# People as Sensors: Imputing Maps from Human Actions

arXiv: https://arxiv.org/abs/1711.01022 · HTML: https://mlsanity.com/paper/1711.01022/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2017.

## Built on by (most cited first, 7 of 7)

- [ReasonNet: End-to-End Driving with Temporal and Global Reasoning](https://mlsanity.com/paper/2305.10507/) (2023)
- [Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments](https://mlsanity.com/paper/2309.13893/) (2023)
- [Multi-Agent Variational Occlusion Inference Using People as Sensors](https://mlsanity.com/paper/2109.02173/) (2021)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [Social Occlusion Inference with Vectorized Representation for Autonomous Driving](https://mlsanity.com/paper/2303.10385/) (2023)
- [BlindSpotNet: Seeing Where We Cannot See](https://mlsanity.com/paper/2207.03870/) (2022)
- [Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps](https://mlsanity.com/paper/2303.07227/) (2023)

## Abstract (arXiv)

Despite growing attention in autonomy, there are still many open problems, including how autonomous vehicles will interact and communicate with other agents, such as human drivers and pedestrians. Unlike most approaches that focus on pedestrian detection and planning for collision avoidance, this paper considers modeling the interaction between human drivers and pedestrians and how it might influence map estimation, as a proxy for detection. We take a mapping inspired approach and incorporate people as sensors into mapping frameworks. By taking advantage of other agents' actions, we demonstrate how we can impute portions of the map that would otherwise be occluded. We evaluate our framework in human driving experiments and on real-world data, using occupancy grids and landmark-based mapping approaches. Our approach significantly improves overall environment awareness and out-performs standard mapping techniques.