# Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors

arXiv: https://arxiv.org/abs/2402.00467 · HTML: https://mlsanity.com/paper/2402.00467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving](https://mlsanity.com/paper/2105.00373/) (2021)
- [A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop](https://mlsanity.com/paper/1708.01104/) (2017)
- [Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments](https://mlsanity.com/paper/2110.01251/) (2021)
- [Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement](https://mlsanity.com/paper/2211.06308/) (2022)

## Abstract (arXiv)

In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications. In comparison to previous methods that rely on geometric approximations, our presented approach provides more realistic coverage estimates by utilizing accurate and detailed 3D simulation environments. Our method leverages point clouds from LiDAR sensors or camera depth images from high-fidelity simulations of target scenarios to provide accurate and actionable visibility estimates. A Monte Carlo-based reference sensor simulation enables us to accurately estimate blind spot size as a metric of coverage, as well as detection probabilities of objects at arbitrary positions.