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Open AccessArticle
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
by
Ali Asghar Sharifi
Ali Asghar Sharifi 1
,
Ali Zoljodi
Ali Zoljodi 1
and
Masoud Daneshtalab
Masoud Daneshtalab 1,2,*
1
School of Innovation, Design and Technology (IDT), Mälardalen University , 72123 Västerås, Sweden
2
Department of Computer Systems, Tallinn University of Technology, 19086 Tallinn, Estonia
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(17), 5696; https://doi.org/10.3390/s24175696 (registering DOI)
Submission received: 15 July 2024
/
Revised: 27 August 2024
/
Accepted: 29 August 2024
/
Published: 1 September 2024
Abstract
Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. 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 yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.
Share and Cite
MDPI and ACS Style
Sharifi, A.A.; Zoljodi, A.; Daneshtalab, M.
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction. Sensors 2024, 24, 5696.
https://doi.org/10.3390/s24175696
AMA Style
Sharifi AA, Zoljodi A, Daneshtalab M.
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction. Sensors. 2024; 24(17):5696.
https://doi.org/10.3390/s24175696
Chicago/Turabian Style
Sharifi, Ali Asghar, Ali Zoljodi, and Masoud Daneshtalab.
2024. "TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction" Sensors 24, no. 17: 5696.
https://doi.org/10.3390/s24175696
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