# Localization Requirements for Autonomous Vehicles

arXiv: https://arxiv.org/abs/1906.01061 · HTML: https://mlsanity.com/paper/1906.01061/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 16 of 16)

- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation](https://mlsanity.com/paper/2211.14651/) (2022)
- [Convolutional Cross-View Pose Estimation](https://mlsanity.com/paper/2303.05915/) (2023)
- [Enhancing Vectorized Map Perception with Historical Rasterized Maps](https://mlsanity.com/paper/2409.00620/) (2024)
- [View Consistent Purification for Accurate Cross-View Localization](https://mlsanity.com/paper/2308.08110/) (2023)
- [Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems](https://mlsanity.com/paper/2206.13883/) (2022)
- [PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers](https://mlsanity.com/paper/2503.02388/) (2025)
- [Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness](https://mlsanity.com/paper/2403.14641/) (2024)
- [D2HDMap: Non-visible Driveline Map Prior for Online Vectorized HD Map Prediction](https://mlsanity.com/paper/2606.20725/) (2026)
- [Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights](https://mlsanity.com/paper/2309.08731/) (2023)
- [Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data](https://mlsanity.com/paper/2503.13896/) (2025)
- [SATMapTR: Satellite Image Enhanced Online HD Map Construction](https://mlsanity.com/paper/2512.11319/) (2025)
- [Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization](https://mlsanity.com/paper/2306.17529/) (2023)
- [A Quantitative Autonomy Quantification Framework for Fully Autonomous Robotic Systems](https://mlsanity.com/paper/2311.01939/) (2023)
- [Camera-Based Localization and Enhanced Normalized Mutual Information](https://mlsanity.com/paper/2412.16137/) (2024)
- [Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2602.07938/) (2026)

## Abstract (arXiv)

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%).