基于生态驾驶的道路合流区车辆运行状态估计
Estimation of Vehicle Operating States in Merging Areas of Roads Based on Eco-driving
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摘要: 针对城市道路合流区生态驾驶策略的研究, 需考虑真实场景车辆运行车速特性。通过分析车速的累积频率、分布趋势、特征百分位值等统计特性, 得出平/高峰时段下合流区车速的差异性及高峰时段下合流区车速及加速度的运行特性。结果表明: 高峰时段交织区车速分布为相对集中的左偏分布, 平峰时段交织区车速基本符合Gaussian分布; 高峰时段合流区车速紊乱分布于0, 20.0km/h, 平峰时段合流区车速有序分布于7.5, 45.0km/h; 高峰时段合流区纵向加速度与车速关系的散点分布呈不等腰三角形, 而纵向减速度与车速关系的散点分布呈直角三角形; 高峰时段合流区纵向加速度分位值折线图呈现M形, 纵向减速度分位值折线图呈现出明显的上升趋势。该车速状态估计模型拟合程度指标达79%以上, 精度满足要求, 可为生态驾驶车速估计研究提供一定理论参考。Abstract: The study of ecological driving strategy in the merging zone of urban roads needs to consider the characteristics of vehicle speed in real scenarios. By analyzing the cumulative frequency, distribution trend, characteristic percentile value and other statistical characteristics of vehicle speed, the differences of vehicle speed in the merging zone during flat/peak hours and the operational characteristics of vehicle speed and acceleration in the merging zone during peak hours were derived. Finally, a multiple linear regression speed estimation model based on the relationship between longitudinal acceleration (deceleration) speed and vehicle speed was constructed. Results show that the distribution of vehicle speed in the intertwined zone during peak hours is a relatively concentrated left-skewed distribution, while the speed in the intertwined zone during the flat hours basically conforms to the Gaussian distribution. The speed disorder in the merging area during peak hours is distributed at 0, 20 km/h, and the orderly distribution of vehicle speed in the merging area during the peak hours is distributed at 7.5, 45.0 km/h. The scatter distribution of longitudinal acceleration versus speed in the peak hour merging area is unequal-waisted triangle, while the scatter distribution of longitudinal deceleration versus speed in the peak hour merging area is right-angled triangle. The longitudinal acceleration quantile shows an M-shaped and the longitudinal deceleration quantile shows an obvious rising trend. The degree of fit index reaches more than 79%. The accuracy of the speed state estimation model constructed in this study meets the requirements, and can provide some theoretical reference for the research of eco-driving speed estimation.