Intelligent vehicles face considerable challenges in the complex traffic environment since they need to deal with various constraints and elements. This dissertation puts forward a novel trajectory planning framework for intelligent vehicles to generate safe and optimal driving trajectories. First, we design a spatiotemporal occupancy framework to deal with all kinds of elements in the complex driving environment based on the Frenét frame. This framework unifies various constraints on the road in the three-dimensional spatiotemporal representation and clearly describes the collision-free configuration space. Then we use the convex approximation method to construct a time-varying convex feasible region based on the above accurate temporal and spatial description. We formulate the trajectory planning problem as a standard quadratic programming formulation with collision-free and dynamics constraints. Finally, we apply the iterative convex optimization algorithm to solve the quadratic programming problem in the time-varying convex feasible region. Moreover, we design several typical experimental scenarios and have verified that the proposed method has good effectiveness and real-time.

中文翻译：

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基于迭代凸优化的复杂交通智能车辆最优轨迹生成

智能车辆在复杂的交通环境中面临着相当大的挑战，因为它们需要处理各种约束和要素。本论文提出了一种新颖的智能车辆轨迹规划框架，以生成安全和最优的行驶轨迹。首先，我们设计了一个时空占用框架来处理基于 Frenét 框架的复杂驾驶环境中的各种元素。该框架在三维时空表示中统一了道路上的各种约束，并清晰地描述了无碰撞配置空间。然后我们利用凸逼近的方法，在上述准确时空描述的基础上，构造一个时变凸可行区域。我们将轨迹规划问题制定为具有无碰撞和动态约束的标准二次规划公式。最后，我们应用迭代凸优化算法来解决时变凸可行域中的二次规划问题。此外，我们设计了几个典型的实验场景，并验证了所提出的方法具有良好的有效性和实时性。