# Towards robust sensing for Autonomous Vehicles: An adversarial perspective

arXiv: https://arxiv.org/abs/2007.10115 · HTML: https://mlsanity.com/paper/2007.10115/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

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

- [Robustness and invariance properties of image classifiers](https://mlsanity.com/paper/2209.02408/) (2022)
- [Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense](https://mlsanity.com/paper/2507.03427/) (2025)
- [Enhanced Automotive Object Detection via RGB-D Fusion in a DiffusionDet Framework](https://mlsanity.com/paper/2406.03129/) (2024)
- [Enhancing Self-Driving Segmentation in Adverse Weather Conditions: A Dual Uncertainty-Aware Training Approach to SAM Optimization](https://mlsanity.com/paper/2509.04735/) (2025)
- [Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks](https://mlsanity.com/paper/2509.17987/) (2025)
- [Robust Processing and Learning: Principles, Methods, and Wireless Applications](https://mlsanity.com/paper/2602.09848/) (2026)
- [Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors](https://mlsanity.com/paper/2606.29963/) (2026)

## Abstract (arXiv)

Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions. Fundamental building blocks of such systems are sensors and classifiers that process ultrasound, RADAR, GPS, LiDAR and camera signals~citeKhan2018. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations, and can even be adversarial perturbations (APs). Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements, with the objective of attacking and defeating the autonomous systems. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems in the fast-evolving domain of AVs. To this end, we survey the emerging field of sensing in adversarial settings: after reviewing adversarial attacks on sensing modalities for autonomous systems, we discuss countermeasures and present future research directions.