Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles

Abstract

Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental understanding. This paper first formalizes multi-sensor fusion strategies into data-level, feature-level, and decision-level categories and then provides a systematic review of deep learning-based methods corresponding to each strategy. We present key multi-modal datasets and discuss their applicability in addressing real-world challenges, particularly in adverse weather conditions and complex urban environments. Additionally, we explore emerging trends, including the integration of Vision-Language Models (VLMs), Large Language Models (LLMs), and the role of sensor fusion in end-to-end autonomous driving, highlighting its potential to enhance system adaptability and robustness. Our work offers valuable insights into current methods and future directions for multi-sensor fusion in autonomous driving.

arXiv commentAccepted by IEEE IV 2025

References in corpus (4)

• 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
• CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
• RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles
• Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction

Cited by in corpus (1)

• HeCoFuse: Cross-Modal Complementary V2X Cooperative Perception with Heterogeneous Sensors