论文标题
具有自动驾驶汽车的多模式行人轨迹预测的社会意识到人群导航
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles
论文作者
论文摘要
在拥挤的行人环境中无缝操作自动驾驶汽车是一项非常具有挑战性的任务。这是因为在这种环境中，人类运动和互动很难预测。最近的工作表明，基于强化的学习方法具有学习人群驾驶的能力。但是，由于人类运动预测的差异很大，因此由于对行人未来状态的预测不准确，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，该方法将深厚的社会意识认识的价值网络与人类的多模式轨迹预测模型相结合，以帮助确定最佳的驾驶政策。我们还引入了一种新型技术，以扩展离散的动作空间，并具有最小的其他计算要求。还考虑了车辆的运动学限制，以确保光滑和安全的轨迹。我们根据人群导航的最先进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，更接近人类的行为。
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.
