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Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization
Dongyeon Yu1, Honggyu Lee1, Taehoon Kim1
1Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea.
Sensors (Basel, Switzerland)
|December 10, 2021
Summary
This study introduces an advanced Long Short-Term Memory model with an attention mechanism for accurate autonomous vehicle trajectory prediction. The model enhances safety in complex traffic by considering vehicle interactions and road geometry.
Area of Science:
- Artificial Intelligence
- Robotics
- Computer Science
Background:
- Autonomous vehicles require sophisticated trajectory prediction for safe navigation.
- Predicting vehicle behavior is complex due to driver variations and inter-vehicle interactions.
Purpose of the Study:
- To develop an accurate trajectory prediction model for autonomous vehicles.
- To improve the safety and planning capabilities of level 3+ autonomous driving systems.
Main Methods:
- Utilized a Long Short-Term Memory (LSTM) encoder-decoder model with an attention mechanism.
- Trained the model on the large-scale Highway Drone (HighD) dataset.
- Developed a road geometry linearization method for broader applicability.
Main Results:
- The proposed model demonstrated effective future trajectory prediction.
- The attention mechanism successfully managed the importance of surrounding vehicle flow and ego-vehicle dynamics.
- Road geometry linearization improved model performance across diverse road environments.
Conclusions:
- The LSTM model with attention offers a robust solution for vehicle trajectory prediction.
- The integration of road geometry linearization enhances the model's adaptability and performance.
- This research contributes to safer and more efficient autonomous driving systems.
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