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 • 17 Apr 2024 • Chuheng Wei, Guoyuan Wu, Matthew J. Barth
Our study introduces "Feature Corrective Transfer Learning", a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts.
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 • 6 Feb 2023 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints and a Random Priority Filtering (RPF) method is proposed to conduct DFS with high performance.
no code implementations • 14 Dec 2022 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection.
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 • 22 Aug 2022 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi, Zhitong Huang
Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems.
1 code implementation • 12 Mar 2022 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
3D object detection plays a fundamental role in enabling autonomous driving, which is regarded as the significant key to unlocking the bottleneck of contemporary transportation systems from the perspectives of safety, mobility, and sustainability.
no code implementations • 28 Feb 2022 • Zhengwei Bai, Saswat Priyadarshi Nayak, Xuanpeng Zhao, Guoyuan Wu, Matthew J. Barth, Xuewei Qi, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems.
no code implementations • 28 Jan 2022 • Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems.
no code implementations • 24 Jan 2022 • Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
In this study, a \textit{Cyber Mobility Mirror (CMM)} Co-Simulation Platform is designed for enabling CDA by providing authentic perception information.
no code implementations • 19 Jan 2022 • Zhengwei Bai, Peng Hao, Wei Shangguan, Baigen Cai, Matthew J. Barth
However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system.
no code implementations • 18 Nov 2021 • Xuanpeng Zhao, Ahmed Abdo, Xishun Liao, Matthew J. Barth, Guoyuan Wu
In this study, we investigate cybersecurity risks of a representative cooperative traffic management application, i. e., highway on-ramp merging, in a mixed traffic environment.
no code implementations • 20 Feb 2019 • Ziran Wang, Kyuntae Han, BaekGyu Kim, Guoyuan Wu, Matthew J. Barth
Different from previous studies in this field where control gains of the consensus algorithm are pre-determined and fixed, we develop algorithms to build up a lookup table, searching for the ideal control gains with respect to different initial conditions of CAVs in real time.