Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion planning and relative distance to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

翻译：交通路口是交通系统中几乎随处可见的重要场景。当前大多数仿真方法在高速公路和城市交通网络表现良好，但在路口场景中面临缺乏清晰车道标线的挑战——来自不同方向、具备多种运动规划的智能体在中心区域交汇。传统基于模型的方法在缺少预定义车道的情况下难以驱动智能体实现真实运动，而数据驱动方法往往需要大量高质量输入数据，同时需繁琐的参数调优才能获得理想仿真结果。本文提出一种新颖的、自适应且具有规划感知能力的混合驱动方法（TraInterSim）来模拟交通路口场景。该混合驱动方法将基于优化的数据驱动方案与速度连续性模型相结合，利用真实数据引导智能体运动，并能生成输入数据中未包含的行为。优化方法充分考虑了速度连续性、期望速度、方向引导以及规划感知碰撞规避。智能体可感知其他参与者的运动规划与相对距离以规避潜在碰撞。为保持不同智能体的个体灵活性，方法中的参数在仿真过程中自动调整。TraInterSim能够在交互速率下生成不同交通路口场景中异质智能体的真实行为。通过大量实验与用户研究，我们验证了所提仿真方法的有效性与合理性。