Scenario understanding and motion prediction for autonomous vehicles—review and comparison
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
High-definition maps: Comprehensive survey, challenges and future perspectives
G Elghazaly, R Frank, S Harvey… - IEEE Open Journal of …, 2023 - ieeexplore.ieee.org
In cooperative, connected, and automated mobility (CCAM), the more automated vehicles
can perceive, model, and analyze the surrounding environment, the more they become …
can perceive, model, and analyze the surrounding environment, the more they become …
Query-centric trajectory prediction
Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
Motion transformer with global intention localization and local movement refinement
Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …
make safe decisions. Existing works explore to directly predict future trajectories based on …
Hivt: Hierarchical vector transformer for multi-agent motion prediction
Accurately predicting the future motions of surrounding traffic agents is critical for the safety
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction
Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …
problems in autonomous driving. Applying deep learning to this problem requires fusing …
Leapfrog diffusion model for stochastic trajectory prediction
To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a
sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have …
sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have …
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 …
Vip3d: End-to-end visual trajectory prediction via 3d agent queries
Perception and prediction are two separate modules in the existing autonomous driving
systems. They interact with each other via hand-picked features such as agent bounding …
systems. They interact with each other via hand-picked features such as agent bounding …
Mtr++: Multi-agent motion prediction with symmetric scene modeling and guided intention querying
Motion prediction is crucial for autonomous driving systems to understand complex driving
scenarios and make informed decisions. However, this task is challenging due to the diverse …
scenarios and make informed decisions. However, this task is challenging due to the diverse …