Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2501.06918 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912185430573056 |
|---|---|
| author | Joshi, Aparna Adugyamfi, Kojo Merickel, Jennifer Gunaratne, Pujitha Sharma, Anuj |
| author_facet | Joshi, Aparna Adugyamfi, Kojo Merickel, Jennifer Gunaratne, Pujitha Sharma, Anuj |
| contents | By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road. Drivers over 70 face higher crash death rates compared to those in their forties and fifties, underscoring the importance of developing more effective safety interventions for this demographic. Although the impact of aging on driving behavior has been studied, there is limited research on how these behaviors translate into real-world driving scenarios. This study addresses this need by leveraging Naturalistic Driving Data (NDD) to analyze driving performance measures - specifically, speed limit adherence on interstates and deceleration at stop intersections, both of which may be influenced by age-related declines. Using NDD, we developed Cumulative Distribution Functions (CDFs) to establish benchmarks for key driving behaviors among senior and young drivers. Our analysis, which included anomaly detection, benchmark comparisons, and accuracy evaluations, revealed significant differences in driving patterns primarily related to speed limit adherence at 75mph. While our approach shows promising potential for enhancing Advanced Driver Assistance Systems (ADAS) by providing tailored interventions based on age-specific adherence to speed limit driving patterns, we recognize the need for additional data to refine and validate metrics for other driving behaviors. By establishing precise benchmarks for various driving performance metrics, ADAS can effectively identify anomalies, such as abrupt deceleration, which may indicate impaired driving or other safety concerns. This study lays a strong foundation for future research aimed at improving safety interventions through detailed driving behavior analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06918 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data Joshi, Aparna Adugyamfi, Kojo Merickel, Jennifer Gunaratne, Pujitha Sharma, Anuj Methodology Computer Vision and Pattern Recognition By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road. Drivers over 70 face higher crash death rates compared to those in their forties and fifties, underscoring the importance of developing more effective safety interventions for this demographic. Although the impact of aging on driving behavior has been studied, there is limited research on how these behaviors translate into real-world driving scenarios. This study addresses this need by leveraging Naturalistic Driving Data (NDD) to analyze driving performance measures - specifically, speed limit adherence on interstates and deceleration at stop intersections, both of which may be influenced by age-related declines. Using NDD, we developed Cumulative Distribution Functions (CDFs) to establish benchmarks for key driving behaviors among senior and young drivers. Our analysis, which included anomaly detection, benchmark comparisons, and accuracy evaluations, revealed significant differences in driving patterns primarily related to speed limit adherence at 75mph. While our approach shows promising potential for enhancing Advanced Driver Assistance Systems (ADAS) by providing tailored interventions based on age-specific adherence to speed limit driving patterns, we recognize the need for additional data to refine and validate metrics for other driving behaviors. By establishing precise benchmarks for various driving performance metrics, ADAS can effectively identify anomalies, such as abrupt deceleration, which may indicate impaired driving or other safety concerns. This study lays a strong foundation for future research aimed at improving safety interventions through detailed driving behavior analysis. |
| title | Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data |
| topic | Methodology Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.06918 |