论文标题
QML用于Angoverse 2运动预测挑战
QML for Argoverse 2 Motion Forecasting Challenge
论文作者
论文摘要
为了安全地在各种复杂的流量方案中进行导航，自主驾驶系统通常配备了运动预测模块，为下游计划模块提供重要信息。对于现实世界应用应用程序，运动预测模型的准确性和延迟都是必不可少的。在本报告中，我们提出了一个有效而有效的解决方案，该解决方案是2022年Argoverse 2运动预测挑战中的第三名。
To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.
