研究方向

自动驾驶环境认知，系统安全性  

个人简介与工作经历

尹慧琳，中德TUV南德教席教授，博导。

工作经历：

(1) 2020-01 至今, 同济大学电子与信息工程学院控制科学与工程系

(2) 2016-01 至今, 同济大学中德学院电子与信息系TUV南德基金教席主任

(3) 2006-07 至 2019-12, 同济大学中德学院电子与信息系

教育经历：

(1) 2003-03 至 2006-07, 同济大学, 博士, 专业：控制理论与控制工程

(2) 2005-10 至 2006-04, 德国慕尼黑工业大学, 访问学者, 专业：信息学

(3) 1999-07 至 2002-09, 同济大学-慕尼黑工业大学, 双学位硕士, 专业：控制理论与控制工程

科研与教学

科研项目：

- 国家自然科学基金项目: 面向自动驾驶环境认知的态势评估功能模型及实现方法

- 科技部国家重点研发计划新能源汽车专项课题：自动驾驶电动汽车评价理论研究- AEV预期功能安全风险评估理论研究

- 科技部国家重点研发计划新能源汽车专项课题：智能电动汽车全状态参数估计、复杂环境感知与多源信息融合

- 中央高校科研专项资金重大国际合作预研项目：智能卡系统的攻击及防御关键技术研究

中德研究生课程：

- 先进控制理论

- 现代测量技术（和可靠性）

已发表论文：

[1] A multimodality fusion deep neural network and safety test strategy for intelligent vehicles. J Nie, J Yan, H Yin, L Ren, Q Meng, IEEE Transactions on Intelligent Vehicles 6 (2), 310-322, 2020.

[2] Trajectory prediction for intelligent vehicles using spatial‐attention mechanism. J Yan, Z Peng, H Yin, J Wang, X Wang, Y Shen, W Stechele, D Cremers, IET Intelligent Transport Systems 14 (13), 1855-1863, 2020.

[3] Patch-based attack on traffic sign recognition. B Ye, H Yin, J Yan, W Ge, IEEE International Intelligent Transportation Systems Conference (ITSC), 2021.

[4] Memory-attention hierarchical model for driving-behavior recognition and motion prediction. H Yin, J Wang, J Lin, D Han, C Ying, Q Meng, International journal of automotive technology 22, 895-908, 2021.

[5] Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic. W Zhou, D Chen, J Yan, Z Li, H Yin, W Ge, Autonomous Intelligent Systems 2 (1), 2022.

[6] Improved 3d object detector under snowfall weather condition based on lidar point cloud. J Lin, H Yin, J Yan, W Ge, H Zhang, G Rigoll, IEEE Sensors Journal 22 (16), 16276-16292, 2022.

[7] Causal information bottleneck boosts adversarial robustness of deep neural network. H Hua, J Yan, X Fang, W Huang, H Yin, W Ge, arXiv preprint arXiv:2210.14229, 2022.

[8] On adversarial robustness of semantic segmentation models for automated driving. H Yin, R Wang, B Liu, J Yan, IEEE Intelligent Vehicles Symposium (IV), 867-873, 2022.

[9] Curriculum Defense: An Effective Adversarial Training Method. H Yin, X Deng, J Yan, Chinese Control Conference (CCC), 7399-7406, 2022.

[10] Review on Uncertainty Estimation in Deep-Learning-Based Environment Perception of Intelligent Vehicles. H Yin, Z Chen, J Yan, G Rigoll, SAE Technical Paper, 2022.

[11] Exploring aesthetic procedural noise for crafting model-agnostic universal adversarial perturbations. J Yan, H Yin, W Ge, L Liu, Displays 79, 2023.

[12] Attack Detection for Intelligent Vehicles via CAN-Bus: A Lightweight Image Network Approach. S Gao, L Zhang, L He, X Deng, H Yin, H Zhang, IEEE Transactions on Vehicular Technology, 2023.

[13] FSFNet: Foreground score-aware fusion for 3D object detector under unfavorable conditions. J Lin, H Yin, J Yan, K Jian, Y Lu, W Ge, H Zhang, G Rigoll, IEEE Sensors Journal 23 (14), 15988-16001, 2023.

[14] DSENet: a deep sub-ensembles convolutional neural network for robust semantic segmentation. H Yin, X Xu, Q Meng, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL), 2023.

[15] An Adversarial Attack on Salient Regions of Traffic Sign. J Yan, H Yin, B Ye, W Ge, H Zhang, G Rigoll, Automotive Innovation, 1-14, 2023.

[16] A Survey of Vehicle Trajectory Prediction Based on Deep-Learning. H Yin, Y Wen, J Li, International Conference on Neural Networks, Information and Communication Engineering (NNICE), 2023.

[17] Ground-optimized SLAM with Hierarchical Loop Closure Detection in Large-scale Environment. H Yin, M Sun, L Zhang, J Yan, J Betz, IEEE International Conference on Intelligent Transportation Systems (ITSC), 2023.

[18] Multi-Object Tracking with Object Candidate Fusion for Camera and LiDAR data. H Yin, Y Lu, J Lin, M Schratter, D Watzenig, IEEE International Conference on Intelligent Transportation Systems (ITSC), 2023.

[19] AGV Path Planning Using Curiosity-driven Deep Reinforcement Learning. H Yin, Y Lin, J Yan, Q Meng, K Festl, L Schichler, D Watzenig, IEEE CASE, 2023.

[20] Wavelet regularization benefits adversarial training. J Yan, H Yin, Z Zhao, W Ge, H Zhang, G Rigoll, Information Sciences, 1-20, 2023.

欢迎同学报考中德硕士/博士研究生！