# "Curse of rarity" for autonomous vehicles

arXiv: https://arxiv.org/abs/2207.02749 · HTML: https://mlsanity.com/paper/2207.02749/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2022.

## Built on by (most cited first, 30 of 63)

- [ReSim: Reliable World Simulation for Autonomous Driving](https://mlsanity.com/paper/2506.09981/) (2025)
- [Centaur: Robust End-to-End Autonomous Driving with Test-Time Training](https://mlsanity.com/paper/2503.11650/) (2025)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.09800/) (2025)
- [Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failures](https://mlsanity.com/paper/2603.01063/) (2026)
- [A Comprehensive Survey on Rare Event Prediction](https://mlsanity.com/paper/2309.11356/) (2023)
- [Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles](https://mlsanity.com/paper/2409.06450/) (2024)
- [The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration](https://mlsanity.com/paper/2401.16634/) (2024)
- [Simulating the Visual World with Artificial Intelligence: A Roadmap](https://mlsanity.com/paper/2511.08585/) (2025)
- [FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality](https://mlsanity.com/paper/2406.02983/) (2024)
- [FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback](https://mlsanity.com/paper/2503.08162/) (2025)
- [Evolution and The Knightian Blindspot of Machine Learning](https://mlsanity.com/paper/2501.13075/) (2025)
- [SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation](https://mlsanity.com/paper/2409.10320/) (2024)
- [World Engine: Towards the Era of Post-Training for Autonomous Driving](https://mlsanity.com/paper/2606.19836/) (2026)
- [Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2505.00972/) (2025)
- [LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2505.00284/) (2025)
- [VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness](https://mlsanity.com/paper/2601.12672/) (2026)
- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation](https://mlsanity.com/paper/2505.03344/) (2025)
- [Scaling Self-Play for End-to-End Driving](https://mlsanity.com/paper/2606.19641/) (2026)
- [Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems](https://mlsanity.com/paper/2403.13869/) (2024)
- [A Unified Probabilistic Approach to Traffic Conflict Detection](https://mlsanity.com/paper/2407.10959/) (2024)
- [HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation](https://mlsanity.com/paper/2503.15049/) (2025)
- [Learning collision risk proactively from naturalistic driving data at scale](https://mlsanity.com/paper/2505.13556/) (2025)
- [A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording](https://mlsanity.com/paper/2507.04529/) (2025)
- [Controllable Collision Scenario Generation via Collision Pattern Prediction](https://mlsanity.com/paper/2510.12206/) (2025)
- [ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving](https://mlsanity.com/paper/2603.15221/) (2026)
- [The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models](https://mlsanity.com/paper/2603.16050/) (2026)
- [Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity](https://mlsanity.com/paper/2402.01795/) (2024)
- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)

## Abstract (arXiv)

In this paper, we reveal that the rarity of safety-critical events in high-dimensional driving environments is the root cause of the safety challenge for autonomous vehicle development. We formulate it as "curse of rarity" (CoR) because it occurs ubiquitously in various safety-critical systems such as medical diagnosis and aerospace systems.