20 citations · 76 across the 23 of their papers we have counts for
23 papers
AdaMap: High-Scalable Real-Time Cooperative Perception at the Edge
Qiang Liu, Yongjie Xue, Yuru Zhang +2
Cooperative perception is the key approach to augment the perception of connected and automated vehicles (CAVs) toward safe autonomous driving. However, it is challenging to achiev…
Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control
Zhouqiao Zhao, Xishun Liao, Amr Abdelraouf +4
Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used.…
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles. Existing methods mainly rely on generic trajectory predictions…
Visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges
Yueyang Liu, Xiaolong Tu, Dawei Chen +4
A Mobility Digital Twin is an emerging implementation of digital twin technology in the transportation domain, which creates digital replicas for various physical mobility entities…
Confidence-based federated distillation for vision-based lane-centering
Yitao Chen, Dawei Chen, Haoxin Wang +2
A fundamental challenge of autonomous driving is maintaining the vehicle in the center of the lane by adjusting the steering angle. Recent advances leverage deep neural networks to…
MDAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Yunsheng Ma, Liangqi Yuan, Amr Abdelraouf +4
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. I…
CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers
Yunsheng Ma, Wenqian Ye, Xu Cao +4
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments. Existing approaches primarily focus…
EPAM: A Predictive Energy Model for Mobile AI
Anik Mallik, Haoxin Wang, Jiang Xie +2
Artificial intelligence (AI) has enabled a new paradigm of smart applications -- changing our way of living entirely. Many of these AI-enabled applications have very stringent late…
CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing
Yongjie Xue, Yuru Zhang, Qiang Liu +2
Crowdsourcing data from connected and automated vehicles (CAVs) is a cost-efficient way to achieve high-definition maps with up-to-date transient road information. Achieving the ma…
Metamobility: Connecting Future Mobility with Metaverse
Haoxin Wang, Ziran Wang, Dawei Chen +3
A Metaverse is a perpetual, immersive, and shared digital universe that is linked to but beyond the physical reality, and this emerging technology is attracting enormous attention…
Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior
Xishun Liao, Xuanpeng Zhao, Ziran Wang +5
Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environme…
A Study on Learning and Simulating Personalized Car-Following Driving Style
Shili Sheng, Erfan Pakdamanian, Kyungtae Han +2
Automated vehicles are gradually entering people's daily life to provide a comfortable driving experience for the users. The generic and user-agnostic automated vehicles have limit…
Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study
Yongkang Liu, Ziran Wang, Kyungtae Han +3
With the rapid development of intelligent vehicles and Advanced Driver-Assistance Systems (ADAS), a new trend is that mixed levels of human driver engagements will be involved in t…
Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections
Ziran Wang, Kyungtae Han, Prashant Tiwari
Digital Twin, as an emerging technology related to Cyber-Physical Systems (CPS) and Internet of Things (IoT), has attracted increasing attentions during the past decade. Conceptual…
A Game Theory Based Ramp Merging Strategy for Connected and Automated Vehicles in the Mixed Traffic: A Unity-SUMO Integrated Platform
Xishun Liao, Xuanpeng Zhao, Guoyuan Wu +4
Ramp merging is considered as one of the major causes of traffic congestion and accidents because of its chaotic nature. With the development of connected and automated vehicle (CA…
Motion Estimation of Connected and Automated Vehicles under Communication Delay and Packet Loss of V2X Communications
Ziran Wang, Kyungtae Han, Prashant Han
The emergence of the connected and automated vehicle (CAV) technology enables numerous advanced applications in our transportation system, benefiting our daily travels in terms of…
Planning for Automated Vehicles with Human Trust
Shili Sheng, Erfan Pakdamanian, Kyungtae Han +4
Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider…
Augmented Reality-Based Advanced Driver-Assistance System for Connected Vehicles
Ziran Wang, Kyungtae Han, Prashant Tiwari
With the development of advanced communication technology, connected vehicles become increasingly popular in our transportation systems, which can conduct cooperative maneuvers wit…
Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles
Yongkang Liu, Ziran Wang, Kyungtae Han +3
With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the transportation syste…
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Zhenyu Shou, Ziran Wang, Kyungtae Han +3
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent…
Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
Jianyu Su, Peter A. Beling, Rui Guo +1
The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems. Acceleration prediction is important…
A Case Study of Trust on Autonomous Driving
Shili Sheng, Erfan Pakdamanian, Kyungtae Han +4
As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and…
Lookup Table-Based Consensus Algorithm for Real-Time Longitudinal Motion Control of Connected and Automated Vehicles
Ziran Wang, Kyuntae Han, BaekGyu Kim +2
Connected and automated vehicle (CAV) technology is one of the promising solutions to addressing the safety, mobility and sustainability issues of our current transportation system…