Work

VI-Planning: Infrastructure-Assisted Real-Time Planning Optimization for Autonomous Driving

Year: 2025

Type: conference-paper

Abstract: Infrastructure-assisted autonomous driving has emerged as a pivotal technology to overcome the challenges posed by occlusions and limited fields of view for individual vehicles. Vehicles can fuse perception information from the infrastructure with their own in real-time, thereby enhancing their perception ability. However, our real-world experiments demonstrate that such an approach could introduce artifacts such as ghost objects, resulting in unsafe and unreliable planning outcomes. Besides, the system integration complexity and communication overhead are typically considerable, posing challenges to practical deployment. Therefore, we propose VI-Planning, an innovative infrastructure-assisted system that effectively optimizes autonomous vehicle planning in real time. The core idea of VI-Planning is to leverage the scene-level future occupancy grid maps constructed by the infrastructure as future drivable area references to directly optimize planning outcomes of autonomous vehicles. Since VI-Planning operates only at the autonomous vehicle's final output stage, without modifying the vehicle's underlying system architecture, it can be plug-and-play for most autonomous driving systems, whether they are modular or end-to-end architectures. Moreover, VI-Planning employs a novel bitwise encoding mechanism to efficiently compress these maps, enabling practical transmission. We implement VI-Planning end-to-end on a real-world testbed. The results of closed-loop and open-loop experiments indicate that VI-Planning can achieve real-time planning optimization (62.54 ms on average) and 817 × data transmission efficiency compared to the state-of-the-art baseline. A video demo of VI-Planning on our real-world testbed is available at: https://youtu.be/DXl5BhDEvFQ.

Source: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

DOI: 10.1145/3680207.3765255