Predictive Runtime Monitoring of Vehicle Models Using Bayesian Estimation and Reachability Analysis
Predictive Runtime Monitoring of Vehicle Models Using Bayesian Estimation and Reachability Analysis
复制标题
DOI：
10.1109/iros45743.2020.9340755
复制
发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Yi‐Shyong Chou;Hansol Yoon;S. Sankaranarayanan
中科院分区：
文献类型：
--
作者：
Yi‐Shyong Chou;Hansol Yoon;S. Sankaranarayanan
我们提出了一种预测运行时监测技术，用于估计未来的车辆位置和与障碍物碰撞的概率。车辆动力学将位置和速度如何随时间变化作为外部输入的函数进行建模。它们通常由离散时间随机模型描述。虽然位置和速度可以测量，但在这些模型中，输入（转向和油门）不能直接测量。在我们的论文中，我们应用贝叶斯推理技术的实时估计，先验分布的未知数和噪声状态测量。接下来，我们预先计算集值可达性分析，以近似车辆的未来位置。预先计算的可达性集合与通过贝叶斯估计计算的后验概率相结合，以提供可用于检测与障碍物的即将发生的碰撞的预测验证框架。我们的方法进行了评估，使用无人机的协调转弯车辆模型，使用从一个Talon无人机飞行试验获得的机载测量数据。我们还比较了基于采样的方法的结果。我们发现，预先计算的可达性分析可以提供准确的警告，提前6秒，提高了准确性的警告的时间范围从6秒缩小到2秒。该方法还优于采样方面的板载计算成本和准确性措施。
We present a predictive runtime monitoring technique for estimating future vehicle positions and the probability of collisions with obstacles. Vehicle dynamics model how the position and velocity change over time as a function of external inputs. They are commonly described by discrete-time stochastic models. Whereas positions and velocities can be measured, the inputs (steering and throttle) are not directly measurable in these models. In our paper, we apply Bayesian inference techniques for real-time estimation, given prior distribution over the unknowns and noisy state measurements. Next, we pre-compute the set-valued reachability analysis to approximate future positions of a vehicle. The pre-computed reachability sets are combined with the posterior probabilities computed through Bayesian estimation to provided a predictive verification framework that can be used to detect impending collisions with obstacles. Our approach is evaluated using the coordinated-turn vehicle model for a UAV using on-board measurement data obtained from a flight test of a Talon UAV. We also compare the results with sampling-based approaches. We find that precomputed reachability analysis can provide accurate warnings up to 6 seconds in advance and the accuracy of the warnings improve as the time horizon is narrowed from 6 to 2 seconds. The approach also outperforms sampling in terms of on-board computation cost and accuracy measures.