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A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction.

Yikun Fan¹, Wei Zhang², Wenting Zhang¹

¹College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, China.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

This study presents a novel double-layer LSTM model for precise autonomous vehicle trajectory prediction, improving safety and performance by considering driving styles and interactions. The model demonstrates superior accuracy over existing methods using real-world datasets.

Keywords:

LSTM autonomous vehicle driving style grids in interaction trajectory prediction

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Area of Science:

Autonomous Systems
Artificial Intelligence
Robotics

Background:

Ensuring safety in autonomous vehicles (AVs) is critical for their evolution.
Precise trajectory prediction is essential for enhancing AV safety and performance in complex environments.
Conventional prediction methods often fail to account for predicted vehicle behavior and interactions.

Purpose of the Study:

To introduce a novel double-layer long short-term memory (LSTM) model for accurate autonomous vehicle trajectory prediction.
To overcome the limitations of existing methods by incorporating driving-style and adaptive grid generation.
To improve the prediction of vehicle intentions and trajectories in intricate driving scenarios.

Main Methods:

A novel double-layer LSTM model was developed, integrating convolutional and max-pooling layers for feature extraction.
Multi-sensor data from perception modules were fused to extract vehicle trajectories.
Driving-style category values and an improved adaptive grid generation method were incorporated.
Historical trajectory data and vehicle/lane information were leveraged for dynamic grid adjustment.

Main Results:

The proposed model demonstrated significantly enhanced representation of vehicle motion features and interactions.
Experiments on the NGSIM US-101 and I-80 datasets showed superior performance compared to existing benchmarks.
The model achieved higher intention accuracy and a lower root mean square error (RMSE) over a 5-second prediction horizon.

Conclusions:

The developed double-layer LSTM model effectively predicts autonomous vehicle trajectories by capturing temporal and spatial features.
The incorporation of driving style and adaptive grids enhances prediction accuracy and accounts for vehicle interactions.
The model's stability and effectiveness were verified through rigorous experimentation, offering a promising advancement in AV safety technology.