arXiv:2501.06918stat.MEcs.CV2025-01被引 3

分析老年人驾驶行为,发现75mph限速遵守差异显著。

Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data

  • 用自然驾驶数据构建老年与年轻司机的驾驶行为基准
  • 70岁以上司机在75mph限速路段违规率明显更高
  • 可为自动驾驶辅助系统提供年龄适配的安全干预依据

到2030年,65岁及以上老年人口预计将增长超过50%,道路上老年司机数量将显著上升。70岁以上司机的车祸死亡率高于四五十岁人群,凸显针对该群体开发更有效安全干预措施的重要性。尽管已有关于衰老对驾驶行为影响的研究,但缺乏对这些行为在真实道路场景中表现的分析。本研究利用自然驾驶数据(NDD)分析关键驾驶性能指标——高速公路上的限速遵守情况及在停车路口的减速行为,这两项均可能受年龄相关衰退影响。通过构建累积分布函数(CDFs),我们为老年与年轻司机建立了关键驾驶行为基准。分析包括异常检测、基准对比和准确率评估,结果显示年龄相关差异主要体现在75mph限速路段的遵守情况上。该方法为提升高级驾驶辅助系统(ADAS)提供了基于年龄的个性化干预潜力,但需更多数据以完善其他驾驶行为的评估指标。通过建立精确的驾驶性能基准,ADAS可有效识别异常行为,如急刹车,或提示驾驶能力受损等安全隐患。本研究为未来通过详细驾驶行为分析改进安全干预措施奠定了坚实基础。

原文摘要 · Abstract (English)

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.

驾驶行为老龄化数据驱动ADAS

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