A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions
Authors: Ji Zhou, Yilin Ding, Yongqi Zhao, Jiachen Xu, Dong Bi, Johannes Betz, Arno Eichberger
Organizations: Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria · Independent Researcher, 200000 Shanghai, China · School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, 442002 Shiyan, China · Professorship of Autonomous Vehicle Systems, Technical University of Munich, 85748 Garching, Germany
Reliable environmental perception remains one of the main obstacles for safe operation of automated vehicles. Safety of the Intended Functionality (SOTIF) concerns safety risks from perception insufficiencies, particularly under adverse conditions where conventional detectors often falter. While Large Vision-Language Models (LVLMs) demonstrate promising semantic reasoning, their quantitative effectiveness for safety-critical 2D object detection is underexplored. This paper presents a systematic evaluation of ten representative LVLMs using the PeSOTIF dataset, a benchmark specifically curated for long-tail traffic scenarios and environmental degradations. Performance is quantitatively compared against two specialized detectors: the anchor-based YOLOv5 and the transformer-based RT-DETRv4. Experimental results reveal a critical trade-off: top-performing LVLMs (e.g., Gemini 3) surpass the YOLOv5 in recall by over 25% and closely match RT-DETRv4 under natural visual degradation, while specialized detectors retain an advantage in geometric precision for handcrafted perturbations. These findings highlight the complementary strengths of semantic reasoning versus geometric regression, supporting the use of LVLMs as high-level safety validators in SOTIF-oriented automated driving systems.