Safe motion planning for autonomous vehicles is a challenging task, since the exact future motion of other traffic participant is usually unknown. In this article, we present a verification technique ensuring that autonomous vehicles do not cause collisions by using fail-safe trajectories. Fail-safe trajectories are executed if the intended motion of the autonomous vehicle causes a safety-critical situation. Our verification technique is real-time capable and operates under the premise that intended trajectories are only executed if they have been verified as safe. The benefits of our proposed approach are demonstrated in different scenarios on an actual vehicle. Moreover, we present the first in-depth analysis of our verification technique used in dense urban traffic. Our results indicate that fail-safe motion planning has the potential to drastically reduce accidents while not resulting in overly conservative behaviors of the autonomous vehicle.

中文翻译：

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使用凸优化的自动驾驶汽车在线验证的故障安全运动规划

自动驾驶汽车的安全运动规划是一项具有挑战性的任务，因为其他交通参与者的确切未来运动通常是未知的。在本文中，我们提出了一种验证技术，可确保自动驾驶汽车通过使用故障安全轨迹不会引起碰撞。如果自动驾驶车辆的预期运动导致安全危急情况，则会执行故障安全轨迹。我们的验证技术具有实时能力，并且在预期轨迹只有在经过验证是安全的情况下才会执行的前提下运行。我们提出的方法的好处在实际车辆的不同场景中得到了证明。此外，我们首次对密集城市交通中使用的验证技术进行了深入分析。