PRED-RAG: a Predictive Radial Grid for Automotive Radar Multipath - Identification Identification of objects created by the radar multipath phenomenon, with focus on low computational complexity.

Abstract

Automotive radar sensors are crucial for advanced driver assistance systems but are susceptible to the multipath phenomenon, where radio waves reflect multiple times between surfaces, creating false "ghost" objects that can trigger unnecessary safety interventions. Previous work relies on restrictive assumptions about reflection surfaces and environmental conditions, yielding solutions that perform well in specific scenarios but demonstrate limited generalization capabilities in the complex, diverse situations encountered during real-world driving. This thesis addresses the challenge of identifying radar multipath objects in real-time environments, focusing on developing an algorithm that maintains low computational complexity while achieving high accuracy. We established a development and evaluation pipeline using synthetic data together with a simulation framework, enabling data driven development of our algorithm. We propose the PRED-RAG algorithm, a novel approach that utilizes a radial grid structure combined with host motion prediction of static detections for enhanced high-level environment mapping. The algorithm identifies triplets consisting of a ghost object, reflection point and true object, then evaluates them using velocity-based criteria. When compared to a state-of-the-art algorithm, our approach demonstrates superior performance in both accuracy and computational efficiency across various driving scenarios. The PRED-RAG algorithm achieves 94.43% accuracy for high-priority objects compared to 39.26% for the baseline, with significantly better generalization capabilities, particularly in complex environments. The geometric properties employed in the grid-based approach effectively separate ghost objects from true objects while maintaining runtime performance suitable for real-time automotive applications. This work contributes to safer autonomous driving systems by reducing false objects that could lead to unnecessary emergency interventions.