论文标题
自动驾驶汽车的多模式变压器路径预测
Multi-modal Transformer Path Prediction for Autonomous Vehicle
论文作者
论文摘要
关于车辆路径预测的推理是自动驾驶系统安全操作的必不可少的问题。有许多用于路径预测的研究工作。但是，其中大多数不使用车道信息，也不基于变压器体系结构。通过利用从配备自动驾驶车辆的传感器收集的不同类型的数据，我们提出了一个名为多模式变压器路径预测（MTPP）的路径预测系统，该系统旨在预测目标试剂的长期未来轨迹。为了实现更准确的路径预测，在我们的模型中采用了变压器体系结构。为了更好地利用车道信息，目标剂不太可能采用与目标试剂相反的车道并因此被过滤掉。另外，将连续的车道块组合在一起，以确保车道输入足够长以预测路径。进行了广泛的评估，以表明使用现实世界中的轨迹预测数据集Nuscene的拟议系统的功效。
Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of data collected from sensors equipped on the self-driving vehicles, we propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer architecture is adopted in our model. To better utilize the lane information, the lanes which are in opposite direction to target agent are not likely to be taken by the target agent and are consequently filtered out. In addition, consecutive lane chunks are combined to ensure the lane input to be long enough for path prediction. An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.
