Embodied Interactive Intelligence Towards Autonomous Driving

Abstract

Autonomous driving depends on successful interactions among humans, vehicles, and roads. However, people often lack an understanding of autonomous vehicle (AV) behaviours and decisions. Moreover, AVs have difficulty aligning with human intentions in their interactions. To overcome the obstacles associated with the absence of interactive intelligence, especially in complex and uncertain environments, we introduce the concept of embodied interactive intelligence towards autonomous driving (EIIAD), which establishes representation and learning methods aligned with the physical world, enhancing human–machine integration. Building on this concept, we propose an end-to-end unified constrained vehicle environment interaction (UniCVE) model, which involves the construction of an end-to-end perception–cognition–behaviour closed-loop feedback paradigm and continuous learning through accumulated split driving scenarios. This model realizes interaction cognition through networks designed for pedestrians and vehicles, and it unifies the cognition as a value network of AVs to generate socially compatible behaviours. The UniCVE model is implemented on Dongfeng autonomous buses, which have successfully travelled 22 thousand kilometres and completed 45 thousand navigation tasks in Xiong’an New Area, China, demonstrating its general applicability in various driving scenarios. In addition, we highlight the high-level interactive intelligence of the UniCVE model in selected simulated complex interaction scenarios, demonstrating that it makes AVs more intelligent, more reliable, and more attuned to human relationships. Furthermore, the UniCVE model’s capacity for self-learning and self-growth allows it to infinitely approximate true intelligence, even with limited experience.