Multi-Modal Sensing for Intelligent V2X: A Review of Recent Advances Towards Deployment

The integration of multi-modal sensing and communication systems advances the integrated sensing and communication (ISAC) concept by combining camera, LiDAR, radar, and radio-frequency (RF) data. This approach provides a promising avenue to enhance wireless link reliability and spectral efficiency while reducing end-to-end latency in 6G vehicle-to-everything (V2X) networks. Surveys on sensing and communication integration primarily concentrate on RF technologies. While initial studies have explored the combination of various sensing methods, there is a paucity of comprehensive reviews regarding how such data is utilized to enhance V2X communication performance. Therefore, this review comprehensively analyzes recent research on multi-modal sensing-assisted vehicular networks to enhance communication performance, centered around channel prediction, beam management, mobility management, and multi-task solutions. Since data-centric techniques are the main approach in this field, relevant datasets and simulators supporting data-driven designs are also thoroughly examined. Finally, by critically analyzing current research, the survey highlights unresolved issues and future trends in research cornerstones, method implementation, and system design. It also addresses key deployment challenges—spectrum availability, roadside hardware costs, and privacy-aware learning—thereby charting the course for developing real-world intelligent V2X networks.