no code implementations • 4 Apr 2024 • Juanwu Lu, Can Cui, Yunsheng Ma, Aniket Bera, Ziran Wang
In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object.
no code implementations • 14 Dec 2023 • Can Cui, Zichong Yang, Yupeng Zhou, Yunsheng Ma, Juanwu Lu, Lingxi Li, Yaobin Chen, Jitesh Panchal, Ziran Wang
We also validate that the proposed memory module considers personalized preferences and further reduces the takeover rate by up to 65. 2% compared with those without a memory module.
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 • 21 Nov 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
1 code implementation • 25 Oct 2023 • Yunsheng Ma, Juanwu Lu, Can Cui, Sicheng Zhao, Xu Cao, Wenqian Ye, Ziran Wang
We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules.
no code implementations • 12 Oct 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation.
no code implementations • 4 Oct 2023 • Liangqi Yuan, Ziran Wang, Christopher G. Brinton
The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse.
no code implementations • 19 Sep 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities.
no code implementations • 21 Aug 2023 • Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton, Stanislaw H Zak, Ziran Wang
This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV).
no code implementations • 2 Jun 2023 • Liangqi Yuan, Ziran Wang, Lichao Sun, Philip S. Yu, Christopher G. Brinton
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.
1 code implementation • 27 May 2023 • Can Cui, Yunsheng Ma, Juanwu Lu, Ziran Wang
Sensor fusion is a crucial augmentation technique for improving the accuracy and reliability of perception systems for automated vehicles under diverse driving conditions.
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.
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 • 14 Apr 2023 • Liangqi Yuan, Yunsheng Ma, Lu Su, Ziran Wang
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents.
no code implementations • 19 Mar 2023 • Vishnu Pandi Chellapandi, Liangqi Yuan, Stanislaw H /. Zak, Ziran Wang
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system.
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 • 12 Jan 2023 • Liangqi Yuan, Lu Su, Ziran Wang
This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets with and without system heterogeneity.
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.
1 code implementation • 19 Sep 2022 • Yunsheng Ma, Ziran Wang
Ensuring traffic safety and mitigating accidents in modern driving is of paramount importance, and computer vision technologies have the potential to significantly contribute to this goal.
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.
1 code implementation • 11 Aug 2020 • Ziran Wang, Guoyuan Wu, George Scora
In this paper, we introduce an open-source model "MOVESTAR" to calculate the fuel consumption and pollutant emissions of motor vehicles.
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 • 24 Jan 2020 • Zhensong Wei, Yu Jiang, Xishun Liao, Xuewei Qi, Ziran Wang, Guoyuan Wu, Peng Hao, Matthew Barth
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system.
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.