# A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving

arXiv: https://arxiv.org/abs/2106.08372 · HTML: https://mlsanity.com/paper/2106.08372/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Tracking Multiple Vehicles Using a Variational Radar Model](https://mlsanity.com/paper/1711.03799/) (2017)
- [Polarimetric Radar Cross-Sections of Pedestrians at Automotive Radar Frequencies](https://mlsanity.com/paper/1910.13706/) (2019)
- [Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving](https://mlsanity.com/paper/2104.06772/) (2021)
- [A Sensitivity Analysis Approach for Evaluating a Radar Simulation for Virtual Testing of Autonomous Driving Functions](https://mlsanity.com/paper/2008.02725/) (2020)
- [Automatic Generation of Road Geometries to Create Challenging Scenarios for Automated Vehicles Based on the Sensor Setup](https://mlsanity.com/paper/2008.11580/) (2020)

## Built on by (most cited first, 7 of 7)

- [RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation](https://mlsanity.com/paper/2404.18150/) (2024)
- [NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds](https://mlsanity.com/paper/2504.00859/) (2025)
- [Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks](https://mlsanity.com/paper/2308.02632/) (2023)
- [DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments](https://mlsanity.com/paper/2207.09934/) (2022)
- [Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving](https://mlsanity.com/paper/2407.04070/) (2024)
- [A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems](https://mlsanity.com/paper/2509.22379/) (2025)
- [Generative Adversarial Synthesis of Radar Point Cloud Scenes](https://mlsanity.com/paper/2410.13526/) (2024)

## Abstract (arXiv)

With the increasing safety validation requirements for the release of a self-driving car, alternative approaches, such as simulation-based testing, are emerging in addition to conventional real-world testing. In order to rely on virtual tests the employed sensor models have to be validated. For this reason, it is necessary to quantify the discrepancy between simulation and reality in order to determine whether a certain fidelity is sufficient for a desired intended use. There exists no sound method to measure this simulation-to-reality gap of radar perception for autonomous driving. We address this problem by introducing a multi-layered evaluation approach, which consists of a combination of an explicit and an implicit sensor model evaluation. The former directly evaluates the realism of the synthetically generated sensor data, while the latter refers to an evaluation of a downstream target application. In order to demonstrate the method, we evaluated the fidelity of three typical radar model types (ideal, data-driven, ray tracing-based) and their applicability for virtually testing radar-based multi-object tracking. We have shown the effectiveness of the proposed approach in terms of providing an in-depth sensor model assessment that renders existing disparities visible and enables a realistic estimation of the overall model fidelity across different scenarios.