Maneuver-based trajectory prediction for self-driving cars using spatio-temporal convolutional networks
B Mersch, T Höllen, K Zhao… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
The ability to predict the future movements of other vehicles is a subconscious and effortless
skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for …
skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for …
Interacting vehicle trajectory prediction with convolutional recurrent neural networks
S Mukherjee, S Wang, A Wallace - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Anticipating the future trajectories of surrounding vehicles is a crucial and challenging task
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …
A survey on deep-learning approaches for vehicle trajectory prediction in autonomous driving
With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …
issue, making urgent demands for more intelligent perception and planning systems. Self …
Multimodal trajectory predictions for autonomous driving using deep convolutional networks
Autonomous driving presents one of the largest problems that the robotics and artificial
intelligence communities are facing at the moment, both in terms of difficulty and potential …
intelligence communities are facing at the moment, both in terms of difficulty and potential …
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 …
UST: Unifying spatio-temporal context for trajectory prediction in autonomous driving
Trajectory prediction has always been a challenging problem for autonomous driving, since
it needs to infer the latent intention from the behaviors and interactions from traffic …
it needs to infer the latent intention from the behaviors and interactions from traffic …
Attention based vehicle trajectory prediction
K Messaoud, I Yahiaoui… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Self-driving vehicles need to continuously analyse the driving scene, understand the
behavior of other road users and predict their future trajectories in order to plan a safe …
behavior of other road users and predict their future trajectories in order to plan a safe …
Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving
Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for
urban autonomous driving where there are complex contextual factors, such as lane …
urban autonomous driving where there are complex contextual factors, such as lane …
Toward driver intention prediction for intelligent vehicles: A deep learning approach
MN Azadani, A Boukerche - 2021 IEEE 46th Conference on …, 2021 - ieeexplore.ieee.org
High-level scene understanding and situational awareness are fundamental for autonomous
vehicles before being widely used on public roads in a thoroughly efficient and safe manner …
vehicles before being widely used on public roads in a thoroughly efficient and safe manner …