Perception to Motion: The Role of Multi-Sensor Data Fusion in Realizing the Full Potential of Autonomous Vehicles

Multi-source and heterogeneous information fusion (MSHIF) is a critical approach for enhancing the performance of autonomous vehicles (AVs), particularly in environmental perception and decision-making. This review discusses the potential of AVs in reducing carbon emissions and traffic flow through the revolution of transportation systems. Various types of sensors in AV systems are determined in this review. They are cameras, LiDAR, MMW-Radar, and GPS/IMU modules. Multiple fusion algorithms are employed to harness the full potential of the sensors, such as Kalman filtering, particle filters, and Bayesian networks. These sensors significantly enhance the accuracy and reliability of AV operations; however, addressing their inherent challenges and exploring future research directions in the AV domain are essential. AVs require real-time data processing so that rapid decision-making can be made to handle the dynamic environments. It is also crucial to be concerned about the advancements in computational efficiency and algorithmic sophistication. Cybersecurity emerges as another critical concern, given the increasing connectivity of AVs to external networks. Besides that, the integration of blockchain technology is also addressed in this review to enhance security measures and facilitate transparent data sharing among AV stakeholders. Last but not least, ethical considerations surrounding AI-driven decision-making in AVs are also discussed because human safety needs to be prioritized for establishing ethical guidelines. Further studies and development for AVs could focus on sensor fusion techniques, cybersecurity, and ethical frameworks. The advancements will not only enhance the safety and reliability of AV systems but also pave the way for their widespread adoption in future transportation ecosystems.