no code implementations • 6 May 2024 • Ziye Qin, Siyan Li, Guoyuan Wu, Matthew J. Barth, Amr Abdelraouf, Rohit Gupta, Kyungtae Han
The results show that the Personalized Transformer Encoder improves the accuracy of predicting driver decision-making in the dilemma zone by 3. 7% to 12. 6% compared to the Generic Transformer Encoder, and by 16. 8% to 21. 6% over the binary logistic regression model.
1 code implementation • 17 Apr 2024 • Chuheng Wei, Guoyuan Wu, Matthew J. Barth, Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems.
no code implementations • 13 Mar 2024 • Anik Mallik, Dawei Chen, Kyungtae Han, Jiang Xie, Zhu Han
With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs.
1 code implementation • 7 Dec 2023 • Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Peiran Liu, Juanwu Lu, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Aniket Bera, James M. Rehg, Ziran Wang
Autonomous driving (AD) has made significant strides in recent years.
1 code implementation • 25 Oct 2023 • Jessica Echterhoff, An Yan, Kyungtae Han, Amr Abdelraouf, Rohit Gupta, Julian McAuley
In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationalize and explain driver or vehicle behavior.
no code implementations • 19 Oct 2023 • Xiaolong Tu, Anik Mallik, Dawei Chen, Kyungtae Han, Onur Altintas, Haoxin Wang, Jiang Xie
In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices.
no code implementations • 10 Sep 2023 • Zhouqiao Zhao, Xishun Liao, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Matthew J. Barth, Guoyuan Wu
In the online component, driver feedback is used to update the driving gap preference in real time.
no code implementations • 14 Aug 2023 • Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles.
no code implementations • 5 Jun 2023 • Yitao Chen, Dawei Chen, Haoxin Wang, Kyungtae Han, Ming Zhao
Machine learning-based steering angle prediction needs to consider the vehicle's limitation in uploading large amounts of potentially private data for model training.
1 code implementation • 13 May 2023 • Yunsheng Ma, Liangqi Yuan, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Zihao Li, Ziran Wang
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.
no code implementations • 13 May 2023 • Yunsheng Ma, Wenqian Ye, Xu Cao, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Ziran Wang
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments.
no code implementations • 2 Mar 2023 • Anik Mallik, Haoxin Wang, Jiang Xie, Dawei Chen, Kyungtae Han
Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources.
no code implementations • 17 Jan 2023 • Haoxin Wang, Ziran Wang, Dawei Chen, Qiang Liu, Hongyu Ke, Kyungtae Han
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 from different industries.
no code implementations • 2 Nov 2022 • Xishun Liao, Xuanpeng Zhao, Ziran Wang, Zhouqiao Zhao, Kyungtae Han, Rohit Gupta, Matthew J. Barth, Guoyuan Wu
The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network.
no code implementations • 7 Dec 2021 • Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen
To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions.
no code implementations • 4 May 2021 • 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.
no code implementations • 19 Jan 2021 • 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 safety, mobility, and sustainability.
no code implementations • 8 Jul 2020 • Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen
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 system.
no code implementations • 23 Jun 2020 • Zhenyu Shou, Ziran Wang, Kyungtae Han, Yongkang Liu, Prashant Tiwari, Xuan Di
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 hazards in the surrounding environment.
no code implementations • 22 Nov 2019 • Jianyu Su, Peter A. Beling, Rui Guo, Kyungtae Han
The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems.