International Journal on Science and Technology

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Toward Agentic Driver Intent Prediction from Smartphone Inertial Sensors: A Conceptual Framework for Future Study

Author(s) Gaurav Pokharkar
Country United States
Abstract Predicting driver intent, such as braking, lane changes, and turns, before execution remains a critical challenge for advanced driver assistance systems (ADAS). Existing approaches rely on expensive dedicated hardware (cameras, LiDAR, CAN bus) and generic models that fail to capture individual driving signatures. This paper presents an agentic framework for driver intent prediction using only smartphone inertial measurement unit (IMU) sensors, specifically accelerometer and gyroscope data. We introduce a three-tier agent architecture consisting of a Signal Processing Agent for extracting pre-motion micro-patterns, a Driver Profiling Agent that maintains personalized driving behavior models through continual learning, and a Prediction Agent that fuses contextual features with driver-specific signatures to anticipate maneuvers before execution. To address the fundamental signal-to-noise ratio challenge inherent in weak pre-motion signals, we propose a wavelet-enhanced temporal attention network (WE-TAN) that selectively amplifies discriminative micro-patterns while suppressing sensor noise. No new data collection, model training, or quantitative testing is reported in the present paper. Instead, the contribution is a conceptual research framework and a detailed future validation pipeline covering data collection, labeling, model training, baseline comparison, robustness analysis, and simulation-in-the-loop evaluation. The goal is to provide a credible roadmap for how smartphone-only personalized intent prediction can be studied rigorously in future work.
Keywords Driver intent prediction, smartphone sensors, agentic AI, personalized driving model, lane change prediction, braking prediction
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-28
DOI https://doi.org/10.71097/IJSAT.v17.i2.11402
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Crossref DOI prefix of IJSAT is 10.71097/IJSAT

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