# On the Assessment of Sensitivity of Autonomous Vehicle Perception

arXiv: https://arxiv.org/abs/2602.00314 · HTML: https://mlsanity.com/paper/2602.00314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving](https://mlsanity.com/paper/1904.04620/) (2019)
- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [Synthetic Datasets for Autonomous Driving: A Survey](https://mlsanity.com/paper/2304.12205/) (2023)
- [Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.04448/) (2022)
- [Reconstructing Objects in-the-wild for Realistic Sensor Simulation](https://mlsanity.com/paper/2311.05602/) (2023)
- [Adversarial Attacks on Traffic Sign Recognition: A Survey](https://mlsanity.com/paper/2307.08278/) (2023)
- [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024)
- [Data generation using simulation technology to improve perception mechanism of autonomous vehicles](https://mlsanity.com/paper/2207.00191/) (2022)
- [Bridging the Domain Gap between Synthetic and Real-World Data for Autonomous Driving](https://mlsanity.com/paper/2306.02631/) (2023)
- [Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots](https://mlsanity.com/paper/1710.06230/) (2017)
- [AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features](https://mlsanity.com/paper/2501.03700/) (2025)

## Built on by (most cited first, 1 of 1)

- [Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption](https://mlsanity.com/paper/2607.19146/) (2026)

## Abstract (arXiv)

The viability of automated driving is heavily dependent on the performance of perception systems to provide real-time accurate and reliable information for robust decision-making and maneuvers. These systems must perform reliably not only under ideal conditions, but also when challenged by natural and adversarial driving factors. Both of these types of interference can lead to perception errors and delays in detection and classification. Hence, it is essential to assess the robustness of the perception systems of automated vehicles (AVs) and explore strategies for making perception more reliable. We approach this problem by evaluating perception performance using predictive sensitivity quantification based on an ensemble of models, capturing model disagreement and inference variability across multiple models, under adverse driving scenarios in both simulated environments and real-world conditions. A notional architecture for assessing perception performance is proposed. A perception assessment criterion is developed based on an AV's stopping distance at a stop sign on varying road surfaces, such as dry and wet asphalt, and vehicle speed. Five state-of-the-art computer vision models are used, including YOLO (v8-v9), DEtection TRansformer (DETR50, DETR101), Real-Time DEtection TRansformer (RT-DETR)in our experiments. Diminished lighting conditions, e.g., resulting from the presence of fog and low sun altitude, have the greatest impact on the performance of the perception models. Additionally, adversarial road conditions such as occlusions of roadway objects increase perception sensitivity and model performance drops when faced with a combination of adversarial road conditions and inclement weather conditions. Also, it is demonstrated that the greater the distance to a roadway object, the greater the impact on perception performance, hence diminished perception robustness.