低渗透率智能网联环境下高风险事件预警方法

Early Warning Methods for Traffic High-risk Events Under Low Penetration of Connected and Autonomous Vehicles

摘要:

提出一种低渗透率智能网联环境下高风险事件预警方法。具体而言，基于熵能表征系统状态的特点提出交通熵的概念，将个体车辆的微观驾驶行为量化为交通熵，以表征交通流状态；再将交通熵作为长短时记忆网络模型（Long Short-term Memory， LSTM）的输入参数建立预警模型；最后，使用HighD轨迹数据集提取高风险事件，并验证模型有效性。结果显示，使用交通熵的模型误报率和漏报率大幅降低。以智能车渗透率10 %为例，误报率和漏报率分别从6.18 %和11.47 %下降到了1.95 %和3.12 %；在预测模式下，对高风险事件误报率和漏报率为2.28 %和3.82 %。

Abstract:

We propose an early warning method for high-risk events of traffic operation under low penetration of connected and autonomous vehicles(CAVs）. Specifically， we first define the concept of traffic entropy， and quantifies the micro driving behavior of individual vehicles as a parameter represented by traffic entropy， which is used to characterize the state of macroscopic traffic flow. And then the traffic entropy is used as the input parameter of the Long Short-Term Memory （LSTM） model to establish the early warning model of high-risk events. The HighD Dataset from German highways was utilized for the empirical analyses. In order to compare the application results under CAVs environment， an autonomous-vehicles scenario and a connected-vehicles scenario were set for the high-risk events and non-risk events extracted from the HighD Dataset. and the effectiveness of the warning of high-risk events under different vehicle permeability was compared. Results show that， the false alarm and missed alarm rates of early warning model with traffic entropy parameters are both reduced. Taking the low-penetration CAVs of 10% as an example， the false alarm and missed alarm rates reduced from 6.18 % and 11.47 % to 1.95 % and 3.12 %， respectively. At the same time， the false alarm and missed alarm rates are only 2.28 % and 3.82 % under the prediction environment.