姓 名 |
吕超 |
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职 称 |
副教授 |
□博导 ■硕导 |
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学院专业 |
机械与车辆学院 车辆工程 |
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办公地址 |
车辆实验楼 103室 |
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邮 编 |
100081 |
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办公电话 |
010-68915012 |
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邮 件 |
chaolu@bit.edu.cn |
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教育及工作经历 |
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2016.01至今,平博pinnacle体育平台,车辆工程系,讲师,副教授 2017.06-2017.12,英国克兰菲尔德大学,访问学者 2011-2013,英国利兹大学,教学助理 2010.10-2015.01,英国利兹大学,工学博士学位 2005.09-2009.06,平博国际体育官网,工学学士学位 |
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主要研究方向 |
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智能车辆机器学习技术(强化学习、迁移学习、深度学习); 智能车辆场景理解与驾驶行为建模; 智能车辆类人决策系统与智能交通系统。 |
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代表性论文及研究项目 |
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代表论文: [1] Z. Zhang, C. Lu*, G. Cui, X. Meng, C. Gong and J. Gong. Prediction of Pedestrian Spatial-Temporal Risk Levels for Intelligent Vehicles: A Data-driven Approach[J]. IEEE Transactions on Vehicular Technology, 2024. (领域顶级期刊SCI, Q1, IF: 6.8) [2] H. Lu, Y. Liu, M. Zhu, C. Lu*, H. Yang and Y. Wang, Enhancing Interpretability of Autonomous Driving Via Human-Like Cognitive Maps: A Case Study on Lane Change[J]. IEEE Transactions on Intelligent Vehicles, 2024. (领域顶级期刊SCI, Q1, IF: 8.2) [3] Lu C, Lu H, Chen D, et al. Human-like decision making for lane change based on the cognitive map and hierarchical reinforcement learning[J]. Transportation research part C: emerging technologies, 2023, 156: 104328. (领域顶级期刊SCI, Q1, IF: 8.3) [4] Gong H, Li Z, Lu C*, et al. Leveraging Multi-Stream Information Fusion for Trajectory Prediction in Low-Illumination Scenarios: A Multi-Channel Graph Convolutional Approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. (领域顶级期刊SCI, Q1, IF: 8.5) [5] Lin Y, Li Z, Gong C, Lu C*, et al. Continual Interactive Behavior Learning With Traffic Divergence Measurement: A Dynamic Gradient Scenario Memory Approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. (领域顶级期刊SCI, Q1, IF: 8.5). [6] Liu Q, Liu H, Lu C*, et al. Human-Like Wall-Climbing Planning for Heavy Unmanned Ground Vehicles Using Driver Model and Dynamic Motion Primitives[J]. IEEE/ASME Transactions on Mechatronics, 2023. (领域顶级期刊SCI, Q1, IF: 6.4). [7] Liu Q X, Yao H, Lu C*, et al. Object-Level Attention Prediction for Drivers in the Information-Rich Traffic Environment[J]. IEEE Transactions on Industrial Electronics, 2023. (领域顶级期刊SCI, Q1, IF: 7.7). [8] 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. (领域顶级期刊SCI, Q1, IF: 8.5) [9] Li Z, Gong C, Lin Y, …, Lu C*, et al. Continual Driver Behaviour Learning for Connected Vehicles and Intelligent Transportation Systems: Framework, Survey and Challenges[J]. Green Energy and Intelligent Transportation, 2023: 100103. [10] 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) [11] 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) [12] 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) [13] 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) [14] 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) (领域顶级期刊SCI, Q1, IF: 7.7). [15] 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, IF: 6.1) [16] Yang L, Lu C, Xiong G, et al. A hybrid motion planning framework for autonomous driving in mixed traffic flow[J]. Green Energy and Intelligent Transportation, 2022, 1(3): 100022. [17] 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) [18] 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) [19] 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) [20] 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) [21] 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) [22] 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) [23] 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) [24] 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). [25] 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) [26] 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) [27] 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) [28] 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) [29] 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) [30] 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) [31] 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) [32] Lu C, Zhao Y, Gong J. Intelligent ramp control for incident response using dyna-architecture [J]. Mathematical Problems in Engineering, 2015, 2015. (SCI) [33] 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. [34] 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. [35] Lu C, Chen H. Hierarchical planning for agent-based traffic management and control [J]. IFAC Proceedings Volumes, 2012, 45(24): 256-261. [36] 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. [37] 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. [38] 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. [39] 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].平博国际体育官网学报,2022,42(07):688-695. [40] 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].平博国际体育官网学报,2022,42(07):665-674. 代表项目: [41] 科技创新2030—“新一代人工智能”重大项目,基于路端强化的自动驾驶决策关键技术,子课题负责人 [42] 国家自然科学基金面上项目,复杂交互环境下智能车辆类脑风险认知与可持续学习方法研究,主持 [43] 国家自然科学基金青年项目,智能车辆类人驾驶行为知识迁移原理与在线学习建模方法研究,主持 [44] 上汽基金会产学研重点项目,人类驾驶员城区环境下道路交叉口行驶的决策规划模型研究与应用,主持 [45] 国家自然科学基金联合基金项目,地面移动平台脑机混合操控基础理论与关键技术,参加 [46] 国家自然科学基金面上项目,融合驾驶员操纵特性和脑电信息的车速预测方法,参加
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成果及荣誉 |
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北京市科技进步二等奖,指导学生获平博国际体育官网2020年度优秀硕士论文奖,第二十四届与二十五届中国机器人及人工智能大赛全国一等奖、北京市一等奖,2022世界人工智能大会AI驾驶仿真挑战赛一等奖,2023中国国际智能网联汽车大赛技术优胜奖 |
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社会职务 |
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CAA平行智能专业委员会委员(2018-) 世界交通运输大会(WTC)技术委员会委员(2018-) |