Multi-Agent Variational Occlusion Inference Using People as Sensors (2109.02173v3)

Abstract: Autonomous vehicles must reason about spatial occlusions in urban environments to ensure safety without being overly cautious. Prior work explored occlusion inference from observed social behaviors of road agents, hence treating people as sensors. Inferring occupancy from agent behaviors is an inherently multimodal problem; a driver may behave similarly for different occupancy patterns ahead of them (e.g., a driver may move at constant speed in traffic or on an open road). Past work, however, does not account for this multimodality, thus neglecting to model this source of aleatoric uncertainty in the relationship between driver behaviors and their environment. We propose an occlusion inference method that characterizes observed behaviors of human agents as sensor measurements, and fuses them with those from a standard sensor suite. To capture the aleatoric uncertainty, we train a conditional variational autoencoder with a discrete latent space to learn a multimodal mapping from observed driver trajectories to an occupancy grid representation of the view ahead of the driver. Our method handles multi-agent scenarios, combining measurements from multiple observed drivers using evidential theory to solve the sensor fusion problem. Our approach is validated on a cluttered, real-world intersection, outperforming baselines and demonstrating real-time capable performance. Our code is available at https://github.com/sisl/MultiAgentVariationalOcclusionInference .

• The paper proposes a multi-agent variational occlusion inference framework that leverages human driver behavior ('People as Sensors') via a CVAE.
• The CVAE-driven model consistently outperformed baseline methods, achieving higher top-3 accuracy in inferring occupancy states from human behaviors.
• The research improves autonomous vehicle safety in occluded settings and advances the integration of human behavior modeling into autonomous perception systems.

Multi-Agent Variational Occlusion Inference Using People as Sensors: A Review

The paper proposes an advanced methodology for occlusion inference in autonomous driving systems, utilizing observed human driver behaviors to enhance the environmental map of an ego vehicle. This innovative concept is positioned within the field of autonomous navigation, addressing the persistent challenge of reasoning about occluded spaces and integrating the notion of "People as Sensors" (PaS). The novel method employs a conditional variational autoencoder (CVAE) framework to model the inherent multimodal uncertainty and aleatoric scenarios that arise from interpreting human behaviors, and successfully scales this to multi-agent environments using evidential theory for sensor fusion.

Autonomous vehicles often encounter environments with occluded regions, which pose significant challenges for perception and decision-making processes. Traditional approaches to managing occlusions have included memory-based inference, environmental inpainting, leveraging structure-based occlusion inference, and modeling social interactions. However, these methods have limitations in dynamic and cluttered urban settings due to assumptions of static backgrounds or worst-case scenarios.

The paper outlines a novel framework capturing multimodal possibilities inherent in driver sensor data using a CVAE architecture with a discrete latent space. The CVAE models the uncertainty associated with driver trajectories, mapping these behaviors to an occupancy grid representing the anticipated environmental state ahead of the driver. In terms of performance, the CVAE-driven model presented in this research outperformed baseline methods like k-means and Gaussian mixture models (GMM) based PaS approaches by consistently achieving higher top-3 accuracy in inferring occupancy states across different metrics.

Key numerical results demonstrate the efficacy of the proposed approach, especially in complex urban intersection scenarios. The multimodal metrics indicate robust handling of scenarios with varying spatial occupancy patterns, showcasing the CVAE’s ability to encode multimodal distributions representative of real-world driving. The sensor fusion process based on Dempster-Shafer theory further enables the aggregation of multiple driver sensors, enhancing the ego vehicle's mapping capability and promoting safety and operational efficiency.

Practically, this research holds significant implications for developing autonomous systems that are better equipped to anticipate hazards in occluded settings. The multi-agent framework allows for a more comprehensive understanding of urban environments, potentially improving the interactions between autonomous vehicles and human road users. Theoretically, the paper contributes to the ongoing discourse on integrating human behavior modeling with autonomous perception systems and highlights opportunities to extend these techniques by incorporating semantic road data or HD mapping frameworks.

Future research directions may involve optimizing the CVAE's latent space for more nuanced behavior modeling or integrating anomaly detection mechanisms to focus on interactive behaviors. Additionally, the inclusion of high-definition mapping data could enhance semantic reasoning, permitting more informed decision-making when navigating occluded spaces. The paper's findings suggest a promising area of exploration in developing highly responsive and context-aware autonomous systems, positioning PaS and multimodal inference as pivotal advancements in autonomous vehicle navigation and safety technologies.