Localization Requirements for Autonomous Vehicles

TL;DR

The paper tackles the challenge of localization for autonomous vehicles by formulating a protection-level and alert-limit framework informed by civil aviation safety practices. It derives horizontal, vertical, and orientation requirements tied to US road geometry and vehicle dimensions, translating a target integrity level of roughly $TLS = 2\times10^{-10}$ fatal crashes per vehicle mile into a practical localization standard of $P_{loc} \approx 10^{-8}$ failures/hour. Key contributions include explicit highway and local-street localization bounds (e.g., highway lateral bound $\leq 0.57$ m and orientation $\leq 1.50^\circ$, with 95% performance around $0.20$ m and $0.48$ m; local roads require tighter lateral/longitudinal bounds around $0.29$ m and orientation around $0.50^\circ$) and the design equations that couple position and attitude errors to alert limits. The work argues for a system-of-systems approach—combining GNSS, IMU, cameras, LiDAR, HD maps, and V2X—plus rigorous certification and map integration to achieve the required reliability and safety for full autonomous operation.

Abstract

Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10^-8 probability of failure per hour of operation. We then define the geometry of the problem, where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on. Longitudinal, lateral, and vertical localization error bounds (alert limits) and 95% accuracy requirements are derived based on US road geometry standards (lane width, curvature, and vertical clearance) and allowable vehicle dimensions. For passenger vehicles operating on freeway roads, the result is a required lateral error bound of 0.57 m (0.20 m, 95%), a longitudinal bound of 1.40 m (0.48 m, 95%), a vertical bound of 1.30 m (0.43 m, 95%), and an attitude bound in each direction of 1.50 deg (0.51 deg, 95%). On local streets, the road geometry makes requirements more stringent where lateral and longitudinal error bounds of 0.29 m (0.10 m, 95%) are needed with an orientation requirement of 0.50 deg (0.17 deg, 95%).

Figures (19)

• Figure 1: Definition of localization protection levels for automotive applications.
• Figure 2: Society of Automotive Engineers (SAE) levels of road vehicle autonomy (source: National Highway Traffic Safety Administration).
• Figure 3: Historical trend in widely available RMS position accuracy from a variety of technologies. Compiled by the authors based on data from weems1951Dippy1946Kelly1986Lo2013Stansell1968Stansell1971PARKINSON1995WilliamJ.HughesTechnicalCenterFederalAviationAdministrationGPS2001WilliamJ.HughesTechnicalCenterFederalAviationAdministrationGPS2006WilliamJ.HughesTechnicalCenterFederalAviationAdministrationGPS2011WilliamJ.HughesTechnicalCenterFederalAviationAdministrationGPS2017.
• Figure 4: Our approach to developing localization requirements for autonomous vehicles. We derive system integrity risk allocation based on a target level of safety. This risk budget is then distributed throughout the autonomous vehicle system, following methodologies developed in civil aviation. We define the geometry of the problem to establish positioning bounds based on vehicle dimensions and road geometry. Combining these defines the desired distribution of our position errors and the localization requirements.
• Figure 5: Relationship between protection level, alert limit, availability, and different operations.