Traffic simulation plays a crucial role in evaluating and improving autonomous driving planning systems. After being deployed on public roads, autonomous vehicles need to interact with human road participants with different social preferences (e.g., selfish or courteous human drivers). To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment. We propose a socially-controllable behavior generation (SCBG) model for this purpose, which allows the users to specify the level of courtesy of the generated trajectory while ensuring realistic and human-like trajectory generation through learning from real-world driving data. Specifically, we define a novel and differentiable measure to quantify the level of courtesy of driving behavior, leveraging marginal and conditional behavior prediction models trained from real-world driving data. The proposed courtesy measure allows us to auto-label the courtesy levels of trajectories from real-world driving data and conveniently train an SCBG model generating trajectories based on the input courtesy values. We examined the SCBG model on the Waymo Open Motion Dataset (WOMD) and showed that we were able to control the SCBG model to generate realistic driving behaviors with desired courtesy levels. Interestingly, we found that the SCBG model was able to identify different motion patterns of courteous behaviors according to the scenarios.

翻译：交通模拟在评估和改进自动驾驶规划系统中起着关键作用。在部署到公共道路后，自动驾驶车辆需要与具有不同社会偏好（例如，自私或礼貌的人类驾驶员）的人类道路参与者进行交互。为确保自动驾驶车辆在不同交互式交通场景中采取安全且高效的行驶策略，我们应能在模拟环境中针对具有不同社会特性的反应式智能体评估自动驾驶车辆。为此，我们提出了一种社会可控行为生成（SCBG）模型，该模型允许用户指定生成轨迹的礼貌程度，同时通过从真实驾驶数据中学习，确保生成真实且类人的轨迹。具体而言，我们利用从真实驾驶数据中训练得到的边际和条件行为预测模型，定义了一种新颖且可微分的度量，以量化驾驶行为的礼貌程度。所提出的礼貌度量使我们能够自动标注真实驾驶数据中轨迹的礼貌级别，并方便地训练一个基于输入礼貌值生成轨迹的SCBG模型。我们在Waymo开放运动数据集（WOMD）上检验了SCBG模型，结果表明我们能够控制SCBG模型生成具有期望礼貌级别的真实驾驶行为。有趣的是，我们发现SCBG模型能够根据场景识别出礼貌行为的不同运动模式。