Evidence-Based Data Fusion for Robust Autonomous Vehicle Perception

Accurate environmental perception is crucial for the safe and efficient navigation of autonomous vehicles. Integrating data from various sensors, such as cameras, radars, and lidars, presents challenges due to differing sensor reliability and environmental conditions. This study introduces a new method for combining data from multiple sensors using evidence theory (Dempster-Shafer), which helps to address uncertainties and conflicting information. By dynamically merging sensor inputs and resolving conflicts, our model improves object detection and classification in uncertain situations. Through extensive simulations and detailed analyses using metrics like Belief (Bel), Plausibility (Pl), Confidence Interval (CI), and Pignistic Probability (P), the study confirms the effectiveness and dependability of this fusion approach for practical autonomous vehicle applications.