Deep learning with attention mechanism for predicting driver intention at intersection
In this paper, a driver's intention prediction near a road intersection is proposed. Our
approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention …
approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention …
Multimodal trajectory predictions for urban environments using geometric relationships between a vehicle and lanes
A Kawasaki, A Seki - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
Implementation of safe and efficient autonomous driving systems requires accurate
prediction of the long-term trajectories of surrounding vehicles. High uncertainty in traffic …
prediction of the long-term trajectories of surrounding vehicles. High uncertainty in traffic …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
A multi-modal vehicle trajectory prediction framework via conditional diffusion model: A coarse-to-fine approach
Z Li, H Liang, H Wang, X Zheng, J Wang… - Knowledge-Based …, 2023 - Elsevier
Accurate prediction of the future motion of surrounding vehicles is crucial for ensuring the
safety of motion planning in autonomous vehicles. However, it is challenging to perform …
safety of motion planning in autonomous vehicles. However, it is challenging to perform …
Intention-convolution and hybrid-attention network for vehicle trajectory prediction
Trajectory prediction aims to estimate the future location of the vehicle based on its historical
motion state, which is essential for driving decision-making and local motion planning of …
motion state, which is essential for driving decision-making and local motion planning of …
Conditional generative neural system for probabilistic trajectory prediction
J Li, H Ma, M Tomizuka - 2019 IEEE/RSJ International …, 2019 - ieeexplore.ieee.org
Effective understanding of the environment and accurate trajectory prediction of surrounding
dynamic obstacles are critical for intelligent systems such as autonomous vehicles and …
dynamic obstacles are critical for intelligent systems such as autonomous vehicles and …
Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Human driver behavior prediction based on urbanflow
How autonomous vehicles and human drivers share public transportation systems is an
important problem, as fully automatic transportation environments are still a long way off …
important problem, as fully automatic transportation environments are still a long way off …
Long-term prediction of vehicle trajectory using recurrent neural networks
A Benterki, V Judalet, M Choubeila… - IECON 2019-45th …, 2019 - ieeexplore.ieee.org
The expectations regarding autonomous vehicles are very high to transform the future
mobility and ensure more road safety. Autonomous driving system should be able in the …
mobility and ensure more road safety. Autonomous driving system should be able in the …
Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model
There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction.
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …