Abstract
The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has several modules responsible for one or more of the aforementioned items. Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers. The module is also responsible for respecting kinematic constraints and any applicable road constraints. In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. First, a spatial-temporal graph is constructed using the autonomous vehicle, its surrounding vehicles, and virtual nodes along the road with respect to the vehicle itself. Next, the graph is forwarded into a sequential network to obtain the desired states. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Furthermore, a novel potential function is also proposed to train the network. Finally, the proposed planner is tested on three different complex driving tasks, and the performance is compared with two frequently used methods. The results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride.
Abstract (translated)
预计在未来十年里，自动驾驶行业将增长20倍以上，因此会激发研究人员深入研究这个领域。他们的研究主要关注确保安全性、舒适性和效率。自动驾驶汽车有多个模块负责实现上述一或多个项目。在这些模块中，轨迹规划器在车辆的安全性和乘客的舒适性方面起着关键作用。该模块还负责遵守运动约束和适用的道路限制。在本文中，介绍了一种新颖的在线空间-时间图轨迹规划器，用于生成安全和舒适的轨迹。首先，使用自动驾驶汽车、周围车辆和道路上的虚拟节点构建了空间-时间图。然后，将该图传递给一个序列网络以获得所需的状态。为了支持规划器，还提出了一个简单的行为层，用于确定规划器的运动约束。此外，还提出了一个新型的势能函数来训练网络。最后，对所提出的规划器在三个不同的复杂驾驶任务上进行了测试，并将性能与两种常用的方法进行了比较。结果表明，与两种常用方法相比，所提出的规划器在实现类似或更长的前进距离的同时，还提供了安全和舒适的驾驶体验。
URL
https://arxiv.org/abs/2404.12256
