论文标题
PLOP：自动驾驶的概率多项式对象轨迹计划
PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving
论文作者
论文摘要
为了在城市环境中安全地驾驶，自动驾驶汽车（EGO车辆）必须理解并预测其周围环境，尤其是其他道路使用者（邻居）的行为和意图。在大多数情况下，所有道路用户都可以接受多种决策选择（例如，右转或左转，或避免障碍的不同方式），导致高度不确定和多模式的决策空间。我们在这里专注于通过概率框架预测自我车辆和邻居的多个可行的未来轨迹。我们依靠有条件的模仿学习算法，该算法由自我车辆的导航命令（例如“右转”）条件。我们的模型处理自我车辆的前置摄像头图像和鸟眼视图网格，该网格是根据LiDar Point Clouds计算出的，对过去和现在的对象进行了检测，以便为Ego车辆及其邻居生成多个轨迹。我们的方法在计算上是有效的，仅依赖于车载传感器。我们在公开可用的数据集Nuscenes上评估我们的方法，实现最先进的性能，研究我们的体系结构选择对在线模拟实验的影响，并显示对真正车辆控制的初步见解
To navigate safely in urban environments, an autonomous vehicle (ego vehicle) must understand and anticipate its surroundings, in particular the behavior and intents of other road users (neighbors). Most of the times, multiple decision choices are acceptable for all road users (e.g., turn right or left, or different ways of avoiding an obstacle), leading to a highly uncertain and multi-modal decision space. We focus here on predicting multiple feasible future trajectories for both ego vehicle and neighbors through a probabilistic framework. We rely on a conditional imitation learning algorithm, conditioned by a navigation command for the ego vehicle (e.g., "turn right"). Our model processes ego vehicle front-facing camera images and bird-eye view grid, computed from Lidar point clouds, with detections of past and present objects, in order to generate multiple trajectories for both ego vehicle and its neighbors. Our approach is computationally efficient and relies only on on-board sensors. We evaluate our method offline on the publicly available dataset nuScenes, achieving state-of-the-art performance, investigate the impact of our architecture choices on online simulated experiments and show preliminary insights for real vehicle control
