Maneuver-aware pooling for vehicle trajectory prediction
Autonomous vehicles should be able to predict the future states of its environment and
respond appropriately. Specifically, predicting the behavior of surrounding human drivers is …
respond appropriately. Specifically, predicting the behavior of surrounding human drivers is …
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability
to scale to different data domains and the impact of larger dataset sizes on their …
to scale to different data domains and the impact of larger dataset sizes on their …
[HTML][HTML] Injecting knowledge in data-driven vehicle trajectory predictors
Vehicle trajectory prediction tasks have been commonly tackled from two distinct
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
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 …
Exploring attention GAN for vehicle motion prediction
C Gómez-Huélamo, MV Conde, M Ortiz… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
The design of a safe and reliable Autonomous Driving stack (ADS) is one of the most
challenging tasks of our era. These ADS are expected to be driven in highly dynamic …
challenging tasks of our era. These ADS are expected to be driven in highly dynamic …
AI-TP: Attention-based interaction-aware trajectory prediction for autonomous driving
K Zhang, L Zhao, C Dong, L Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic
management and autonomous driving systems. However it presents unique challenges due …
management and autonomous driving systems. However it presents unique challenges due …
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 …
Hierarchical motion encoder-decoder network for trajectory forecasting
Q Xue, S Li, X Li, J Zhao, W Zhang - arXiv preprint arXiv:2111.13324, 2021 - arxiv.org
Trajectory forecasting plays a pivotal role in the field of intelligent vehicles or social robots.
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …
Pre-training on Synthetic Driving Data for Trajectory Prediction
Accumulating substantial volumes of real-world driving data proves pivotal in the realm of
trajectory forecasting for autonomous driving. Given the heavy reliance of current trajectory …
trajectory forecasting for autonomous driving. Given the heavy reliance of current trajectory …