TP-FRL: An Efficient and Adaptive Trajectory Prediction Method Based on the Rule and Learning-Based Frameworks Fusion
Y Han, Q Liu, H Liu, B Wang, Z Zang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Effective trajectory prediction is of great significance for the design of intelligent driving
systems. To overcome the problems of low algorithm efficiency and insufficient scenario …
systems. To overcome the problems of low algorithm efficiency and insufficient scenario …
Tpnet: Trajectory proposal network for motion prediction
Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
Probabilistic multi-modal expected trajectory prediction based on LSTM for autonomous driving
Z Gao, M Bao, F Gao, M Tang - Proceedings of the Institution …, 2023 - journals.sagepub.com
Autonomous vehicles (AVs) need to adequately predict the trajectory space of surrounding
vehicles (SVs) in order to make reasonable decision-making and improve driving safety. In …
vehicles (SVs) in order to make reasonable decision-making and improve driving safety. In …
Driver Intention & Interaction-Aware Trajectory Forecasting via Modular Multi-Task Learning
Forecasting the trajectories of vehicles in a highway scene is a crucial task in the area of
Intelligent Transportation Systems (ITS), as the complexities of a web of driving maneuvers …
Intelligent Transportation Systems (ITS), as the complexities of a web of driving maneuvers …
Ontology-based reasoning approach for long-term behavior prediction of road users
F Fang, S Yamaguchi, A Khiat - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
A human driver determines his/her driving action by predicting future behavior of other road
users based on the consideration of relative relationships and reactions between all players …
users based on the consideration of relative relationships and reactions between all players …
IMGTP: A Unified Framework for Improving and Measuring the Generalizability of Trajectory Prediction Models
Accurately predicting nearby agents' future trajectories is fundamental for ensuring the safety
and efficiency of autonomous driving. However, existing learning-based trajectory prediction …
and efficiency of autonomous driving. However, existing learning-based trajectory prediction …
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 …
Transfollower: Long-sequence car-following trajectory prediction through transformer
Car-following refers to a control process in which the following vehicle (FV) tries to keep a
safe distance between itself and the lead vehicle (LV) by adjusting its acceleration in …
safe distance between itself and the lead vehicle (LV) by adjusting its acceleration in …
3D-MBNET: Intention based multimodal vehicle trajectory prediction with 3D social convolution
Predicting vehicle trajectories in traffic scenes is an important issue for autonomous driving
and advanced driver assistance systems. The spatial and temporal interactions between the …
and advanced driver assistance systems. The spatial and temporal interactions between the …
Ltp: Lane-based trajectory prediction for autonomous driving
The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …