Benchmarking Prompt Extension and Image Generation Models for Automotive Instructional Imagery

Generative image models can support instructional visual content, but safety-relevant automotive imagery requires more than photorealism: generated scenes must preserve cockpit geometry, human-machine-interface (HMI) iconography, and physically plausible vehicle behavior. We evaluate a two-stage pipeline in which large language models (LLMs) extend technical owner-manual descriptions and reference-image constraints into downstream image-generation prompts, across four Cadillac LYRIQ advanced driver assistance system (ADAS) scenarios. Using a factorial design over prompt-extension LLM, thinking mode, injected Chain-of-Thought (CoT) source, prompt style, scenario, and image generator, we collect N = 18, 551 manually rated images (Excellent/Good/Fair/Poor). The downstream generator was the dominant quality determinant: Nano Banana Pro and Nano Banana 2 produced 16.9% and 14.3% usable images, whereas GPT-Image-1.5 produced 0.5% and collapsed to near-uniform failure. Within the viable Google generators, LLM and CoT effects were generator- and scenario-dependent; because both viable generators are Google, the CoT-source effect reflects trace–generator alignment (Google-aligned CoT helped, OpenAI-sourced CoT hurt) rather than a general CoT benefit. A robustness analysis further reveals a trade-off between high-performing specialist and lower-variance generalist configurations. Domain-constrained instructional image generation should therefore be evaluated as a coupled system spanning generator, prompt-extension model, reasoning trace, prompt template, and scenario.