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Enhancing motion safety by identifying safety-critical passageways

机译:通过识别安全关键通道来提高运动安全性

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Safety is the most important aspect of systems which have to perform collision-free motions in dynamic environments. Formal verification methods, such as reachability analysis, are capable of guaranteeing safety for a given model and given assumptions (e. g. bounded velocity and acceleration). However, certain assumptions can be violated by dynamic obstacles during the execution of the verified motion plan, exposing the system to potential collisions. To compensate for the invalidated verification, this paper introduces the Point of No Return (PNR) and the Point of Guaranteed Arrival (PGA) by incorporating invariably safe sets. These concepts allow one to divide the planned trajectory into safe sections and safety-critical passageways. For the former, we are able to provide safety guarantees for an infinite time horizon. For the latter, we present a method to minimize such safety-critical passageways prior to execution and thus reduce the risk of potential collisions if assumptions are violated during execution. The safety benefits are highlighted by a numerical example of overtaking maneuvers of self-driving vehicles.
机译:安全是必须在动态环境中执行无碰撞运动的系统的最重要方面。形式验证方法,例如可达性分析,能够保证给定模型和给定假设(例如,有限速度和加速度)的安全性。但是,在执行经过验证的运动计划期间,动态障碍可能会违反某些假设,从而使系统遭受潜在的碰撞。为了补偿无效验证,本文通过引入不变的安全集,介绍了不返回点(PNR)和保证到达点(PGA)。这些概念使人们可以将计划的轨迹划分为安全段和安全关键通道。对于前者,我们能够在无限的时间范围内提供安全保证。对于后者,我们提出了一种在执行之前最小化此类安全关键通道的方法,从而降低了在执行过程中违反假设的情况下可能发生碰撞的风险。通过自动驾驶车辆超车的数值示例突出了安全益处。

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