Advanced Driver Assistance Systems and Emotion-based Driver Behavior

Waters, Anthony; Paglioni, Vincent · 2025 · Crossref

DOI: 10.54941/ahfe1006514

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This review paper addresses the persistent risk of vehicle collisions despite the widespread adoption of Advanced Driver Assistance Systems (ADAS). The authors argue that while ADAS have reduced crash severity, they remain limited because ultimate vehicle control rests with human drivers whose emotional states significantly influence driving behavior. The research is motivated by the need to develop adaptive ADAS capable of detecting and responding to adverse driver emotions, such as anger or stress, which are known to increase risk-taking and error rates. The paper serves as the foundational review for a broader effort to integrate emotion-aware capabilities into automotive safety systems. The authors conducted a comprehensive literature review covering three primary domains: the impact of emotions on driver behavior and decision-making, the current capabilities and limitations of ADAS, and existing methodologies for emotion detection in driving contexts. They analyzed how emotions function as Performance Influencing Factors (PIFs) in Human Reliability Analysis (HRA), affecting working memory and cognitive performance. The review also examined specific ADAS functions, including collision warnings, interventions, and driving control assistance, noting inconsistencies in design and terminology across manufacturers. Furthermore, the paper evaluated various emotion detection techniques, ranging from invasive physiological sensors like electrocardiograms (ECG) and electroencephalography (EEG) to less intrusive methods like facial expression analysis. The findings indicate that emotions significantly alter driving performance, with negative valence emotions like anger and stress leading to increased braking intensity, sudden acceleration, and higher error rates. Hyperreactive drivers are 2.3 times more likely to be involved in collisions than those with basic emotional reactivity. Current ADAS are largely static and cannot adapt to these emotional shifts, leading to potential misuse or deactivation by drivers. Regarding detection, ECG paired with camera systems achieves approximately 80% accuracy in distinguishing emotions like anger and happiness in simulated environments. However, most current detection methods are too invasive or cumbersome for practical in-vehicle implementation. The significance of this work lies in its proposal for next-generation adaptive ADAS that leverage non-invasive data, such as facial recognition and driving behavior trends, to identify emotional states. By adapting system parameters—such as tightening lane-centering margins or limiting acceleration—based on the driver’s emotional state, these systems could mitigate risky behaviors while maintaining driver satisfaction. The authors conclude that integrating AI-driven emotion recognition with existing ADAS infrastructure offers a viable path to further reducing human-error-related accidents, provided that detection methods become less intrusive and more standardized.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• affect mood
• automation surprise
• adas effectiveness
• situational awareness
• behavioral adaptation risk compensation
• anger road rage

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Theoretical Contribution: computational model, theory or model