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 …
Deep predictive autonomous driving using multi-agent joint trajectory prediction and traffic rules
Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …
understand complex and dynamic environment. This understanding consists of predicting …
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 …
Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles
Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs
to know the current state of the surrounding objects but also their possible states in the …
to know the current state of the surrounding objects but also their possible states in the …
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 …
Context and intention aware planning for urban driving
We present a novel autonomous driving system which uses the road contextual information
and intentions of other road users for urban driving. Unlike highways, urban environments …
and intentions of other road users for urban driving. Unlike highways, urban environments …
Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network
B Dong, H Liu, Y Bai, J Lin, Z Xu, X Xu… - arXiv preprint arXiv …, 2021 - arxiv.org
Predicting future trajectories of surrounding obstacles is a crucial task for autonomous
driving cars to achieve a high degree of road safety. There are several challenges in …
driving cars to achieve a high degree of road safety. There are several challenges in …
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 …
Trajectory prediction with graph-based dual-scale context fusion
Motion prediction for traffic participants is essential for a safe and robust automated driving
system, especially in cluttered urban environments. However, it is highly challenging due to …
system, especially in cluttered urban environments. However, it is highly challenging due to …
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 …