# Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety

arXiv: https://arxiv.org/abs/2402.01507 · HTML: https://mlsanity.com/paper/2402.01507/

## 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 5 of its 35 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## 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, 5 of 5)

- [Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception](https://mlsanity.com/paper/2105.08169/) (2021)
- [Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification](https://mlsanity.com/paper/1807.01262/) (2018)
- [Risk Assessment and Planning with Bidirectional Reachability for Autonomous Driving](https://mlsanity.com/paper/1909.08059/) (2019)
- [Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving](https://mlsanity.com/paper/2107.02326/) (2021)
- [Occlusion-aware Risk Assessment and Driving Strategy for Autonomous Vehicles Using Simplified Reachability Quantification](https://mlsanity.com/paper/2306.07004/) (2023)

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

- [From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving](https://mlsanity.com/paper/2504.01408/) (2025)
- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.15099/) (2025)

## Abstract (arXiv)

Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded traffic participants with limited information about surrounding obstacles. To address this problem, we propose a software module that handles blind spots (BS) created by static and dynamic obstacles in urban environments. We identify potential occluded traffic participants, predict their movement, and assess the ego vehicle's trajectory using various criticality metrics. The method offers a straightforward and modular integration into motion planner algorithms. We present critical real-world scenarios to evaluate our module and apply our approach to a publicly available trajectory planning algorithm. Our results demonstrate that safe yet efficient driving with occluded road users can be achieved by incorporating safety assessments into the planning process. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Occlusion.