职 称

副教授

□博导 ■硕导

学院专业

机械与车辆学院 车辆工程

办公地址

车辆实验楼 103室

邮 编

100081

办公电话

010-68915012

邮 件

chaolu@bit.edu.cn

教育及工作经历

2016.01至今，必赢线路检测3003，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，必赢线路检测3003，工学学士学位

主要研究方向

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。

代表性论文及研究项目

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].必赢线路检测3003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].必赢线路检测3003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

成果及荣誉

北京市科技进步二等奖，指导学生获必赢线路检测30032020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

社会职务

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-）