Operational requirements for localization in autonomous vehicles

TL;DR

This paper proposes a practical, safety-focused framework for autonomous vehicle localization that accounts for roadway type, lane boundaries, and environmental factors, improving upon previous restrictive models.

Contribution

It introduces a Weibull distribution-based deviation penalty tailored to roadway conditions, enhancing the robustness and realism of localization requirements for AVs.

Findings

Deviation penalty modeled with Weibull distribution

Parameters customized based on roadway and lane conditions

Adaptive penalty updates based on lane gap availability

Abstract

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.