论文标题
在没有协调的紧密约束环境中避免碰撞：一种分层控制方法
Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach
论文作者
论文摘要
我们提出了一种层次控制方法，用于在存在其他移动的AV和/或人类驱动车辆的紧密约束环境中操纵自动驾驶汽车（AV）。提出了两级层次结构：高级数据驱动的策略预测指标和基于低级模型的反馈控制器。该策略预测因子将动态环境的编码映射到通过神经网络的一组高级策略。根据所选策略，在线生成了AV位置空间中的一组时变超平面，并且相应的半空间约束包含在基于低级模型的后退视野控制器中。这些依赖策略的约束将车辆推向可能保持可行的区域。此外，预测的策略还告知在一组离散的策略之间切换，这在预测置信度较低时可以更保守。我们通过在1/10比例自主汽车平台上的模拟和实验在两车碰撞的情况下展示了提出的数据驱动的层次控制框架的有效性，在这两种情况下，策略指导的方法都优于模型预测控制基线，在1/10比例自主汽车平台上进行了实验。
We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV's position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.
