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Found 19 papers, 3 papers with code
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning
Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits.
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits.
AutoMine: An Unmanned Mine Dataset
The main contributions of the AutoMine dataset are as follows: 1. The first autonomous driving dataset for perception and localization in mine scenarios.
MSAF: Multimodal Split Attention Fusion
Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world.
Ranked #15 on
Action Recognition
on NTU RGB+D
Multi-View Adaptive Fusion Network for 3D Object Detection
3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving.
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles.
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles.
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles.
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology.
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles
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 control.
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
The advantages of the proposed framework in convergence rate and control performance are illuminated.
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL).
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
This paper focusing on helping the relevant researchers realize the state-of-the-art of HEVs energy management field and also recognize its future development direction.
CenterNet3D: An Anchor Free Object Detector for Point Cloud
However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries.
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles
In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis.
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review
Autonomous vehicles were experiencing rapid development in the past few years.
Affordance Learning In Direct Perception for Autonomous Driving
Our results indicate that this method could act as a cheaper way for training data collection in autonomous driving.
