Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty
Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty
复制标题
具有多模态环境不确定性的机会约束轨迹规划
DOI：
10.1109/lcsys.2022.3186269
复制
发表时间：
2022
影响因子：
3
通讯作者：
M. Kamgarpour
中科院分区：
文献类型：
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作者：
Kai Ren;Heejin Ahn;M. Kamgarpour
我们解决了高斯混合模型（GMM）的不确定性下的安全轨迹规划。具体来说，我们使用GMM模型的障碍物的不确定状态的多模态行为。然后，我们对具有确定性线性系统和多面体障碍物的机会约束轨迹规划问题开发了一个混合整数圆锥曲线逼近。当GMM矩估计通过有限的样本，我们开发了一个紧密的浓度界，以确保机会约束与所需的信心。此外，为了限制违反约束的数量，我们开发了一个条件风险价值（CVaR）的方法对应的机会约束，并推导出一个易于处理的近似已知和估计的GMM时刻。我们用最先进的轨迹预测算法和自动驾驶数据集验证了我们的方法。
We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles’ uncertain states. Then, we develop a mixed-integer conic approximation to the chance-constrained trajectory planning problem with deterministic linear systems and polyhedral obstacles. When the GMM moments are estimated via finite samples, we develop a tight concentration bound to ensure the chance constraint with a desired confidence. Moreover, to limit the amount of constraint violation, we develop a Conditional Value-at-Risk (CVaR) approach corresponding to the chance constraints and derive a tractable approximation for known and estimated GMM moments. We verify our methods with state-of-the-art trajectory prediction algorithms and autonomous driving datasets.