no code implementations • 13 Dec 2024 • Songyan Zhang, Wenhui Huang, Zihui Gao, Hao Chen, Chen Lv
The emergence of general human knowledge and impressive logical reasoning capacity in rapidly progressed vision-language models (VLMs) have driven increasing interest in applying VLMs to high-level autonomous driving tasks, such as scene understanding and decision-making.
no code implementations • 17 Nov 2024 • Lei Yang, Xinyu Zhang, Jun Li, Chen Wang, Zhiying Song, Tong Zhao, Ziying Song, Li Wang, Mo Zhou, Yang shen, Kai Wu, Chen Lv
Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby improving the safety of autonomous driving.
1 code implementation • 19 Sep 2024 • Shiyu Fang, Jiaqi Liu, Mingyu Ding, Yiming Cui, Chen Lv, Peng Hang, Jian Sun
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.
no code implementations • 18 Jul 2024 • Qingfan Wang, Dongyang Xu, Gaoyuan Kuang, Chen Lv, Shengbo Eben Li, Bingbing Nie
Results demonstrate the superior performance of our model, with a significant improvement in most metrics.
1 code implementation • 23 May 2024 • Qingyuan Wu, Simon Sinong Zhan, YiXuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning (RL).
no code implementations • 16 May 2024 • Hao Chen, Xiangkun He, Shuo Cheng, Chen Lv
Modeling of nonlinear behaviors with physical-based models poses challenges.
no code implementations • 16 May 2024 • Hao Chen, Chen Lv
The various conditions under the double-lane change scenarios are built on the CarSim/Simulink co-simulation platform, and extensive comparisons are conducted with the linear MPC (LMPC) and nonlinear MPC (NMPC) informed by the physics-based model.
no code implementations • CVPR 2024 • Jianwu Fang, Lei-Lei Li, Junfei Zhou, Junbin Xiao, Hongkai Yu, Chen Lv, Jianru Xue, Tat-Seng Chua
This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories.
1 code implementation • 5 Feb 2024 • Qingyuan Wu, Simon Sinong Zhan, YiXuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang
To address these challenges, we present a novel Auxiliary-Delayed Reinforcement Learning (AD-RL) method that leverages auxiliary tasks involving short delays to accelerate RL with long delays, without compromising performance in stochastic environments.
no code implementations • 4 Feb 2024 • Haochen Liu, Zhiyu Huang, Wenhui Huang, Haohan Yang, Xiaoyu Mo, Chen Lv
First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-wise perceptions.
no code implementations • 12 Jun 2023 • Yining Ma, Wei Jiang, Lingtong Zhang, Junyi Chen, Hong Wang, Chen Lv, Xuesong Wang, Lu Xiong
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
no code implementations • 12 May 2023 • Long Chen, Yuchen Li, Chao Huang, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
no code implementations • 30 Mar 2023 • Long Chen, Yuchen Li, Chao Huang, Bai Li, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Xiaoxiang Na, Zixuan Li, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits.
no code implementations • 11 Jan 2023 • Wenbo Shao, Yanchao Xu, Jun Li, Chen Lv, Weida Wang, Hong Wang
The results indicate that the deep ensemble-based method has advantages in improving prediction robustness and estimating epistemic uncertainty.
1 code implementation • 1 Jan 2023 • Wenhui Huang, Yanxin Zhou, Xiangkun He, Chen Lv
Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue.
no code implementations • 5 Oct 2022 • Qun Wang, Haoxuan Dong, Fei Ju, Weichao Zhuang, Chen Lv, Liangmo Wang, Ziyou Song
A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors.
3 code implementations • 6 Sep 2022 • Yanchao Xu, Wenbo Shao, Jun Li, Kai Yang, Weida Wang, Hua Huang, Chen Lv, Hong Wang
Then, the behaviors of traffic light violations in SIND are recorded.
1 code implementation • 24 Aug 2022 • Haochen Liu, Zhiyu Huang, Xiaoyu Mo, Chen Lv
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures.
1 code implementation • 31 Jul 2022 • Haochen Liu, Zhiyu Huang, Chen Lv
Therefore, this paper proposes a novel Multi-modal Hierarchical Transformer network that fuses the vectorized (agent motion) and visual (scene flow, map, and occupancy) modalities and jointly predicts the flow and occupancy of the scene.
no code implementations • 1 Jul 2022 • Jingda Wu, Wenhui Huang, Niels de Boer, Yanghui Mo, Xiangkun He, Chen Lv
Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL.
1 code implementation • 20 Jun 2022 • Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang, Chen Lv
We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.
1 code implementation • 17 Feb 2022 • Boxue Xiao, Zhuoran Zheng, Xiang Chen, Chen Lv, Yunliang Zhuang, Tao Wang
Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time.
1 code implementation • IEEE Transactions on Intelligent Transportation Systems 2022 • Xiaoyu Mo, Zhiyu Huang, Yang Xing, Chen Lv
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations.
no code implementations • 21 Jan 2022 • Hao Chen, Junzhi Zhang, Chen Lv
To further advance the lateral stabilization performance of the IMDV, in this paper a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method.
no code implementations • 5 Jan 2022 • Peng Hang, Chao Huang, Zhongxu Hu, Chen Lv
To realize human-like driving and personalized decision-making, driving aggressiveness is first defined for AVs.
no code implementations • 5 Jan 2022 • Peng Hang, Chao Huang, Zhongxu Hu, Chen Lv
To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users.
1 code implementation • 26 Sep 2021 • Jingda Wu, Zhiyu Huang, Wenhui Huang, Chen Lv
A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm.
no code implementations • 3 Sep 2021 • Tianci Yang, Carlos Murguia, Chen Lv
In this manuscript, we propose a novel attack detection scheme that leverage real-time sensor/network data and physics-based mathematical models of vehicles in the platoon.
no code implementations • 23 Jun 2021 • Jingda Wu, Zhiyu Huang, Chen Lv
Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance.
no code implementations • 19 Jun 2021 • Tianci Yang, Carlos Murguia, Dragan Nešić, Chen Lv
The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side.
no code implementations • 24 Mar 2021 • Zhongxu Hu, Chen Lv, Yanxin Zhou, Yiran Zhang, Wenhui Huang
To handle the error of the head pose estimation model, this paper proposes an adaptive Kalman Filter.
no code implementations • 14 Mar 2021 • Peng Hang, Chao Huang, Zhongxu Hu, Yang Xing, Chen Lv
To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours.
no code implementations • 24 May 2020 • Zhongxu Hu, Yang Xing, Chen Lv, Peng Hang, Jie Liu
This paper proposes a novel Bernoulli heatmap for head pose estimation from a single RGB image.