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 …
Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
Autonomous driving has achieved significant milestones in research and development over
the last decade. There is increasing interest in the field as the deployment of self-operating …
the last decade. There is increasing interest in the field as the deployment of self-operating …
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 …
Argoverse 2: Next generation datasets for self-driving perception and forecasting
We introduce Argoverse 2 (AV2)-a collection of three datasets for perception and forecasting
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …
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 …
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 …
Tnt: Target-driven trajectory prediction
Predicting the future behavior of moving agents is essential for real world applications. It is
challenging as the intent of the agent and the corresponding behavior is unknown and …
challenging as the intent of the agent and the corresponding behavior is unknown and …
Deep reinforcement learning for autonomous driving: A survey
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …
(RL) has become a powerful learning framework now capable of learning complex policies …
Prediction-uncertainty-aware decision-making for autonomous vehicles
Motion prediction is the fundamental input for decision-making in autonomous vehicles. The
current motion prediction solutions are designed with a strong reliance on black box …
current motion prediction solutions are designed with a strong reliance on black box …
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …
for safe and efficient operation of connected automated vehicles under complex driving …