558 citations · 1.5k across the 22 of their papers we have counts for
18 papers · 1 filter
Multi-View Adaptive Fusion Network for 3D Object Detection
Guojun Wang, Bin Tian, Yachen Zhang +3
3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both…
MSAF: Multimodal Split Attention Fusion
Lang Su, Chuqing Hu, Guofa Li +1
Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world. While making a prediction, the human brain tend…
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
Hong Shu, Teng Liu, Xingyu Mu +1
Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to tr…
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
Teng Liu, Wenhao Tan, Xiaolin Tang +3
Motivated by the concerns on transported fuel consumption and global air pollution, industrial engineers, and academic researchers have made many efforts to construct more efficien…
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
Teng Liu, Yang Xing, Long Chen +2
Parallel driving is a novel framework to synthesize vehicle intelligence and transport automation. This article aims to define digital quadruplets in parallel driving. In the cyber…
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
Teng Liu, Bo Wang, Dongpu Cao +2
To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy…
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
Teng Liu, Wenhao Tan, Xiaolin Tang +2
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology. This approach is bi…
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles
Teng Liu, Bin Tian, Yunfeng Ai +3
As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and contro…
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
Jiangdong Liao, Teng Liu, Xiaolin Tang +3
Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making p…
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
Teng Liu, Xingyu Mu, Xiaolin Tang +3
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein…
Decision-Making in Driver-Automation Shared Control: A Review and Perspectives
Wenshuo Wang, Xiaoxiang Na, Dongpu Cao +4
Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver's abilities to control. The human driver, a…
CenterNet3D: An Anchor Free Object Detector for Point Cloud
Guojun Wang, Jian Wu, Bin Tian +3
Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, howev…
Game Theoretic Approaches in Vehicular Networks: A Survey
Zemin Sun, Yanheng Liu, Jian Wang +2
In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs)…
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Ying Li, Lingfei Ma, Zilong Zhong +4
Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated…
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review
Yaodong Cui, Ren Chen, Wenbo Chu +4
Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic…
A Unified Multi-scale and Multi-task Learning Framework for Driver Behaviors Reasoning
Yang Xing, Chen Lv, Dongpu Cao +1
Mutual understanding between driver and vehicle is critically important to the design of intelligent vehicles and customized interaction interface. In this study, a unified driver…
Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns
K. Shu, H. Yu, X. Chen +4
Left-turn planning is one of the formidable challenges for autonomous vehicles, especially at unsignalized intersections due to the unknown intentions of oncoming vehicles. This pa…
Human-Machine Collaboration for Automated Vehicles via an Intelligent Two-Phase Haptic Interface
Chen Lv, Yutong Li, Yang Xing +4
Prior to realizing fully autonomous driving, human intervention will be required periodically to guarantee vehicle safety. This fact poses a new challenge in human-machine interact…