Spatial-temporal ConvLSTM for vehicle driving intention prediction
Driving intention prediction from a bird's-eye view has always been an active research area.
However, existing research, on one hand, has only focused on predicting lane change …
However, existing research, on one hand, has only focused on predicting lane change …
Interpretable decision-making for autonomous vehicles at highway on-ramps with latent space reinforcement learning
H Wang, H Gao, S Yuan, H Zhao… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
This paper presents a latent space reinforcement learning method for interpretable decision-
making of autonomous vehicles at highway on-ramps. This method is based on the latent …
making of autonomous vehicles at highway on-ramps. This method is based on the latent …
Continual multi-agent interaction behavior prediction with conditional generative memory
Multi-agent trajectory prediction plays a crucial role in robotics and autonomous driving. The
current mainstream research focuses on how to achieve accurate prediction on one large …
current mainstream research focuses on how to achieve accurate prediction on one large …
Causal-based time series domain generalization for vehicle intention prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
In recent years, the expansion of internet technology and advancements in automation have
brought significant attention to autonomous driving technology. Major automobile …
brought significant attention to autonomous driving technology. Major automobile …
Vehicle trajectory prediction using intention-based conditional variational autoencoder
Vehicle trajectory prediction has been an active research area in autonomous driving. In a
real traffic scene, autonomous vehicle needs to predict future motion of surrounding vehicles …
real traffic scene, autonomous vehicle needs to predict future motion of surrounding vehicles …
Pomdp and hierarchical options mdp with continuous actions for autonomous driving at intersections
When applying autonomous driving technology to real-world scenarios, environmental
uncertainties make the development of decision-making algorithms difficult. Modeling the …
uncertainties make the development of decision-making algorithms difficult. Modeling the …
Learning highway ramp merging via reinforcement learning with temporally-extended actions
Several key scenarios, such as intersection navigation, lane changing, and ramp merging,
are active areas of research in autonomous driving. In order to properly navigate these …
are active areas of research in autonomous driving. In order to properly navigate these …
Automatically generated curriculum based reinforcement learning for autonomous vehicles in urban environment
We address the problem of learning autonomous driving behaviors in urban intersections
using deep reinforcement learning (DRL). DRL has become a popular choice for creating …
using deep reinforcement learning (DRL). DRL has become a popular choice for creating …
A taxonomy and review of algorithms for modeling and predicting human driver behavior
K Brown, K Driggs-Campbell… - arXiv preprint arXiv …, 2020 - arxiv.org
We present a review and taxonomy of 200 models from the literature on driver behavior
modeling. We begin by introducing a mathematical framework for describing the dynamics of …
modeling. We begin by introducing a mathematical framework for describing the dynamics of …