# EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism

arXiv: https://arxiv.org/abs/2312.00207 · HTML: https://mlsanity.com/paper/2312.00207/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 74 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [NEAT: Neural Attention Fields for End-to-End Autonomous Driving](https://mlsanity.com/paper/2109.04456/) (2021)
- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems](https://mlsanity.com/paper/1812.10812/) (2018)
- [Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios](https://mlsanity.com/paper/2004.06531/) (2020)
- [Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2112.11255/) (2021)
- [Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1804.06760/) (2018)
- [Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving](https://mlsanity.com/paper/1810.10193/) (2018)
- [Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving](https://mlsanity.com/paper/1912.03238/) (2019)
- [Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems](https://mlsanity.com/paper/2210.15432/) (2022)
- [LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles](https://mlsanity.com/paper/2208.14656/) (2022)
- [jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics](https://mlsanity.com/paper/1903.02915/) (2019)
- [Evolve the Model Universe of a System Universe](https://mlsanity.com/paper/2309.13342/) (2023)

## Built on by (most cited first, 6 of 6)

- [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025)
- [Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing](https://mlsanity.com/paper/2503.08936/) (2025)
- [Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions](https://mlsanity.com/paper/2512.11887/) (2025)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [IRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models](https://mlsanity.com/paper/2601.23266/) (2026)
- [Real-World Perturbation Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2607.04953/) (2026)

## Abstract (arXiv)

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular, EpiTESTER adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV, EpiTESTER benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors and then calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER, we compare it with a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented) and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four initial environments from CARLA, an open-source simulator for autonomous driving research, and an end-to-end AV controller, Interfuser. Our results show that EpiTESTER achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.