# Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

arXiv: https://arxiv.org/abs/2102.03483 · HTML: https://mlsanity.com/paper/2102.03483/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 24 of 24)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [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)
- [NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/1707.03501/) (2017)
- [Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques](https://mlsanity.com/paper/1605.04965/) (2016)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology](https://mlsanity.com/paper/1905.03419/) (2019)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies](https://mlsanity.com/paper/1905.03428/) (2019)
- [Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles](https://mlsanity.com/paper/2003.01886/) (2020)
- [Standard detectors aren't (currently) fooled by physical adversarial stop signs](https://mlsanity.com/paper/1710.03337/) (2017)
- [Model Predictive Instantaneous Safety Metric for Evaluation of Automated Driving Systems](https://mlsanity.com/paper/2005.09999/) (2020)
- [Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing](https://mlsanity.com/paper/2101.02828/) (2021)

## Built on by (most cited first, 27 of 27)

- [A Survey on Model-based Reinforcement Learning](https://mlsanity.com/paper/2206.09328/) (2022)
- [Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.09800/) (2025)
- [Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors](https://mlsanity.com/paper/2408.03200/) (2024)
- [Testing of Deep Reinforcement Learning Agents with Surrogate Models](https://mlsanity.com/paper/2305.12751/) (2023)
- [From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios](https://mlsanity.com/paper/2502.02145/) (2025)
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner](https://mlsanity.com/paper/2412.18086/) (2024)
- [A-Eye: Driving with the Eyes of AI for Corner Case Generation](https://mlsanity.com/paper/2202.10803/) (2022)
- [Sim2Real Diffusion: Leveraging Foundation Vision Language Models for Adaptive Automated Driving](https://mlsanity.com/paper/2507.00236/) (2025)
- [Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge](https://mlsanity.com/paper/2103.10245/) (2021)
- [CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs](https://mlsanity.com/paper/2309.09844/) (2023)
- [Continual Driving Policy Optimization with Closed-Loop Individualized Curricula](https://mlsanity.com/paper/2309.14209/) (2023)
- [VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2503.23046/) (2025)
- [Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving](https://mlsanity.com/paper/2309.14235/) (2023)
- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening](https://mlsanity.com/paper/2411.16996/) (2024)
- [AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models](https://mlsanity.com/paper/2503.20804/) (2025)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios](https://mlsanity.com/paper/2503.23708/) (2025)
- [An NCAP-like Safety Indicator for Self-Driving Cars](https://mlsanity.com/paper/2104.00859/) (2021)
- [Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs](https://mlsanity.com/paper/2308.13658/) (2023)
- [Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Framework](https://mlsanity.com/paper/2406.02865/) (2024)
- [GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation](https://mlsanity.com/paper/2410.13514/) (2024)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)
- [ROMAN: Reward-Orchestrated Multi-Head Attention Network for Autonomous Driving System Testing](https://mlsanity.com/paper/2602.05629/) (2026)
- [OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge](https://mlsanity.com/paper/2604.20423/) (2026)

## Abstract (arXiv)

Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.