What type of study is this?

This is a Experimental Study study.

October 17, 2025Frontiers in Artificial IntelligenceOpen Access

Situational perception in distracted driving: an agentic multi-modal LLM framework

View Full Paper

Ask AI

Bookmark

The framework achieves a semantic intervention correctness of 85.7%, and response latency of 1.74 seconds.
Integration of camera and GPS inputs supports accurate environmental perception, enhancing driver awareness.
Agentic design allows specialized tools to manage object detection and traffic conditions effectively.
Real-time data synthesis points to the framework's potential for applications in safety-critical environments.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

This proposal demonstrates a high-performing LLM framework for distracted driving alerts, suggesting improved situational perception through context-aware interventions.

Cite This Study

Nazar et al. (2025) studied this question.

synapsesocial.com/papers/68f199ccde32064e504dd109 https://doi.org/10.3389/frai.2025.1669937

View Full Paper

Ask AI

Bookmark

Implication

This proposal demonstrates a high-performing LLM framework for distracted driving alerts, suggesting improved situational perception through context-aware interventions.

Situational perception in distracted driving: an agentic multi-modal LLM framework

Key Points

Discussion

Implication

Cite This Study

Situational perception in distracted driving: an agentic multi-modal LLM framework

Key Points

Discussion

Implication

Cite This Study