论文标题
估计车辆使用多个车道变化达到近期目标状态的概率
Estimating the Probability that a Vehicle Reaches a Near-Term Goal State Using Multiple Lane Changes
论文作者
论文摘要
本文提出了一个模型，以根据与交通状况和驾驶行为相对应的参数使用一个或多个车道更改来估算车辆达到近期目标状态的可能性。拟议的模型不仅在路径计划和自动驾驶汽车导航中具有广泛的应用，还可以将其合并到预先警告系统中，以减少在复发和非转变期间的交通延迟。该模型首先是通过系统地减少参数的数量并将问题转换为抽象统计形式的两车道道路段制定的，为此，可以通过数值计算该概率。然后，使用总概率定律将其扩展到具有较高车道数量的情况。 Vissim模拟用于验证模型的预测，并研究不同参数对概率的影响。在大多数情况下，仿真结果在模型预测的4％之内，不同参数（例如驱动行为和交通密度）对概率的影响与我们的期望相匹配。该模型可以通过几乎实时性能实现，计算时间随车道数量线性增加。
This paper proposes a model to estimate the probability of a vehicle reaching a near-term goal state using one or multiple lane changes based on parameters corresponding to traffic conditions and driving behavior. The proposed model not only has broad application in path planning and autonomous vehicle navigation, it can also be incorporated in advance warning systems to reduce traffic delay during recurrent and non-recurrent congestion. The model is first formulated for a two-lane road segment through systemic reduction of the number of parameters and transforming the problem into an abstract statistical form, for which the probability can be calculated numerically. It is then extended to cases with a higher number of lanes using the law of total probability. VISSIM simulations are used to validate the predictions of the model and study the effect of different parameters on the probability. For most cases, simulation results are within 4% of model predictions, and the effect of different parameters such as driving behavior and traffic density on the probability match our expectation. The model can be implemented with near real-time performance, with computation time increasing linearly with the number of lanes.
