The long short-term memory (LSTM) model is one of the most commonly used vehicle
trajectory predicting models. In this paper, we study two problems of the existing LSTM …

Predicting the trajectory of neighboring vehicles is closely related to the driving safety of
intelligent vehicles and supports driving assistance. This article proposes a dual-attention …

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …

This article describes a human-like automated lane-changing system to mimic the lane-
changing maneuver of human drivers in order to make automated vehicles more realistic. In …

An accurate vehicle trajectory prediction promotes understanding of the traffic environment
and enables task criticality assessment in advanced driver assistance systems (ADASs) in …

In this paper, we study how to learn an appropriate lane changing strategy for autonomous
vehicles by using deep reinforcement learning. We show that the reward of the system …

Developing an autonomous driving system which can generate human-like actions requires
the ability to utilize the basic driving skills learned from the driving data. The efficiency of the …

Non-player character vehicles (NPC-Vs) denote crucial components of autonomous driving
systems (ADSs) and autonomous driving assistance algorithms (ADAAs) when conducting …

Experience replay memory in reinforcement learning enables agents to remember and
reuse past experiences. Most of the reinforcement models are subject to single experience …

A novel driving-behavior-oriented method is proposed in this paper for improving trajectory
planning performance of autonomous vehicle driving on urban structural road. Differ from …