Motionlm: Multi-agent motion forecasting as language modeling
Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Ganet: Goal area network for motion forecasting
Predicting the future motion of road participants is crucial for autonomous driving but is
extremely challenging due to staggering motion uncertainty. Recently, most motion …
extremely challenging due to staggering motion uncertainty. Recently, most motion …
Bootstrap motion forecasting with self-consistent constraints
We present a novel framework to bootstrap Motion forecasting with Self-consistent
Constraints (MISC). The motion forecasting task aims at predicting future trajectories of …
Constraints (MISC). The motion forecasting task aims at predicting future trajectories of …
From prediction to planning with goal conditioned lane graph traversals
The field of motion prediction for automated driving has seen tremendous progress recently,
bearing ever-more mighty neural network architectures. Leveraging these powerful models …
bearing ever-more mighty neural network architectures. Leveraging these powerful models …
Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with
various open research questions. State-of-the-art approaches regress trajectories either in a …
various open research questions. State-of-the-art approaches regress trajectories either in a …
SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs
Trajectory prediction in autonomous driving relies on accurate representation of all relevant
contexts of the driving scene including traffic participants, road topology, traffic signs as well …
contexts of the driving scene including traffic participants, road topology, traffic signs as well …
Convex risk-bounded continuous-time trajectory planning and tube design in uncertain nonconvex environments
In this paper, we address the trajectory planning problem in uncertain nonconvex static and
dynamic environments that contain obstacles with probabilistic location, size, and geometry …
dynamic environments that contain obstacles with probabilistic location, size, and geometry …
A Hierarchical Hybrid Learning Framework for Multi-Agent Trajectory Prediction
Accurate trajectory prediction for neighboring agents is crucial for autonomous vehicles
navigating complex scenes. Recent deep learning (DL) methods excel in encoding complex …
navigating complex scenes. Recent deep learning (DL) methods excel in encoding complex …
LFENav: LLM-Based Frontiers Exploration for Visual Semantic Navigation
Y Shi, J Liu, X Zheng - IFIP International Conference on Artificial …, 2024 - Springer
Robot navigation in an unknown environment is a challenge task, due to the lack of spatial
awareness and semantic understanding of the environment. Previous works mostly rely on …
awareness and semantic understanding of the environment. Previous works mostly rely on …