Deep Learning Based Dynamic Environment Reconstruction for Vehicular ISAC Scenarios

TL;DR

This paper introduces a deep learning framework for reconstructing dynamic vehicular environments using ISAC signals, enabling accurate, real-time perception without additional sensors, crucial for future intelligent transportation systems.

Contribution

The paper presents a novel multistage deep learning approach and a real-world dataset for environment reconstruction using ISAC channels, improving accuracy and temporal consistency.

Findings

Achieved a Chamfer Distance of 0.29 in environment reconstruction

F Score@1% of 0.87 indicating high accuracy

Demonstrated real-time applicability and efficiency

Abstract

Integrated Sensing and Communication (ISAC) technology plays a critical role in future intelligent transportation systems, by enabling vehicles to perceive and reconstruct the surrounding environment through reuse of wireless signals, thereby reducing or even eliminating the need for additional sensors such as LiDAR or radar. However, existing ISAC based reconstruction methods often lack the ability to track dynamic scenes with sufficient accuracy and temporal consistency, limiting the real world applicability. To address this limitation, we propose a deep learning based framework for vehicular environment reconstruction by using ISAC channels. We first establish a joint channel environment dataset based on multi modal measurements from real world urban street scenarios. Then, a multistage deep learning network is developed to reconstruct the environment. Specifically, a scene decoder identifies the environmental context such as buildings, trees and so on; a cluster center decoder predicts coarse spatial layouts by localizing dominant scattering centers; a point cloud decoder recovers fine grained geometry and structure of surrounding environments. Experimental results demonstrate that the proposed method achieves high-quality dynamic environment reconstruction with a Chamfer Distance of 0.29 and F Score@1% of 0.87. In addition, complexity analysis demonstrates the efficiency and practical applicability of the method in real time scenarios. This work provides a pathway toward low cost environment reconstruction based on ISAC for future intelligent transportation.