Interaction Graphs for Object Importance Estimation in On-road Driving Videos
This work addresses the challenge of identifying influential objects for real-time decision-making in driving scenarios, which is incremental by building on interaction-based models.
The paper tackles the problem of estimating object importance in on-road driving videos to understand human driving behavior and improve autonomous driving systems, achieving state-of-the-art performance with reduced input and pre-processing requirements.
A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions. Learning to estimate the importance of each object on the driver's real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.