论文标题
平行网络：多模式轨迹轨迹融合的多模式轨迹预测
ParallelNet: Multi-mode Trajectory Prediction by Multi-mode Trajectory Fusion
论文作者
论文摘要
5级自动驾驶是一种全自动车辆（AV）不需要人类干预的技术，在广泛使用之前，人们对安全性和稳定性提出了严重的关注。理解和预测道路对象的未来运动轨迹的能力可以帮助AV计划安全易于控制的道路。在本文中，我们提出了一个网络体系结构，该网络体系结构与多个卷积神经网络骨架并行，并融合功能以做出多模式轨迹预测。在2020年的ICRA Nuscene预测挑战中，我们的模型在所有团队中排名第15位。
Level 5 Autonomous Driving, a technology that a fully automated vehicle (AV) requires no human intervention, has raised serious concerns on safety and stability before widespread use. The capability of understanding and predicting future motion trajectory of road objects can help AV plan a path that is safe and easy to control. In this paper, we propose a network architecture that parallelizes multiple convolutional neural network backbones and fuses features to make multi-mode trajectory prediction. In the 2020 ICRA Nuscene Prediction challenge, our model ranks 15th on the leaderboard across all teams.
