Context Aware Road-user Importance Estimation (iCARE)

TL;DR

This paper introduces iCARE, a novel context-aware architecture for estimating the importance of road-users in driving scenes, leveraging local appearance and global scene context to improve decision-making for autonomous vehicles.

Contribution

The paper presents a new importance estimation model that combines local appearance features with global scene context, and introduces a new annotated dataset for real-world driving scenarios.

Findings

The proposed method outperforms several baselines in importance estimation accuracy.

Incorporating global scene context improves importance prediction for complex intersections.

The dataset provides valuable annotations for critical road-user analysis in real-world driving.

Abstract

Road-users are a critical part of decision-making for both self-driving cars and driver assistance systems. Some road-users, however, are more important for decision-making than others because of their respective intentions, ego vehicle's intention and their effects on each other. In this paper, we propose a novel architecture for road-user importance estimation which takes advantage of the local and global context of the scene. For local context, the model exploits the appearance of the road users (which captures orientation, intention, etc.) and their location relative to ego-vehicle. The global context in our model is defined based on the feature map of the convolutional layer of the module which predicts the future path of the ego-vehicle and contains rich global information of the scene (e.g., infrastructure, road lanes, etc.), as well as the ego vehicle's intention information. Moreover, this paper introduces a new data set of real-world driving, concentrated around inter-sections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.