# Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception

arXiv: https://arxiv.org/abs/2105.08169 · HTML: https://mlsanity.com/paper/2105.08169/

## Key facts

- Cited by 23 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 12 of its 49 references that are in the map.
- First posted on arXiv 17 May 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, 12 of 12)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Learning Control Barrier Functions from Expert Demonstrations](https://mlsanity.com/paper/2004.03315/) (2020)
- [Reach-Avoid Problems with Time-Varying Dynamics, Targets and Constraints](https://mlsanity.com/paper/1410.6445/) (2014)
- [Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/1809.04629/) (2018)
- [Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification](https://mlsanity.com/paper/1807.01262/) (2018)
- [Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming](https://mlsanity.com/paper/1807.11553/) (2018)
- [Risk Assessment and Planning with Bidirectional Reachability for Autonomous Driving](https://mlsanity.com/paper/1909.08059/) (2019)

## Built on by (most cited first, 23 of 23)

- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems](https://mlsanity.com/paper/2309.05837/) (2023)
- [Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2307.13929/) (2023)
- [Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination](https://mlsanity.com/paper/2405.00846/) (2024)
- [DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions](https://mlsanity.com/paper/2412.10739/) (2024)
- [SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction](https://mlsanity.com/paper/2110.00843/) (2021)
- [Learning Vision-based Pursuit-Evasion Robot Policies](https://mlsanity.com/paper/2308.16185/) (2023)
- [Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy](https://mlsanity.com/paper/2309.01267/) (2023)
- [CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus](https://mlsanity.com/paper/2502.08169/) (2025)
- [Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models](https://mlsanity.com/paper/2210.14584/) (2022)
- [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)
- [An Intelligent Self-driving Truck System For Highway Transportation](https://mlsanity.com/paper/2112.15304/) (2021)
- [Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety](https://mlsanity.com/paper/2402.01507/) (2024)
- [Data and Knowledge for Overtaking Scenarios in Autonomous Driving](https://mlsanity.com/paper/2305.19421/) (2023)
- [Self-Localized Collaborative Perception](https://mlsanity.com/paper/2406.12712/) (2024)
- [Spatially Visual Perception for End-to-End Robotic Learning](https://mlsanity.com/paper/2411.17458/) (2024)
- [Schr"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation](https://mlsanity.com/paper/2512.21201/) (2025)
- [Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models](https://mlsanity.com/paper/2605.01662/) (2026)
- [RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field](https://mlsanity.com/paper/2305.02493/) (2023)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [Send Less, Perceive More: Masked Quantized Point Cloud Communication for Loss-Tolerant Collaborative Perception](https://mlsanity.com/paper/2602.21667/) (2026)
- [Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.21387/) (2026)
- [G-MARK: Grounded Multi-Agent Reasoning for Cooperative Driving via Knowledge Graphs](https://mlsanity.com/paper/2608.19964/) (2026)

## Abstract (arXiv)

Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents' behaviors to plan safe trajectories. However, as occlusions limit the vehicle's perception ability, reasoning about potential hazards beyond the field of view is one of the most challenging issues in developing autonomous driving systems. This paper introduces a novel analytical approach that poses safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) and an initially hidden traffic participant (pursuer). Due to occlusions, the pursuer's state is initially unknown to the evader and may later be discovered by the vehicle's sensors. The analysis yields optimal strategies for both players as well as the set of initial conditions from which the autonomous vehicle is guaranteed to avoid collisions. We leverage this theoretical result to develop a novel trajectory planning framework for autonomous driving that provides worst-case safety guarantees while minimizing conservativeness by accounting for the vehicle's ability to actively avoid other road users as soon as they are detected in future observations. Our framework is agnostic to the driving environment and suitable for various motion planners. We demonstrate our algorithm on challenging urban and highway driving scenarios using the open-source CARLA simulator.