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Qingguo Zhou

4 accepted papers

2026

KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning

ICML 2026poster

Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to combine the learning capabilities of neural network with the reasoning transparency of fuzzy logic. However, conventional ANFIS architectures suffer from structural complexity, where the product-based inference mechanism causes an exponen…

Cited by 0SourceScholar
2026

Reasoning-VLA: An Efficient and Spatial-Guided General Vision-Language-Action Reasoning Model for Autonomous Driving

ICML 2026poster

Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient inference and generalizing to novel autonomous vehicle configurations and driving scenarios. In this paper, we propose Rea…

Cited by 0SourceScholar
2025

CoC-VLA: Delving into Adversarial Domain Transfer for Explainable Autonomous Driving via Chain-of-Causality Visual-Language-Action Model

NeurIPS 2025poster

Autonomous driving represents a prominent application of artificial intelligence. Recent approaches have shifted from focusing solely on common scenarios to addressing complex, long-tail situations such as subtle human behaviors, traffic accidents, and non-compliant driving patterns. Given the demon…

Cited by 0SourceScholar
2025

MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert

AAAI 2025technical

Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of dif…

Cited by 1SourcePDFScholar