Sharifi Ali Asghar, Zoljodi Ali, Daneshtalab Masoud
School of Innovation, Design and Technology (IDT), Mälardalen University, 72123 Västerås, Sweden.
Department of Computer Systems, Tallinn University of Technology, 19086 Tallinn, Estonia.
Sensors (Basel). 2024 Sep 1;24(17):5696. doi: 10.3390/s24175696.
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.
自动驾驶系统是一项快速发展的技术。轨迹预测是自动驾驶系统的关键组成部分，它通过预测周围物体的运动来实现安全导航。激光雷达点云数据提供了自车周围固体物体的三维视图。因此，由于能提供目标物体与自车之间的距离，使用激光雷达点云数据进行轨迹预测比二维RGB相机表现更好。然而，处理点云数据是一个成本高昂且复杂的过程，并且使用点云数据的先进三维轨迹预测存在预测缓慢和错误的问题。先进的轨迹预测方法存在手工设计且效率低下的架构问题，这可能导致低准确性和次优推理时间。神经架构搜索（NAS）是一种通过使用搜索算法根据神经网络模型的性能和运行时重新设计架构来优化神经网络模型的方法。本文介绍了TrajectoryNAS，这是一种新颖的神经架构搜索（NAS）方法，旨在开发一种高效且更准确的基于激光雷达的轨迹预测模型，用于预测自车周围物体的轨迹。TrajectoryNAS系统地优化了端到端轨迹预测算法的架构，使用元启发式算法纳入了轨迹预测所需的所有堆叠组件，包括目标检测和目标跟踪。这种方法解决了轨迹预测每个组件中的神经架构设计问题，同时考虑了准确性损失和相关的开销延迟。我们的方法引入了一种新颖的多目标能量函数，该函数整合了准确性和效率指标，能够创建一个明显优于现有方法的模型。通过实证研究，TrajectoryNAS证明了其在提高自动驾驶系统性能方面的有效性，标志着该领域的重大进步。实验结果表明，在NuScenes数据集上，TrajcetoryNAS比竞争方法的准确率至少高4.8，延迟低1.1倍。
