People as Sensors: Imputing Maps from Human Actions
This work addresses a specific challenge in autonomous driving by improving map estimation through human behavior modeling, offering a domain-specific incremental advance.
The paper tackles the problem of autonomous vehicles interacting with human agents by modeling driver-pedestrian interactions to improve map estimation, demonstrating that using people as sensors can impute occluded map portions and significantly enhance environment awareness.
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.