用模拟预测自动驾驶车辆行为，支持多车协同决策。
Conditional Prediction by Simulation for Automated Driving
- 基于微观交通仿真生成条件化轨迹预测
- 通过对抗逆强化学习训练真实行为模型
- 支持预测中动态调整候选轨迹，适合协同规划场景
模块化自动驾驶系统通常将预测与规划分步处理，无法实现协同动作。本文提出一种考虑轨迹间条件依赖的预测模型，通过微观交通仿真生成预测，其中各交通参与者由对抗逆强化学习训练的行为模型控制。假设自动驾驶车辆多种候选轨迹，对每种轨迹生成对应的条件预测。此外，该方法支持在预测过程中动态调整候选轨迹。相关示例场景可访问 https://conditionalpredictionbysimulation.github.io/。
原文摘要 · Abstract (English)
Modular automated driving systems commonly handle prediction and planning as sequential, separate tasks, thereby prohibiting cooperative maneuvers. To enable cooperative planning, this work introduces a prediction model that models the conditional dependencies between trajectories. For this, predictions are generated by a microscopic traffic simulation, with the individual traffic participants being controlled by a realistic behavior model trained via Adversarial Inverse Reinforcement Learning. By assuming various candidate trajectories for the automated vehicle, we generate predictions conditioned on each of them. Furthermore, our approach allows the candidate trajectories to adapt dynamically during the prediction rollout. Several example scenarios are available at https://conditionalpredictionbysimulation.github.io/.
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