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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis
06:21

Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis

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TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction.

Ali Asghar Sharifi1, Ali Zoljodi1, Masoud Daneshtalab1,2

  • 1School of Innovation, Design and Technology (IDT), Mälardalen University, 72123 Västerås, Sweden.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

TrajectoryNAS optimizes LiDAR-based trajectory prediction for autonomous driving. This neural architecture search method improves accuracy and reduces latency for safer navigation.

Keywords:
3D point cloudautonomous drivingneural architecture searchtrajectory prediction

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Area of Science:

  • Autonomous Driving Systems
  • Computer Vision
  • Artificial Intelligence

Background:

  • Trajectory prediction is crucial for safe autonomous navigation, with LiDAR data offering superior 3D environmental perception compared to 2D cameras.
  • Current LiDAR-based trajectory prediction methods face challenges with computational cost, slow inference, and accuracy limitations due to inefficient architectures.

Purpose of the Study:

  • To introduce TrajectoryNAS, a novel neural architecture search (NAS) method for developing efficient and accurate LiDAR-based trajectory prediction models.
  • To optimize the complete end-to-end trajectory prediction pipeline, including object detection and tracking, by addressing neural architecture design.

Main Methods:

  • TrajectoryNAS employs metaheuristic algorithms to systematically search and optimize neural network architectures for trajectory prediction.
  • A multi-objective energy function is introduced, balancing prediction accuracy and computational efficiency (latency).
  • The method considers all stacked components of the prediction pipeline to minimize accuracy loss and overhead latency.

Main Results:

  • TrajectoryNAS significantly enhances the performance of autonomous driving systems by improving trajectory prediction.
  • Experimental results on the NuScenes dataset show TrajectoryNAS achieves at least 4.8% higher accuracy and 1.1x lower latency than competing methods.
  • The optimized model demonstrates superior efficiency and accuracy in predicting surrounding object trajectories.

Conclusions:

  • TrajectoryNAS represents a significant advancement in LiDAR-based trajectory prediction for autonomous vehicles.
  • The NAS approach effectively addresses the limitations of handcrafted architectures, leading to more robust and performant systems.
  • The developed model offers a promising solution for enhancing the safety and reliability of autonomous driving technology.