Interaction between the background vehicles (BVs) and automated vehicles...
An accurate trajectory prediction is crucial for safe and efficient
auto...
Despite some successful applications of goal-driven navigation, existing...
Intersection is one of the most challenging scenarios for autonomous dri...
This paper proposes a novel deep learning framework for multi-modal moti...
Human intention prediction provides an augmented solution for the design...
Decision-making is critical for lane change in autonomous driving.
Reinf...
Massive practical works addressed by Deep Q-network (DQN) algorithm have...
Currently, most single image dehazing models cannot run an
ultra-high-re...
In this paper, a human-like driving framework is designed for autonomous...
Reinforcement learning requires skillful definition and remarkable
compu...
Predicting the behaviors of other agents on the road is critical for
aut...
Integrating trajectory prediction to the decision-making and planning mo...
To further improve the learning efficiency and performance of reinforcem...
Simultaneous trajectory prediction for multiple heterogeneous traffic
pa...
Due to the limited smartness and abilities of machine intelligence, curr...
Deep reinforcement learning (DRL) is a promising way to achieve human-li...
Currently, urban autonomous driving remains challenging because of the
c...
Predicting the future trajectory of surrounding vehicles is essential fo...
Human driving behavior modeling is of great importance for designing saf...
Predicting the future trajectory of a surrounding vehicle in congested
t...
Head pose estimation is a crucial problem for many tasks, such as driver...
Considering that human-driven vehicles and autonomous vehicles (AVs) wil...
This paper presents a novel integrated approach to deal with the decisio...
This study aims to improve the control performance and generalization
ca...