This paper presents an optimisation-based approach for an obstacle avoidance problem within an autonomous vehicle racing context. Our control regime leverages online reachability analysis and sensor data to compute the maximal safe traversable region that an agent can traverse within the environment. The idea is to first compute a non-convex safe region, which then can be convexified via a novel coupled separating hyperplane algorithm. This derived safe area is then used to formulate a nonlinear model-predictive control problem that seeks to find an optimal and safe driving trajectory. We evaluate the proposed approach through a series of diverse experiments and assess the runtime requirements of our proposed approach through an analysis of the effects of a set of varying optimisation objectives for generating these coupled hyperplanes.

翻译：本文提出了一种基于优化的方法，用于解决自动驾驶赛车场景中的障碍物规避问题。我们的控制策略利用在线可达性分析与传感器数据，计算出智能体可在环境内安全通行的最大可行区域。其核心思路是：首先计算非凸安全区域，随后通过一种新型耦合分离超平面算法对该区域进行凸化处理。所推导出的安全区域被用于构建非线性模型预测控制问题，旨在寻找最优且安全的行驶轨迹。我们通过一系列多样化实验评估了所提方法，并通过分析一组不同优化目标对生成耦合超平面的影响，评估了该方法的运行时要求。