# I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames

arXiv: https://arxiv.org/abs/2109.09807 · HTML: https://mlsanity.com/paper/2109.09807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 13 of 13)

- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems](https://mlsanity.com/paper/2005.02979/) (2020)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques](https://mlsanity.com/paper/1605.04965/) (2016)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception](https://mlsanity.com/paper/2105.08169/) (2021)
- [Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning](https://mlsanity.com/paper/2004.04450/) (2020)
- [Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/1809.04629/) (2018)
- [Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers](https://mlsanity.com/paper/2008.07303/) (2020)
- [Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification](https://mlsanity.com/paper/1807.01262/) (2018)
- [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018)
- [Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles](https://mlsanity.com/paper/1904.10158/) (2019)
- [Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving](https://mlsanity.com/paper/2009.10033/) (2020)

## Built on by (most cited first, 3 of 3)

- [Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms](https://mlsanity.com/paper/2210.16575/) (2022)
- [A Survey on Hypergame Theory: Modelling Misaligned Perceptions and Nested Beliefs for Multi-Agent Systems](https://mlsanity.com/paper/2507.19593/) (2025)
- [Planning Autonomous Vehicle Maneuvering in Work Zones Through Game-Theoretic Trajectory Generation](https://mlsanity.com/paper/2603.19556/) (2026)

## Abstract (arXiv)

A particular challenge for both autonomous and human driving is dealing with risk associated with dynamic occlusion, i.e., occlusion caused by other vehicles in traffic. Based on the theory of hypergames, we develop a novel multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk in dynamic occlusion scenarios. Furthermore, we present a white-box, scenario-based, accelerated safety validation framework for assessing safety of strategic planners in AV. Based on evaluation over a large naturalistic database, our proposed validation method achieves a 4000% speedup compared to direct validation on naturalistic data, a more diverse coverage, and ability to generalize beyond the dataset and generate commonly observed dynamic occlusion crashes in traffic in an automated manner.