RobustTP：密集交通环境下传感器噪声输入下异构道路智能体的端到端轨迹预测
RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs
摘要：
我们提出了RobustTP，一个端到端的算法，用于预测未来的轨迹的道路代理在密集的交通噪声传感器输入轨迹从RGB摄像机（静态或移动）通过跟踪算法。在这种情况下，我们将噪声视为与地面实况轨迹的偏差。噪声量取决于跟踪算法的准确性。我们的方法是专为密集的异构交通，其中道路代理对应的公共汽车，汽车，踏板车，自行车，或行人的混合物。RobustTP是一种首先使用非线性运动模型和基于深度学习的实例分割算法的组合来计算轨迹的方法。接下来，使用LSTM-CNN神经网络架构来训练这些噪声轨迹，该架构对密集和异构交通中道路代理之间的交互进行建模。我们的轨迹预测算法优于使用传感器输入进行端到端轨迹预测的最先进方法。我们实现了高达18%的平均位移误差的改善和高达35.5%的最终位移误差在预测窗口结束时（5秒），在下一个最好的方法。所有实验都在Nvidia Titan Xp GPU上进行。此外，我们还发布了一个软件框架TrackNPred。该框架包括最先进的跟踪和轨迹预测方法和工具的实现，以在真实世界的密集交通数据集上进行基准测试和评估。
英文摘要：
We present RobustTP, an end-to-end algorithm for predicting future trajectories of road-agents in dense traffic with noisy sensor input trajectories obtained from RGB cameras (either static or moving) through a tracking algorithm. In this case, we consider noise as the deviation from the ground truth trajectory. The amount of noise depends on the accuracy of the tracking algorithm. Our approach is designed for dense heterogeneous traffic, where the road agents corresponding to a mixture of buses, cars, scooters, bicycles, or pedestrians. RobustTP is an approach that first computes trajectories using a combination of a non-linear motion model and a deep learning-based instance segmentation algorithm. Next, these noisy trajectories are trained using an LSTM-CNN neural network architecture that models the interactions between road-agents in dense and heterogeneous traffic. Our trajectory prediction algorithm outperforms state-of-the-art methods for end-to-end trajectory prediction using sensor inputs. We achieve an improvement of upto 18% in average displacement error and an improvement ofup to 35.5% in final displacement error at the end of the prediction window (5 seconds) over the next best method. All experiments were set up on an Nvidia TiTan Xp GPU. Additionally, we release a software framework, TrackNPred. The framework consists of implementations of state-of-the-art tracking and trajectory prediction methods and tools to benchmark and evaluate them on real-world dense traffic datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
