Following a leading vehicle is a daily but challenging task because it requires adapting to various traffic conditions and the leading vehicle's behaviors. However, the question `Does the following vehicle always actively react to the leading vehicle?' remains open. To seek the answer, we propose a novel metric to quantify the interaction intensity within the car-following pairs. The quantified interaction intensity enables us to recognize interactive and non-interactive car-following scenarios and derive corresponding policies for each scenario. Then, we develop an interaction-aware switching control framework with interactive and non-interactive policies, achieving a human-level car-following performance. The extensive simulations demonstrate that our interaction-aware switching control framework achieves improved control performance and data efficiency compared to the unified control strategies. Moreover, the experimental results reveal that human drivers would not always keep reacting to their leading vehicle but occasionally take safety-critical or intentional actions -- interaction matters but not always.

翻译：跟驰前车是一项日常但具有挑战性的任务，因为它需要适应各种交通状况和前车的行为。然而，问题“跟驰车辆是否总是主动对前车做出反应？”仍悬而未决。为寻求答案，我们提出了一种新的指标来量化跟驰对中的交互强度。量化的交互强度使我们能够识别交互式和非交互式跟驰场景，并针对每种场景推导相应的策略。随后，我们开发了一种具有交互式和非交互式策略的交互感知切换控制框架，实现了人类水平的跟驰性能。大量仿真结果表明，与统一控制策略相比，我们的交互感知切换控制框架在控制性能和数据效率方面均有所提升。此外，实验结果揭示，人类驾驶员并非总是持续对前车做出反应，而是偶尔会采取安全关键性或意图性行为——交互重要，但并非总是如此。