Planning-oriented autonomous driving
Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
Motiondiffuser: Controllable multi-agent motion prediction using diffusion
We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …
trajectories over multiple agents. Such representation has several key advantages: first, our …
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 …
Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving
Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …
predictions of interactive behaviors between traffic participants. This paper tackles the …
Lanercnn: Distributed representations for graph-centric motion forecasting
Forecasting the future behaviors of dynamic actors is an important task in many robotics
applications such as self-driving. It is extremely challenging as actors have latent intentions …
applications such as self-driving. It is extremely challenging as actors have latent intentions …
Learning to predict vehicle trajectories with model-based planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review
DE Benrachou, S Glaser, M Elhenawy… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Human errors contribute to 94%(±2.2%) of road crashes resulting in fatal/non-fatal
causalities, vehicle damages and a predicament in the pathway to safer road systems …
causalities, vehicle damages and a predicament in the pathway to safer road systems …
Intention-aware vehicle trajectory prediction based on spatial-temporal dynamic attention network for internet of vehicles
X Chen, H Zhang, F Zhao, Y Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV).
With the help of deep learning and big data, it is possible to understand the between-vehicle …
With the help of deep learning and big data, it is possible to understand the between-vehicle …
Exploring recurrent long-term temporal fusion for multi-view 3d perception
Long-term temporal fusion is a crucial but often overlooked technique in camera-based
Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner …
Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner …
Laformer: Trajectory prediction for autonomous driving with lane-aware scene constraints
Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …
trajectory prediction models which condition future trajectories on observed trajectories …