83 citations · 344 across the 17 of their papers we have counts for
17 papers
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
Jintao Ren, Ziqian Bi, Qian Niu +16
An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications. State-of-the-art advancements in m…
Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions
Sheng Wang, Yingbing Chen, Jie Cheng +4
Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose…
Resistive memory-based zero-shot liquid state machine for multimodal event data learning
Ning Lin, Shaocong Wang, Yi Li +19
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains…
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should a…
CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images
Zhenhua Xu, Yuxiang Sun, Lujia Wang +1
Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. M…
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
Xiaodong Mei, Yuxiang Sun, Yuying Chen +2
In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we pro…
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to mainta…
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
Yuying Chen, Congcong Liu, Xiaodong Mei +2
Accurate pedestrian trajectory prediction is of great importance for downstream tasks such as autonomous driving and mobile robot navigation. Fully investigating the social interac…
Activate or Not: Learning Customized Activation
Ningning Ma, Xiangyu Zhang, Ming Liu +1
We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searc…
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Tianyu Liu, Qinghai Liao, Lu Gan +28
Since early 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly across the world. As at the date of writing this article, the disease has been globally reported in 223…
Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation
Ting Sun, Yuxiang Sun, Ming Liu +1
Moving objects can greatly jeopardize the performance of a visual simultaneous localization and mapping (vSLAM) system which relies on the static-world assumption. Motion removal h…
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
Lei Tai, Peng Yun, Yuying Chen +3
End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually…
Real-Time Subpixel Fast Bilateral Stereo
Rui Fan, Yanan Liu, Mohammud Junaid Bocus +2
Stereo vision technique has been widely used in robotic systems to acquire 3-D information. In recent years, many researchers have applied bilateral filtering in stereo vision to a…
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
Oleksii Zhelo, Jingwei Zhang, Lei Tai +2
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal ext…
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
Giuseppe Paolo, Lei Tai, Ming Liu
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep…
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
Lei Tai, Jingwei Zhang, Ming Liu +2
Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learnin…
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
Lei Tai, Ming Liu
Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent st…