Deep learning on real-world EEG data achieves 90.7% accuracy in predicting driver intentions up to one second before maneuvers, with best performance from the TSCeption model.
Multi-scenario highway lane-change intention prediction: A physics-informed ai framework for three-class classification
2 Pith papers cite this work. Polarity classification is still indexing.
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EVLA combines a Unified Co-State Encoder and Electro-aware Structured Reasoning Chain with physics-guided training to produce energy-optimal driving decisions, reporting +5.6% accuracy gains over fine-tuned VLM baselines on a driving QA benchmark.
citing papers explorer
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Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Deep learning on real-world EEG data achieves 90.7% accuracy in predicting driver intentions up to one second before maneuvers, with best performance from the TSCeption model.
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EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control
EVLA combines a Unified Co-State Encoder and Electro-aware Structured Reasoning Chain with physics-guided training to produce energy-optimal driving decisions, reporting +5.6% accuracy gains over fine-tuned VLM baselines on a driving QA benchmark.