Simulation has played an important role in efficiently evaluating
self-driving vehicles in terms of scalability. Existing methods mostly rely on
heuristic-based simulation, where traffic participants follow certain
human-encoded rules that fail to generate complex human behaviors. Therefore,
the reactive simulation concept is proposed to bridge the human behavior gap
between simulation and real-world traffic scenarios by leveraging real-world
data. However, these reactive models can easily generate unreasonable behaviors
after a few steps of simulation, where we regard the model as losing its
stability. To the best of our knowledge, no work has explicitly discussed and
analyzed the stability of the reactive simulation framework. In this paper, we
aim to provide a thorough stability analysis of the reactive simulation and
propose a solution to enhance the stability. Specifically, we first propose a
new reactive simulation framework, where we discover that the smoothness and
consistency of the simulated state sequences are crucial factors to stability.
We then incorporate the kinematic vehicle model into the framework to improve
the closed-loop stability of the reactive simulation. Furthermore, along with
commonly-used metrics, several novel metrics are proposed in this paper to
better analyze the simulation performance.

本文提出了一种新的反应式仿真框架，通过引入运动学车辆模型提高了仿真的闭环稳定性，同时针对仿真表现提出了新的评估指标，并进行了稳定性分析。