Deep Learning Algorithms for Longitudinal Driving Behavior Prediction: A Comparative Analysis of Convolutional Neural Network and Long–Short-Term Memory …
G Lucente, MS Maarssoe, I Kahl, J Schindler - SAE International Journal of …, 2024 - sae.org
In the realm of transportation science, the advent of deep learning has propelled
advancements in predicting longitudinal driving behavior. This study explores the …
advancements in predicting longitudinal driving behavior. This study explores the …
Deep Long Short-Term Memory Network Based Long-Term Vehicle Trajectory Prediction
This paper proposes two long short-term memory (LSTM) network based models to predict
vehicle trajectories for two different scenarios:(i) sufficient historical information of the …
vehicle trajectories for two different scenarios:(i) sufficient historical information of the …
A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information
H Min, X Xiong, P Wang, Z Zhang - Automotive Innovation, 2024 - Springer
Trajectory prediction is an essential component in autonomous driving systems, as it can
forecast the future movements of surrounding vehicles, thereby enhancing the decision …
forecast the future movements of surrounding vehicles, thereby enhancing the decision …
A CNN-LSTM Based Model to Predict Trajectory of Human-Driven Vehicle
Vehicle trajectory prediction is essential in ensuring the safe and efficient operation of
advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables …
advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables …
Structured deep learning models for accurate prediction of real-world driving speed for short and long-term horizons
Z Zhao, S Yang, C Sauer, A Teraji… - … Joint Conference on …, 2022 - ieeexplore.ieee.org
In this paper, we present a machine learning approach that generates a system of driver-
centered and roadway type-specific deep neural network models for accurate vehicle speed …
centered and roadway type-specific deep neural network models for accurate vehicle speed …
Vehicle trajectory prediction with lane stream attention-based LSTMs and road geometry linearization
D Yu, H Lee, T Kim, SH Hwang - Sensors, 2021 - mdpi.com
It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the
trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories …
trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories …
Joint deep neural network modelling and statistical analysis on characterizing driving behaviors
Google defines the concept of autonomous driving as one of the applications of big data.
Specifically, with the input sensor data, the autonomous vehicles can be provided with the …
Specifically, with the input sensor data, the autonomous vehicles can be provided with the …
A personalized deep learning approach for trajectory prediction of connected vehicles
Forecasting the motion of the leading vehicle is a critical task for connected autonomous
vehicles as it provides an efficient way to model the leading-following vehicle behavior and …
vehicles as it provides an efficient way to model the leading-following vehicle behavior and …
Driving Behavior Prediction Based on Combined Neural Network Model
R Li, X Shu, C Li - IEEE Transactions on Computational Social …, 2024 - ieeexplore.ieee.org
Accurate behavior prediction of surrounding vehicles can greatly improve the operating
safety of autonomous vehicles. However, in real traffic scence, the complexity and …
safety of autonomous vehicles. However, in real traffic scence, the complexity and …
Leveraging transformer model to predict vehicle trajectories in congested urban traffic
Accurate vehicle trajectory prediction enables safe, comfortable, and optimal proactive
motion planning for connected and autonomous vehicles (CAVs). Because of rapid …
motion planning for connected and autonomous vehicles (CAVs). Because of rapid …