Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors
This addresses safety and reliability issues for autonomous vehicles and robotics by improving sensor coverage assessment, though it is incremental as it builds on simulation-based approaches.
The paper tackles the problem of estimating blind spots for autonomous vehicle sensors by introducing a method that uses high-fidelity 3D simulations and Monte Carlo-based reference sensors to provide realistic coverage estimates, resulting in accurate blind spot size and object detection probability metrics.
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.