Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians' future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.

中文翻译：

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具有自动驾驶仪多模式行人轨迹预测功能的社交感知人群导航

在拥挤的行人环境中无缝操作自动驾驶汽车是一项非常艰巨的任务。这是因为在这种环境下很难预测人类的活动和互动。最近的工作表明，基于强化学习的方法具有学习驾驶人群的能力。但是，由于人的运动预测具有较大的方差，由于对行人的未来状态的预测不准确，因此这些方法的性能可能非常差。为了克服这个问题，我们提出了一种新方法，SARL-SGAN-KCE，该方法将具有深层社会意识的注意力价值网络与人类多模式轨迹预测模型相结合，以帮助确定最佳驾驶策略。我们还介绍了一种新颖的技术，以最小的附加计算需求来扩展离散动作空间。还考虑了车辆的运动学约束，以确保平稳和安全的轨迹。我们针对最先进的人群导航方法评估了我们的方法，并提供了一项消融研究，以表明我们的方法更安全，更接近人类行为。