# Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification

arXiv: https://arxiv.org/abs/1807.01262 · HTML: https://mlsanity.com/paper/1807.01262/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception](https://mlsanity.com/paper/2105.08169/) (2021)
- [Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning](https://mlsanity.com/paper/2107.07316/) (2021)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models](https://mlsanity.com/paper/2210.14584/) (2022)
- [Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving](https://mlsanity.com/paper/2107.02326/) (2021)
- [I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames](https://mlsanity.com/paper/2109.09807/) (2021)
- [Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety](https://mlsanity.com/paper/2402.01507/) (2024)
- [From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving](https://mlsanity.com/paper/2504.01408/) (2025)
- [Integrating occlusion awareness in urban motion prediction for enhanced autonomous vehicle navigation](https://mlsanity.com/paper/2406.19798/) (2024)
- [What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models](https://mlsanity.com/paper/2607.00283/) (2026)
- [Hope for the Best, Prepare for the Worst: Occlusion-Aware Contingency Planning for Autonomous Vehicles](https://mlsanity.com/paper/2607.03155/) (2026)

## Abstract (arXiv)

Provable safety is one of the most critical challenges in automated driving. The behavior of numerous traffic participants in a scene cannot be predicted reliably due to complex interdependencies and the indiscriminate behavior of humans. Additionally, we face high uncertainties and only incomplete environment knowledge. Recent approaches minimize risk with probabilistic and machine learning methods - even under occlusions. These generate comfortable behavior with good traffic flow, but cannot guarantee safety of their maneuvers. Therefore, we contribute a safety verification method for trajectories under occlusions. The field-of-view of the ego vehicle and a map are used to identify critical sensing field edges, each representing a potentially hidden obstacle. The state of occluded obstacles is unknown, but can be over-approximated by intervals over all possible states. Then set-based methods are extended to provide occupancy predictions for obstacles with state intervals. The proposed method can verify the safety of given trajectories (e.g. if they ensure collision-free fail-safe maneuver options) w.r.t. arbitrary safe-state formulations. The potential for provably safe trajectory planning is shown in three evaluative scenarios.