SAFE-D: A Spatiotemporal Detection Framework for Abnormal Driving Among Parkinson's Disease-like Drivers

2025-10-20

AI Summary

Existing driving anomaly detection research primarily focuses on transient functional impairments—such as fatigue and distraction—while overlooking progressive driving behavior degradation caused by chronic neurological disorders like Parkinson’s disease (PD). This work proposes the first spatiotemporal joint anomaly detection framework specifically designed for PD-related driving manifestations. Leveraging multimodal vehicle control signals acquired via Logitech G29 and the CARLA simulator, we develop an attention-driven spatiotemporal feature fusion model that adaptively captures subclinical behavioral deviations under physiological variability. Unlike conventional approaches relying solely on overt behavioral metrics, our method enables sensitive early-stage PD driving risk identification. Evaluated across three representative road scenarios, it achieves a mean detection accuracy of 96.8%, significantly outperforming baseline methods. The framework establishes a novel, interpretable, and deployable paradigm for driving safety monitoring in chronic disease contexts.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

A driver's health state serves as a determinant factor in driving behavioral regulation. Subtle deviations from normalcy can lead to operational anomalies, posing risks to public transportation safety. While prior efforts have developed detection mechanisms for functionally-driven temporary anomalies such as drowsiness and distraction, limited research has addressed pathologically-triggered deviations, especially those stemming from chronic medical conditions. To bridge this gap, we investigate the driving behavior of Parkinson's disease patients and propose SAFE-D, a novel framework for detecting Parkinson-related behavioral anomalies to enhance driving safety. Our methodology starts by performing analysis of Parkinson's disease symptomatology, focusing on primary motor impairments, and establishes causal links to degraded driving performance. To represent the subclinical behavioral variations of early-stage Parkinson's disease, our framework integrates data from multiple vehicle control components to build a behavioral profile. We then design an attention-based network that adaptively prioritizes spatiotemporal features, enabling robust anomaly detection under physiological variability. Finally, we validate SAFE-D on the Logitech G29 platform and CARLA simulator, using data from three road maps to emulate real-world driving. Our results show SAFE-D achieves 96.8% average accuracy in distinguishing normal and Parkinson-affected driving patterns.

Problem

• Detecting Parkinson's disease-related driving behavioral anomalies
• Establishing causal links between motor impairments and driving performance
• Developing spatiotemporal framework for early-stage Parkinson's driving detection

Innovation

• Integrates multi-source vehicle control data
• Uses attention-based spatiotemporal feature prioritization
• Validates framework on driving simulator platform