A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction
In the evolution of autonomous vehicles (AVs), ensuring safety is of the utmost significance. Precise trajectory prediction is indispensable for augmenting vehicle safety and system performance in intricate environments. This study introduces a novel double-layer long short-term memory (LSTM) model...
Saved in:
| Main Authors: | Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/7/2059 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
by: Yiying Wei, et al.
Published: (2025-03-01) -
Vehicle Trajectory Prediction Algorithm Based on Hybrid Prediction Model with Multiple Influencing Factors
by: Tao Wang, et al.
Published: (2025-02-01) -
Multi-Area Sampling-Based Spatiotemporal Trajectory Planning for Autonomous Driving in Dynamic On-Road Scenarios
by: Shuhuan Ma, et al.
Published: (2024-11-01) -
Review of Research on Trajectory Prediction of Road Pedestrian Behavior
by: YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu
Published: (2025-05-01) -
Korean translation and validation of the multidimensional driving style inventory
by: Ilsun Rhiu, et al.
Published: (2025-02-01)