近期论文
查看导师新发文章
（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）
Yi Y, Lu C*, Wang B, et al. Fusion of Gaze and Scene Information for Driving Behaviour Recognition: A Graph-Neural-Network-Based Framework [J]. IEEE Transactions on Intelligent Transportation Systems, 2023. (已录用，领域顶级期刊SCI, Q1, IF: 9.551)
Li J, Lu C*, Li P, et al. Driver-Specific Risk Recognition in Interactive Driving Scenarios using Graph Representation [J]. IEEE Transactions on Vehicular Technology, 2022 (领域顶级期刊SCI, Q1, IF: 6.239)
Lu C , Lv C, Gong J*, et al. Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains [J]. IEEE Transactions on Intelligent Transportation Systems, 2022,23(10): 17015-17026. (领域顶级期刊SCI, Q1, IF: 9.551)
Li Z, Gong J, Lu C*, et al. A Hierarchical Framework for Interactive Behaviour Prediction of Heterogeneous Traffic Participants based on Graph Neural Network [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 9102-9114. (领域顶级期刊SCI, Q1, IF: 9.551)
Hu J, Hu Y, Lu C*, et al. Integrated Path Planning for Unmanned Differential Steering Vehicles in Off-road Environment with 3D Terrains and Obstacles [J]. IEEE Transactions on Intelligent Transportation Systems, 2022,23(6): 5562-5572. (领域顶级期刊SCI, Q1, IF: 9.551)
Li Z, Gong J, Lu C*, et al. Personalized Driver Braking Behavior Modeling in the Car-Following Scenario: An Importance-Weight-Based Transfer Learning Approach [J]. IEEE Transactions on Industrial Electronics, 2022,69(10): 10704-10714. (SCI) (WOS:000790866600100)
Lu H, Lu C*, Yu Y, et al. Autonomous Overtaking for Intelligent Vehicles Considering Social Preference Based on Hierarchical Reinforcement Learning [J]. Automotive Innovation, 2022,5(2): 195-208. (SCI)
Li Z, Gong J, Lu C*, et al. Interactive Behaviour Prediction for Heterogeneous Traffic Participants In the Urban Road: A Graph Neural Network-based Multi-task Learning Framework [J]. IEEE/ASME Transactions on Mechatronics, 2021(领域顶级期刊SCI, Q1, IF: 5.867)
Lu C, Hu F, Cao D, et al. Transfer Learning for Driver Model Adaptation in Lane-Changing Scenarios Using Manifold Alignment [J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(8):3281-3293. (领域顶级期刊SCI, Q1, IF: 9.551)
Li Z, Gong J, Lu C*, et al. Importance Weighted Gaussian Process Regression for Transferable Driver Behaviour Learning in the Lane Change Scenario [J]. IEEE Transactions on Vehicular Technology, 2020，69(11): 12497-12509. (领域顶级期刊SCI, Q1, IF: 6.239)
Liu Q, Xu S, Lu C*, et al. Early Recognition of Driving Intention for Lane Change Based on Recurrent Hidden Semi-Markov Model [J]. IEEE Transactions on Vehicular Technology,2020,69(10): 10545-10557. (领域顶级期刊SCI, Q1, IF: 6.239)
Lu C, Hu F, Cao D, et al. Virtual-to-Real Knowledge Transfer for Driving Behaviour Recognition: Framework and a Case Study [J]. IEEE Transactions on Vehicular Technology, 2019, 68(7): 6391-6402. (领域顶级期刊SCI, Q1, IF: 6.239)
Lu C, Wang H, Lv C, et al. Learning Driver-Specific Behavior for Overtaking: A Combined Learning Framework [J]. IEEE Transactions on Vehicular Technology, 2018, 67(8): 6788-6802. (领域顶级期刊SCI, Q1, IF: 6.239)
Lv C, Xing Y, Lu C, et al. Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle [J]. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5718-5729. (领域顶级期刊SCI, Q1, IF: 6.239)
Chen Y, Lu C and Chu W. A Cooperative Driving Strategy Based on Velocity Prediction for Connected Vehicles with Robust Path-following Control [J]. IEEE Internet of Things Journal, 2020. (领域顶级期刊SCI, Q1, IF: 9.936).
Xing Y, Lv C, Cao D, C, Lu C. Energy-Oriented Driving Behavior Analysis and Personalized Prediction of Vehicle Energy Usage with Joint Time Series Modeling Corresponding [J]. Applied Energy, 2020, 261,114471. (领域顶级期刊SCI, Q1, IF: 8.848)
Yang S, Wang W, Lu C, et al. A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior [J]. IEEE Transactions on Human-Machine Systems, 2019, 49(6): 579-588. (SCI, Q2, IF: 4.124)
Yang L, Zhao C, Lu C, et al. Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network [J]. Sensors, 21(24): 8498. [J]. Sensors, 2019, 19, 3672. (SCI, Q2, IF: 3.847)
Lu C, Gong J, Lv C, et al. A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles [J]. Sensors, 2019, 19, 3672. (SCI, Q2, IF: 3.847)
Lu C, Huang J, Deng L, et al. Coordinated ramp metering with equity consideration using reinforcement learning [J]. Journal of Transportation Engineering, Part A: Systems, 2017, 143(7): 04017028. (SCI)
Lu C, Huang J. A self-learning system for local ramp metering with queue management [J]. Transportation Planning and Technology, 2017, 40(2): 182-198. (SCI)
Lu C, Huang J, Gong J. Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters[J]. Promet-Traffic&Transportation, 2016, 28(4): 371-381. (SCI)
Lu C, Zhao Y, Gong J. Intelligent ramp control for incident response using dyna-architecture [J]. Mathematical Problems in Engineering, 2015, 2015. (SCI)
Majid H, Lu C, Karim H. An integrated approach for dynamic traffic routing and ramp metering using sliding mode control [J]. Journal of Traffic and Transportation Engineering (English Edition), 2018, 5(2): 116-128.
Lu C, Chen H, Grant-Muller S. Indirect reinforcement learning for incident-responsive ramp control [J]. Procedia-Social and Behavioral Sciences, 2014, 111: 1112-1122.
Lu C, Chen H. Hierarchical planning for agent-based traffic management and control [J]. IFAC Proceedings Volumes, 2012, 45(24): 256-261.
崔格格, 吕超, 李景行, 熊光明*等.数据驱动的智能车个性化场景风险图构建[J]. 汽车工程, 2023 (已录用).
张哲雨, 吕超*, 李景行, 熊光明, 吴绍斌, 龚建伟. 基于车辆视角数据的行人轨迹预测与风险等级评定[J]. 汽车工程, 2022, 44(5): 675-683.
吕超,鲁洪良,于洋,王昊阳,吴绍斌.基于分层强化学习和社会偏好的自主超车决策系统[J].中国公路学报,2022,35(03):115-126.
吕超,崔格格,孟相浩,陆军琰,徐优志,龚建伟.基于图表示的智能车行人意图识别方法[J].北京理工大学学报,2022,42(07):688-695.
龚建伟,龚乘,林云龙,李子睿,吕超*.智能车辆规划与控制策略学习方法综述[J].北京理工大学学报,2022,42(07):665-674.
京公网安备 11010802027423号