Robust Cognitive Capability in Autonomous Driving Using Sensor Fusion Techniques: A Survey

Abstract

Autonomous driving has become a prominent topic with the rise of intelligent urban vision in communities. Advancements in automated driving technology play a significant role in the intelligent transportation system. Autonomous vehicles (AVs) rely heavily on sensor technologies as they are responsible for navigating safely through their environment and avoiding obstacles. This paper aims to outline the vital role of sensor fusion in intelligent transportation systems. Sensor fusion is the process of combining data from multiple sensors to obtain more comprehensive measurements and greater cognitive abilities than a single sensor could achieve. By merging data from different sensors, it ensures that driving decisions are based on reliable data, with improved accuracy, reliability, and robustness in AVs. This paper provides a comprehensive review of AV capacity, impacts, planning, technological challenges, and omitted concerns. We used state-of-the-art evaluation tools to check the performance of different sensor fusion algorithms in AVs. This paper will help us to determine our position, direction, the impacts of AVs on society, the need for smart city mobility outcomes, and the way to solve the auto industry challenges in the future. The analysis of AV systems from the perspective of sensor fusion in this research is expected to be beneficial to current and future researchers.