Researchers have developed a novel Neuro-Memory Fuzzy Inference System (NeMeFIS) designed to mimic human-like car following behavior in vehicles. This hierarchical machine learning architecture differentiates between acceleration and deceleration, incorporating five types of human memory: procedural, working, episodic, semantic, and declarative. NeMeFIS models cognitive influences on driving across various road types and vehicle conditions, outperforming traditional methods like linear regression and LSTM. The system's analysis indicates that declarative memory plays a significant role in complex braking decisions, while procedural memory governs acceleration, with risk perception being a key factor in urban driving. AI
IMPACT This research could lead to more intuitive and safer autonomous driving systems by better replicating human decision-making processes.
RANK_REASON The cluster contains a research paper detailing a new AI system. [lever_c_demoted from research: ic=1 ai=1.0]
- Adaptive neuro fuzzy inference system
- arterial hypertension
- collector
- Connected and Automated Vehicles Symposium
- connected and autonomous vehicles
- linear regression
- long short-term memory
- NeMeFIS
- Neuro-Memory Fuzzy Inference System
- Rural Highways
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