Visual Grounding Via Heterogeneous Representation Learning and Hierarchical Reasoning of Human-To-Vehicle Commands

Abstract

With the proliferation of autonomous vehicles (AVs) and their increasing interaction and communication with the riders, how to ground or locate the visual objects of interests (OoIs), such as the concerned pedestrians and other traffic participants, based on the human riders’ natural language and communication (e.g., vocal commands), is essential for increasing the efficiency, effectiveness, and reliability/safety of AVs in following the riders’ reasonable commands and preferences. There are several technical challenges to achieve visual grounding for such human-to-vehicle commanding (HVC) scenes, including (1) how to fuse heterogeneous sensor modalities — i.e., visual object information, textual contexts, and situation awareness (say, obtained from the light detection and ranging); (2) how to discern the opaque commands in the human natural language; and (3) how to reason about the relative positions of the OoIs within the visual modality. To meet these challenges, we propose VIGOR, a VIsual Grounding approach based on heterogeneous mOdality learning and hierarchical Reasoning for HVC scenes. First, we design a heterogeneous modality learning approach in order to incorporate the visual, textual, and situational modalities, and learn their cross-modality representations to identify important information for visual grounding. Then, VIGOR performs hierarchical reasoning of objects and context levels, and differentiates the OoIs in the complex traffic environments that relate to the natural language commands. Finally, we conduct extensive experimental studies on a total of 12,037 HVC scenes, demonstrating VIGOR to achieve higher accuracy than the state-of-the-art approaches (by 14.81% on average) in terms of the Intersection over Union (IoU) in grounding the OoIs in the complex (including low-visibility) HVC scenes.