CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs

Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausible-but-false hypotheses under near-identical counterfactual scenes. We present CCTVBench, a Contrastive Consistency Traffic VideoQA Benchmark built on paired real accident videos and world-model-generated counterfactual counterparts, together with minimally different, mutually exclusive hypothesis questions. CCTVBench enforces a single structured decision pattern over each video question quadruple and provides actionable diagnostics that decompose failures into positive omission, positive swap, negative hallucination, and mutual-exclusivity violation, while separating video versus question consistency. Experiments across open-source and proprietary video LLMs reveal a large and persistent gap between standard per-instance QA metrics and quadruple-level contrastive consistency, with unreliable none-of-the-above rejection as a key bottleneck. Finally, we introduce C-TCD, a contrastive decoding approach leveraging a semantically exclusive counterpart video as the contrast input at inference time, improving both instance-level QA and contrastive consistency.

From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving

Simulation is one of the most essential parts in the development stage of automotive software. However, purely virtual simulations often struggle to accurately capture all real-world factors due to limitations in modeling. To address this challenge, this work presents a test framework for automotive software on the centralized E/E architecture, which is a central car server in our case, based on Vehicle-in-the-Loop (ViL) and digital twin technology. The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment. Our approach provides a safe, reproducible, realistic, and cost-effective platform for validating autonomous driving algorithms with a centralized architecture. This test method eliminates the need to test individual physical ECUs and their communication protocols separately. In contrast to traditional ViL methods, the proposed framework runs the full autonomous driving software directly on the vehicle hardware after the simulation process, eliminating flashing and intermediate layers while enabling seamless virtual-physical integration and accurately reflecting centralized E/E behavior. In addition, incorporating mixed testing in both simulated and physical environments reduces the need for full hardware integration during the early stages of automotive development. Experimental case studies demonstrate the effectiveness of the framework in different test scenarios. These findings highlight the potential to reduce development and integration efforts for testing autonomous driving pipelines in the future.

RECSIP: REpeated Clustering of Scores Improving the Precision

The latest research on Large Language Models (LLMs) has demonstrated significant advancement in the field of Natural Language Processing (NLP). However, despite this progress, there is still a lack of reliability in these models. This is due to the stochastic architecture of LLMs, which presents a challenge for users attempting to ascertain the reliability of a model's response. These responses may cause serious harm in high-risk environments or expensive failures in industrial contexts. Therefore, we introduce the framework REpeated Clustering of Scores Improving the Precision (RECSIP) which focuses on improving the precision of LLMs by asking multiple models in parallel, scoring and clustering their responses to ensure a higher reliability on the response. The evaluation of our reference implementation recsip on the benchmark MMLU-Pro using the models GPT-4o, Claude and Gemini shows an overall increase of 5.8 per cent points compared to the best used model.