arXiv AI September 2, 2026 By Irem Yoldas, Martim Brand\~ao, Jie Zhang, Odinaldo Rodrigues

LLM-Driven Autonomous Vehicles Inherit Human Driver Biases in Pedestrian Yielding: Results and Implications From A New Benchmark

The paper investigates how large language and visual‑language models used in autonomous vehicles inherit human driver biases when deciding whether to yield to pedestrians. It introduces two new bias‑testing methods—All Else Being Equal and Self‑Consistency tests—to evaluate these models. Results reveal that the models’ yielding decisions are influenced by pedestrian attributes such as gender, ethnicity, religion, disability, age, skin tone, and socio‑economic status, with varying patterns across models.

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