# Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments

arXiv: https://arxiv.org/abs/2110.01251 · HTML: https://mlsanity.com/paper/2110.01251/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 2 of 2)

- [Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach](https://mlsanity.com/paper/1809.05845/) (2018)
- [Optimized sensor placement for dependable roadside infrastructures](https://mlsanity.com/paper/1910.05314/) (2019)

## Built on by (most cited first, 3 of 3)

- [CoPEM: Cooperative Perception Error Models for Autonomous Driving](https://mlsanity.com/paper/2211.11175/) (2022)
- [Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement](https://mlsanity.com/paper/2211.06308/) (2022)
- [Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors](https://mlsanity.com/paper/2402.00467/) (2024)

## Abstract (arXiv)

Vehicles with driving automation are increasingly being developed for deployment across the world. However, the onboard sensing and perception capabilities of such automated or autonomous vehicles (AV) may not be sufficient to ensure safety under all scenarios and contexts. Infrastructure-augmented environment perception using roadside infrastructure sensors can be considered as an effective solution, at least for selected regions of interest such as urban road intersections or curved roads that present occlusions to the AV. However, they incur significant costs for procurement, installation and maintenance. Therefore these sensors must be placed strategically and optimally to yield maximum benefits in terms of the overall safety of road users. In this paper, we propose a novel methodology towards obtaining an optimal placement of V2X (Vehicle-to-everything) infrastructure sensors, which is particularly attractive to urban AV deployments, with various considerations including costs, coverage and redundancy. We combine the latest advances made in raycasting and linear optimization literature to deliver a tool for urban city planners, traffic analysis and AV deployment operators. Through experimental evaluation in representative environments, we prove the benefits and practicality of our approach.