...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Traffic Injury Prevention >Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system
【24h】

Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system

机译：预测停车牌控制的交叉路口与碰撞相关的违规行为，以开发交叉路口驾驶员辅助系统

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Objective: Intersection crashes resulted in over 5,000 fatalities in the United States in 2014. Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that seek to help drivers safely traverse intersections. I-ADAS uses onboard sensors to detect oncoming vehicles and, in the event of an imminent crash, can either alert the driver or take autonomous evasive action. The objective of this study was to develop and evaluate a predictive model for detecting whether a stop sign violation was imminent.Methods: Passenger vehicle intersection approaches were extracted from a data set of typical driver behavior (100-Car Naturalistic Driving Study) and violations (event data recorders downloaded from real-world crashes) and were assigned weighting factors based on real-world frequency. A k-fold cross-validation procedure was then used to develop and evaluate 3 hypothetical stop sign warning algorithms (i.e., early, intermediate, and delayed) for detecting an impending violation during the intersection approach. Violation detection models were developed using logistic regression models that evaluate likelihood of a violation at various locations along the intersection approach. Two potential indicators of driver intent to stopthat is, required deceleration parameter (RDP) and brake applicationwere used to develop the predictive models. The earliest violation detection opportunity was then evaluated for each detection algorithm in order to (1) evaluate the violation detection accuracy and (2) compare braking demand versus maximum braking capabilities.Results: A total of 38 violating and 658 nonviolating approaches were used in the analysis. All 3 algorithms were able to detect a violation at some point during the intersection approach. The early detection algorithm, as designed, was able to detect violations earlier than all other algorithms during the intersection approach but gave false alarms for 22.3% of approaches. In contrast, the delayed detection algorithm sacrificed some time for detecting violations but was able to substantially reduce false alarms to only 3.3% of all nonviolating approaches. Given good surface conditions (maximum braking capabilities = 0.8 g) and maximum effort, most drivers (55.3-71.1%) would be able to stop the vehicle regardless of the detection algorithm. However, given poor surface conditions (maximum braking capabilities = 0.4 g), few drivers (10.5-26.3%) would be able to stop the vehicle. Automatic emergency braking (AEB) would allow for early braking prior to driver reaction. If equipped with an AEB system, the results suggest that, even for the poor surface conditions scenario, over one half (55.3-65.8%) of the vehicles could have been stopped.Conclusions: This study demonstrates the potential of I-ADAS to incorporate a stop sign violation detection algorithm. Repeating the analysis on a larger, more extensive data set will allow for the development of a more comprehensive algorithm to further validate the findings.
机译：目标：2014年，相交事故在美国造成超过5,000人死亡。相交高级驾驶员辅助系统（I-ADAS）是一种主动安全系统，旨在帮助驾驶员安全地穿越交叉路口。 I-ADAS使用车载传感器检测即将到来的车辆，并且在即将发生撞车事故时，可以警告驾驶员或采取自动回避措施。这项研究的目的是开发和评估一种预测模型，以检测是否即将发生停车标志违规行为。方法：从典型驾驶员行为（100辆汽车自然驾驶研究）和违规行为（事件数据记录器（从实际崩溃中下载），并根据实际频率分配了加权因子。然后使用k倍交叉验证程序来开发和评估3种假设的停车标志警告算法（即，早期，中间和延迟），以检测交叉路口方法中即将发生的违规情况。违规检测模型是使用逻辑回归模型开发的，该模型可评估交叉路口沿线各个位置违规的可能性。驾驶员意图停车的两个潜在指标，即所需的减速参数（RDP）和制动应用被用于开发预测模型。然后针对每种检测算法评估最早的违规检测机会，以便（1）评估违规检测准确性，并且（2）比较制动需求与最大制动能力。结果：共使用了38种违规方法和658种非违规方法。分析。所有3种算法都能够在相交方法中的某个时刻检测到违规。如设计的那样，早期检测算法能够在交叉路口进近中比所有其他算法更早地检测到违规，但对22.3％的进近给出了误报。相比之下，延迟检测算法为检测违规行为花费了一些时间，但能够将错误警报的发生率大幅降低至所有非违规方法的3.3％。在良好的地面条件下（最大制动能力= 0.8 g）和最大的努力，无论采用哪种检测算法，大多数驾驶员（55.3-71.1％）都可以停车。但是，在恶劣的地面条件下（最大制动能力= 0.4 g），很少有驾驶员（10.5-26.3％）能够停车。自动紧急制动（AEB）可以在驾驶员做出反应之前尽早制动。如果配备AEB系统，结果表明，即使在恶劣的地面条件下，也可能有超过一半的车辆（55.3-65.8％）被停止了。结论：本研究表明了I-ADAS整合的潜力停车标志违规检测算法。在更大，更广泛的数据集上重复分析将允许开发更全面的算法以进一步验证发现。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号