Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
A review of vision-based traffic semantic understanding in ITSs
J Chen, Q Wang, HH Cheng, W Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …
situations and emergencies more accurately and provide a more accurate basis for anomaly …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
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 …
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 …
Model-based imitation learning for urban driving
An accurate model of the environment and the dynamic agents acting in it offers great
potential for improving motion planning. We present MILE: a Model-based Imitation …
potential for improving motion planning. We present MILE: a Model-based Imitation …
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
As autonomous driving systems mature, motion forecasting has received increasing
attention as a critical requirement for planning. Of particular importance are interactive …
attention as a critical requirement for planning. Of particular importance are interactive …
Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting
Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …
but is challenging due to the complex interaction between agents and the uncertainty in …
Improving multi-agent trajectory prediction using traffic states on interactive driving scenarios
Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …
intersections, and roundabouts are challenging due to the high density of agents, varying …