论文标题
自动驾驶的最佳行为计划：一种通用的混合式配方
Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation
论文作者
论文摘要
混合二次二次编程（MIQP）已被确定为在低运行时为行为计划问题找到最佳解决方案的合适方法。逻辑约束和连续方程式与旁边进行了优化。但是，它仅是针对一条直路制定的，省略了常见情况，例如在交叉路口进行轮流。这阻止了迄今为止模型在现实中被使用。基于三重整合器模型公式，我们计算车辆的方向并以析取方式对其进行建模。这使我们能够制定线性约束，以解释非独立的避免。这些约束是近似值，我们介绍了理论。我们在两个基准方案中显示了适用性，并通过使用非线性优化解决相同的模型来证明可行性。这种新模型将使研究人员能够利用MIQP的好处，例如逻辑约束或全球最优性。
Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical constraints and continuous equations are optimized alongside. However, it has only been formulated for a straight road, omitting common situations such as taking turns at intersections. This has prevented the model from being used in reality so far. Based on a triple integrator model formulation, we compute the orientation of the vehicle and model it in a disjunctive manner. That allows us to formulate linear constraints to account for the non-holonomy and collision avoidance. These constraints are approximations, for which we introduce the theory. We show the applicability in two benchmark scenarios and prove the feasibility by solving the same models using nonlinear optimization. This new model will allow researchers to leverage the benefits of MIQP, such as logical constraints, or global optimality.
