Multimodal Perception Models for Situational Awareness in Autonomous Vehicle Testing: Model Audit

Abstract

The rapid advancement of autonomous vehicle technology necessitates rigorous evaluation frameworks to ensure safety and reliability in complex, real-world environments. Central to these autonomous systems are multimodal perception models, which synthesize data from diverse sensors such as cameras, light detection and ranging systems, and radio detection and ranging to construct a comprehensive understanding of the vehicle surroundings. Furthermore, the translation of this perceptive data into actionable intelligence is governed by the situational awareness capabilities of the underlying algorithms. This paper presents a comprehensive model audit of multimodal perception models and their resultant situational awareness in autonomous vehicle testing. By systematically dissecting the operational pipeline of these models, the study identifies critical vulnerabilities, failure modes, and performance degradation patterns under adverse conditions. A novel auditing framework is proposed, bridging the gap between raw sensor fusion accuracy and high-level cognitive understanding required for safe navigation. Through extensive analysis utilizing simulated environments and controlled datasets, the research highlights the discrepancies between localized object detection and holistic environmental comprehension. The findings indicate that while modern multimodal architectures exhibit high resilience to single-sensor failures, their capacity to maintain predictive situational awareness rapidly diminishes in edge-case scenarios characterized by high uncertainty. This audit provides essential insights for developers, regulators, and stakeholders, establishing a foundation for more robust, transparent, and accountable autonomous transportation systems.