Search Results for author: Chen Lv

Found 23 papers, 8 papers with code

Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles

no code implementations12 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.

Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

no code implementations12 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.

Autonomous Driving Ethics

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

no code implementations30 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.

Autonomous Driving Ethics

How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?

no code implementations11 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.

Autonomous Driving Decision Making +1

Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

1 code implementation1 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.

Autonomous Navigation Decision Making +2

Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity

no code implementations5 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.

Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving

1 code implementation24 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.

Autonomous Driving Decision Making +3

STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving

1 code implementation31 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.

Autonomous Driving

Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

no code implementations1 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.

Autonomous Driving Decision Making +1

Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

1 code implementation20 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.

Atari Games Q-Learning +2

Single UHD Image Dehazing via Interpretable Pyramid Network

1 code implementation17 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.

Image Dehazing Single Image Dehazing

Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention Network

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.

Autonomous Vehicles Decision Making +3

RHONN Modelling-enabled Nonlinear Predictive Control for Lateral Dynamics Stabilization of An In-wheel Motor Driven Vehicle

no code implementations21 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.

Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits

no code implementations5 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.

Decision Making

Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving

1 code implementation26 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.

Autonomous Driving reinforcement-learning +1

Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks

no code implementations3 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.


Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving

no code implementations23 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.

Autonomous Driving Model-based Reinforcement Learning +2

A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks

no code implementations19 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.

Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours

no code implementations14 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.

Decision Making motion prediction

Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation

no code implementations24 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.

Head Pose Estimation

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