# CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development

arXiv: https://arxiv.org/abs/2504.03989 · HTML: https://mlsanity.com/paper/2504.03989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 82 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 30 of 34)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022)
- [SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout](https://mlsanity.com/paper/2412.12129/) (2024)
- [Scenic: A Language for Scenario Specification and Data Generation](https://mlsanity.com/paper/2010.06580/) (2020)
- [Towards Corner Case Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09184/) (2019)
- [DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search](https://mlsanity.com/paper/2107.06997/) (2021)
- [Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2102.03483/) (2021)
- [Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving](https://mlsanity.com/paper/2409.18343/) (2024)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing](https://mlsanity.com/paper/2211.01829/) (2022)
- [Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors](https://mlsanity.com/paper/2408.03200/) (2024)
- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning](https://mlsanity.com/paper/2107.11762/) (2021)
- [A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety](https://mlsanity.com/paper/1904.02697/) (2019)
- [UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarios](https://mlsanity.com/paper/2405.03807/) (2024)
- [AmbieGen: A Search-based Framework for Autonomous Systems Testing](https://mlsanity.com/paper/2301.01234/) (2023)
- [Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles](https://mlsanity.com/paper/2003.01886/) (2020)
- [Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL](https://mlsanity.com/paper/2109.07960/) (2021)
- [Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios](https://mlsanity.com/paper/2305.14146/) (2023)
- [RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow](https://mlsanity.com/paper/2211.03408/) (2022)
- [Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)](https://mlsanity.com/paper/2003.10662/) (2020)
- [Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresight](https://mlsanity.com/paper/2104.03019/) (2021)
- [Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments](https://mlsanity.com/paper/2107.09614/) (2021)
- [Autonomous Vehicle: Security by Design](https://mlsanity.com/paper/1810.00545/) (2018)
- [Anatomy of a Robotaxi Crash: Lessons from the Cruise Pedestrian Dragging Mishap](https://mlsanity.com/paper/2402.06046/) (2024)
- [Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field](https://mlsanity.com/paper/2210.01747/) (2022)
- [Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms](https://mlsanity.com/paper/2210.16575/) (2022)

## Abstract (arXiv)

Autonomous Vehicles (AVs) aim to improve traffic safety and efficiency by reducing human error. However, ensuring AVs reliability and safety is a challenging task when rare, high-risk traffic scenarios are considered. These 'Corner Cases' (CC) scenarios, such as unexpected vehicle maneuvers or sudden pedestrian crossings, must be safely and reliable dealt by AVs during their operations. But they arehard to be efficiently generated. Traditional CC generation relies on costly and risky real-world data acquisition, limiting scalability, and slowing research and development progress. Simulation-based techniques also face challenges, as modeling diverse scenarios and capturing all possible CCs is complex and time-consuming. To address these limitations in CC generation, this research introduces CORTEX-AVD, CORner Case Testing & EXploration for Autonomous Vehicles Development, an open-source framework that integrates the CARLA Simulator and Scenic to automatically generate CC from textual descriptions, increasing the diversity and automation of scenario modeling. Genetic Algorithms (GA) are used to optimize the scenario parameters in six case study scenarios, increasing the occurrence of high-risk events. Unlike previous methods, CORTEX-AVD incorporates a multi-factor fitness function that considers variables such as distance, time, speed, and collision likelihood. Additionally, the study provides a benchmark for comparing GA-based CC generation methods, contributing to a more standardized evaluation of synthetic data generation and scenario assessment. Experimental results demonstrate that the CORTEX-AVD framework significantly increases CC incidence while reducing the proportion of wasted simulations.