velocity measurements to predict collisions with dynamic obstacles. We show that the uncertainty region for obstacle positions can be expressed in terms of a combination of polynomials generated with Gaussian process regression. To control the growth of uncertainty over arbitrary time horizons, a probabilistic obstacle intention is assumed as a distribution over obstacle positions and velocities, which can be naturally included in the Gaussian process framework. Our approach is demonstrated in a case study in which an obstacle overtakes the agent. In this simulation we show that the collision can be predicted despite having limited knowledge of the obstacle’s behavior.
DOI：
10.15607/rss.2019.xv.042
发表时间：
2019-02
期刊：
ArXiv
影响因子：
--
作者：
Arun Lakshmanan;Andrew Patterson;V. Cichella;N. Hovakimyan
通讯作者：
Arun Lakshmanan;Andrew Patterson;V. Cichella;N. Hovakimyan