Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous work on end-to-end autonomous driving relies on the attention mechanism to handle heterogeneous interactions, which fails to capture geometric priors and is also computationally intensive. In this paper, we propose the Interaction Scene Graph (ISG) as a unified method to model the interactions among the ego-vehicle, road agents, and map elements. With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow. Since a mass of unnecessary interactions are omitted, the more efficient scene-graph-based framework is able to focus on indispensable connections and leads to better performance. We evaluate the proposed method for end-to-end autonomous driving on the nuScenes dataset. Compared with strong baselines, our method significantly outperforms in full-stack driving tasks.

Fiducial marker systems are composed of a number of patterns that are mounted in the environment and automatically detected by computer vision algorithms using digital techniques. Thus, these systems are valuable in augmented reality (AR), robot navigation, and other applications. This paper proposes a new AR marker called CH-marker, which uses Hamming check codes to encode multiple kinds of colors and restore binary codes in the squares occluded in the markers. The marker solves the registration failure, which occurs when the markers are partially occluded in dynamic scenes. Experiments showed that the proposed marker is effective, reliable, and can meet the application demand of AR.