This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a representative set of on-ramps and then collected real-world observational data from the merging vehicle's perspective in various traffic conditions ranging from free-flowing to rush-hour traffic jams. Next, as our core contribution, we introduce a novel car-following model, called MR-IDM, for highway driving that reacts to merging vehicles in a realistic way. This proposed driving model can either be used in traffic simulators to generate realistic highway driving behavior or integrated into a prediction module for autonomous vehicles attempting to merge onto the highway. We quantitatively evaluated the effectiveness of our model and compared it against several other methods. We show that MR-IDM has the least error in mimicking the real-world data, while having features such as smoothness, stability, and lateral awareness.

翻译：本文探讨了现有微观交通模型在考虑高速公路匝道车辆对主车道车辆跟驰行为潜在影响方面的局限性。我们首先调查了美国境内匝道，选取具有代表性的匝道集合，随后从汇入车辆视角采集了涵盖自由流至高峰拥堵等多种交通状态下的真实观测数据。作为核心贡献，我们提出一种名为MR-IDM的新型跟驰模型，该模型能真实模拟高速公路行驶中对汇入车辆的反应行为。该驾驶模型既可用于交通仿真器生成逼真的高速公路驾驶行为，也可集成至意图汇入高速公路的自动驾驶车辆预测模块中。我们通过定量评估验证了模型有效性，并与多种现有方法进行对比。结果表明，MR-IDM在模拟真实数据时误差最小，同时兼具平滑性、稳定性及横向感知能力。