Abstract
Autonomous driving systems are a rapidly evolving technology that enables driverless car production. Trajectory prediction is a critical component of autonomous driving systems, enabling cars to anticipate the movements of surrounding objects for safe navigation. Trajectory prediction using Lidar point-cloud data performs better than 2D images due to providing 3D information. However, processing point-cloud data is more complicated and time-consuming than 2D images. Hence, state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. This paper introduces TrajectoryNAS, a pioneering method that focuses on utilizing point cloud data for trajectory prediction. By leveraging Neural Architecture Search (NAS), TrajectoryNAS automates the design of trajectory prediction models, encompassing object detection, tracking, and forecasting in a cohesive manner. This approach not only addresses the complex interdependencies among these tasks but also emphasizes the importance of accuracy and efficiency in trajectory modeling. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field.Experimental results reveal that TrajcetoryNAS yield a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.
Abstract (translated)
自动驾驶系统是一种快速发展的技术，可以实现无人驾驶汽车的生产。轨迹预测是自动驾驶系统的一个关键组件，可以让汽车预测周围物体的运动，实现安全导航。使用激光点云数据进行轨迹预测的轨迹预测比二维图像更好，因为提供了3D信息。然而，处理点云数据的过程更加复杂和耗时。因此，使用点云数据进行轨迹预测的先进方法存在慢且错误的预测。本文介绍了TrajectoryNAS，一种领先的方法，专注于利用点云数据进行轨迹预测。通过利用神经架构搜索（NAS），TrajectoryNAS自动设计轨迹预测模型，将物体检测、跟踪和预测整合在一起。这种方法不仅解决了这些任务之间的复杂依赖关系，还强调了轨迹建模中准确性和效率的重要性。通过实验研究，TrajectoryNAS在NuScenes数据集上的性能至少比竞争方法提高了4.8个精度，延迟降低了1.1倍。
URL
https://arxiv.org/abs/2403.11695
