Evaluating Emotion Regulation Techniques for Supporting Driving Safety and Performance
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study investigates the efficacy of emotion regulation techniques (ERTs) in mitigating the safety risks associated with driving under elevated emotional states. Research indicates that high-arousal emotions, such as anger and happiness, can increase driving risk by a factor of ten by altering risk perception and consuming cognitive resources. The authors aimed to identify interventions that de-escalate these emotional states to improve driving performance while imposing minimal additional cognitive load on the driver. The researchers conducted an experiment with 46 adult participants using a medium-fidelity driving simulator. Participants were exposed to stimuli designed to elicit either anger (via red lighting and irritating noise) or happiness (via a rigged "wheel of fortune" game and celebratory music). The study compared a control condition with no intervention against three ERTs utilizing different sensory modalities: a visual ERT (VERT) involving a paced breathing application, an auditory ERT (AERT) playing calming music, and a scent-based ERT (SERT) using lavender. Driving safety and performance were assessed using a Driving Risk Propensity Metric (DRPM), Time to Collision (TTC), frequency of negative interactions (tailgating), and perceived workload via the NASA-TLX survey. The results demonstrated that ERTs significantly influenced driving behavior. In the anger condition, both VERT and SERT resulted in significantly lower risk propensity compared to the no-intervention control. SERT also produced significantly longer TTCs than AERT, indicating safer following distances. In the happiness condition, VERT and AERT yielded lower risk propensity than SERT. Across both emotional states, the absence of an intervention was associated with approximately 11% to 13% higher rates of aggressive interactions compared to any ERT condition. Furthermore, in the anger condition, participants using AERT or SERT reported significantly lower perceived workload than those without intervention, suggesting these techniques reduced the cognitive burden of emotional management. The findings suggest that integrating sensory-based emotion regulation techniques into vehicle systems can enhance driving safety by reducing aggressive behaviors and risk-taking. Specifically, auditory and olfactory interventions appear effective at lowering driver workload during anger, while visual and auditory interventions may be more beneficial for regulating risk propensity during happiness. The study supports the development of automated, low-cognitive-load assist systems that can detect elevated emotional states and deploy appropriate regulatory stimuli to maintain safe driving performance.
Key finding
Emotion regulation techniques utilizing visual, auditory, or olfactory modalities significantly improved driving safety metrics and reduced perceived workload compared to no intervention during simulated driving under anger and happiness.
Methodology
simulator
Sample size: 46
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. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- affect mood
- music audio mood
- anger road rage
- stress driving
- anxiety depression driving
- workload measurement
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: theory or model, computational model