# Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems

arXiv: https://arxiv.org/abs/2109.06404 · HTML: https://mlsanity.com/paper/2109.06404/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 64 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection](https://mlsanity.com/paper/2004.12636/) (2020)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [Neural Network Attributions: A Causal Perspective](https://mlsanity.com/paper/1902.02302/) (2019)
- [Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method](https://mlsanity.com/paper/2003.01197/) (2020)
- [Adaptive Stress Testing for Autonomous Vehicles](https://mlsanity.com/paper/1902.01909/) (2019)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation](https://mlsanity.com/paper/2009.08311/) (2020)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving](https://mlsanity.com/paper/2101.06784/) (2021)
- [Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios](https://mlsanity.com/paper/2004.06531/) (2020)
- [Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version)](https://mlsanity.com/paper/2006.10318/) (2020)
- [Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies](https://mlsanity.com/paper/2002.12078/) (2020)
- [Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations](https://mlsanity.com/paper/1802.00541/) (2018)
- [Efficient Black-box Assessment of Autonomous Vehicle Safety](https://mlsanity.com/paper/1912.03618/) (2019)
- [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles](https://mlsanity.com/paper/2109.06126/) (2021)
- [Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing](https://mlsanity.com/paper/2106.00873/) (2021)
- [Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems](https://mlsanity.com/paper/2012.08704/) (2020)

## Built on by (most cited first, 8 of 8)

- [Towards Causal Deep Learning for Vulnerability Detection](https://mlsanity.com/paper/2310.07958/) (2023)
- [Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities](https://mlsanity.com/paper/2306.03454/) (2023)
- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)
- [ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation](https://mlsanity.com/paper/2409.07774/) (2024)
- [COMMIT: Certifying Robustness of Multi-Sensor Fusion Systems against Semantic Attacks](https://mlsanity.com/paper/2403.02329/) (2024)
- [Characterization and Mitigation of Insufficiencies in Automated Driving Systems](https://mlsanity.com/paper/2404.09557/) (2024)
- [TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2507.05932/) (2025)
- [When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?](https://mlsanity.com/paper/2509.24927/) (2025)

## Abstract (arXiv)

Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor outputs, they usually leverage multi-sensor fusion (MSF) to fuse the sensor outputs and produce a more reliable understanding of the surroundings. However, MSF cannot completely eliminate the uncertainties since it lacks the knowledge about which sensor provides the most accurate data and how to optimally integrate the data provided by the sensors. As a result, critical consequences might happen unexpectedly. In this work, we observed that the popular MSF methods in an industry-grade ADAS can mislead the car control and result in serious safety hazards. We define the failures (e.g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors. We further apply causality analysis to show that the found fusion errors are indeed caused by the MSF method. We evaluate our framework on two widely used MSF methods in two driving environments. Experimental results show that FusED identifies more than 150 fusion errors. Finally, we provide several suggestions to improve the MSF methods we study.