城市环境中自动驾驶车辆的横向间隙最大化
On Maximizing Lateral Clearance of an Autonomous Vehicle in Urban Environments
摘要：
我们考虑自动驾驶汽车与道路代理的距离最大化的问题。为此，我们采用了模型预测控制（MPC）方法的转向跟踪控制的自动驾驶汽车（AV）。具体来说，我们首先提出了一个传统的MPC控制器，然后扩展到编码的间隙最大化的目标，通过操纵其成本函数和约束。我们提供的见解，实现这样的目标所需的额外信息，以及如何修改的结构，原来的控制器。此外，建立了常用的安全指标和道路使用者许可之间的联系。我们实现MPC控制器使用两个现成的数值求解器，评估其计算的可行性。最后，我们在波士顿和新加坡的公共道路上展示了所提出的方法的实验结果。
英文摘要：
We consider the problem of maximizing distance to road agents for a self-driving car. To this extent, we employ a Model Predictive Control (MPC) approach for the steering tracking control of an Autonomous Vehicle (AV). Specifically, we first present a traditional MPC controller, which is then extended to encode the clearance maximization goal by manipulating its cost function and constraints. We provide insights on the additional information needed to achieve such goal, and how this modifies the structure of the original controller. Furthermore, a connection between commonly used safety metrics and clearance to road users is established. We implement the MPC controller using two off-the-shelf numerical solvers, assessing its computational feasibility. Finally, we show experimental results of the proposed approach on public roads in Boston and in Singapore.
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