Autonomous vehicles (AVs) need to determine their position and orientation accurately with respect to global coordinate system or local features under different scene geometries, traffic conditions and environmental conditions. \cite{reid2019localization} provides a comprehensive framework for the localization requirements for AVs. However, the framework is too restrictive whereby - (a) only a very small deviation from the lane is tolerated (one every $10^{8}$ hours), (b) all roadway types are considered same without any attention to restriction provided by the environment onto the localization and (c) the temporal nature of the location and orientation is not considered in the requirements. In this research, we present a more practical view of the localization requirement aimed at keeping the AV safe during an operation. We present the following novel contributions - (a) we propose a deviation penalty as a cumulative distribution function of the Weibull distribution which starts from the adjacent lane boundary, (b) we customize the parameters of the deviation penalty according to the current roadway type, particular lane boundary that the ego vehicle is against and roadway curvature and (c) we update the deviation penalty based on the available gap in the adjacent lane. We postulate that this formulation can provide a more robust and achievable view of the localization requirements than previous research while focusing on safety.

翻译：自主驾驶车辆需在不同场景几何结构、交通条件及环境状态下，精确确定其相对于全局坐标系或局部特征的位置与姿态。文献\cite{reid2019localization}为自主驾驶车辆的定位需求提供了系统性框架，但该框架约束过于严格，具体表现为：(a) 仅允许极小的车道偏离容差（每$10^{8}$小时发生一次）；(b) 未区分道路类型，忽略环境对定位的约束作用；(c) 未考虑位置与姿态的时间动态特性。本研究提出更具实用性的定位需求框架，旨在保障自主驾驶车辆运行安全。我们的创新贡献包括：(a) 提出基于威布尔分布累积分布函数的偏离代价函数，其起始阈值为相邻车道边界；(b) 根据当前道路类型、本车所在车道边界特征及道路曲率，定制偏离代价函数参数；(c) 依据相邻车道的可用间隙动态更新偏离代价函数。我们推断，该策略相比既有研究能提供更鲁棒且可实现的定位需求体系，同时聚焦安全性保障。