TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
Ali Asghar Sharifi Ali Zoljodi Masoud Daneshtalab
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.