A novel multimodal vehicle path prediction method based on temporal convolutional networks
MN Azadani, A Boukerche - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Accurate and reliable prediction of future motions of the nearby agents and effective
environment understanding will contribute to high-quality and meticulous path planning for …
environment understanding will contribute to high-quality and meticulous path planning for …
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 …
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 …
Multimodal vehicular trajectory prediction with inverse reinforcement learning and risk aversion at urban unsignalized intersections
Understanding human drivers' intentions and predicting their future motions are significant to
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …
Exploring dynamic context for multi-path trajectory prediction
To accurately predict future positions of different agents in traffic scenarios is crucial for
safely deploying intelligent autonomous systems in the real-world environment. However, it …
safely deploying intelligent autonomous systems in the real-world environment. However, it …
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 …
A road-aware neural network for multi-step vehicle trajectory prediction
Multi-step vehicle trajectory prediction has been of great significance for location-based
services, eg, actionable advertising. Prior works focused on adopting pattern-matching …
services, eg, actionable advertising. Prior works focused on adopting pattern-matching …
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 …
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 …
Map-free trajectory prediction in traffic with multi-level spatial-temporal modeling
To handle two shortcomings of existing methods,(i) nearly all models rely on the high-
definition (HD) maps, yet the map information is not always available in real traffic scenes …
definition (HD) maps, yet the map information is not always available in real traffic scenes …