Autonomous-Driving-Security-Resources

🚗 A repository for documenting and exploring the world of autonomous driving safety, featuring a curated collection of research groups, conferences, journals, competitions, papers, reports, and resource. Inspired by awesome-php.

Contributing

Please feel free to send me pull requests to add links.

Table of Contents

• Foundations
• Revelant Repositories
• Research Labs
• Relevant Conferences
• Relevant Journals
• Relevant Competitions
• Papers

Foundations

• Artificial Intelligence

  • Awesome Machine Learning - A curated list of awesome Machine Learning frameworks, libraries and software. Maintained by Joseph Misiti.Joseph Misiti
  • Deep Learning Papers Reading Roadmap - Deep Learning papers reading roadmap constructed from outline to detail, old to state-of-the-art, from generic to specific areas focus on state-of-the-art for anyone starting in Deep Learning. Maintained by, Flood Sung.
  • Open Source Deep Learning Curriculum - Deep Learning curriculum meant to be a starting point for everyone interested in seriously studying the field.

• Robotics

  • Awesome Robotics - A list of various books, courses and other resources for robotics, maintained by kiloreux.

• Computer Vision

  • Awesome Computer Vision - A curated list of awesome computer vision resources, maintained by Jia-Bin Huang
  • Awesome Deep Vision - A curated list of deep learning resources for computer vision, maintained by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim

Revelant Repositories

• Autonomous Driving Cookbook - Scenarios, tutorials and demos for Autonomous Driving.
• Slam in Autonomous Driving - SLAM Technology in Autonomous Driving corresponds to open source code.
• SensorsCalibration - A Multi-sensor Calibration Toolbox for Autonomous Driving.

Research Labs

University	Institution	Members
University of Delaware	Department of Computer and Information Sciences	Weisong Shi et al.
Purdue University	Center for Road Safety	Yiheng Feng et al.
University of Michigan	Department of Electrical Engineering and Computer Science	Yulong Cao et al.
University of California, Irvine	Department of Computer Science	Qi Alfred Chen et al.
Tsinghua University	School of Behicle and Mobility	Keqiang Li, Dian-ge Yang, Shengbo Li et al.
Tsinghua University	Institute for AI Industry esearch	Yaqin Zhang et al.
Tsinghua University	MARS Lab	Hang Zhao et al.
Peking University	School of Intelligence Science and Technology	Rongqing Zhao et al.
Beijing Institute of Technology	School of Mechanical Engineering	Huiyan Chen, Jianwei Gong et al.
Beihang University	School of Transportation Science and Engineering	Guizhen Yu, Shichun Yang et al.
Beijing Jiaotong University	Institute of Information Science	Yao Zhao, Chunyu Lin et al.
Xi'an Jiaotong University	College of Artificial Ingelligence	Nanning Zheng et al.
Zhejiang University	Research Center for Intelligent Drive and Future Traffic College of Control Science and Engineering	Yong Liu, Dongqin Feng et al.
Nanjing University of Science and Technology	Automation Research Institute	Minghu Ren, Jingyu Yang, Zhaoxia Shi et al.
Tongji University	Intelligent Vehicle and Cooperative control of Multi-agent Lab	Lu Xiong, Xichan Zhu, Hao Zhang et al.
Huazhong University of Science and Technology	School of Artificial Intelligence and Automation	Xinggang Wang, Dingxin He, et al.
Shanghai Jiaotong Univerisity	CyberC3 Intelligent Vehicle Labs	Chengliang Yin, Ming Yang, et al.

(back to top)

Relevant Conferences

• Security

  • ACM Conference on Computer and Communications Security (CCS) - CCF-A
  • Network and Distributed System Security Symposium (NDSS) - CCF-A
  • IEEE Symposium on Security and Privacy (S&P) - CCF-A
  • USENIX Security Symposium - CCF-A

• Artificial Intelligence & Computer Vision

  • Computer Vision and Pattern Recognition (CVPR) - CCF-A
  • IEEE International Conference on Computer Vision (ICCV) - CCF-A
  • European Conference on Computer Vision (ECCV) - CCF-B

• Software Engineering

  • International Conference on Automated Software Engineering (ASE) - CCF-A
  • International Conference on Software Engineering (ICSE) - CCF-A
  • International Symposium on Software Testing and Analysis (ISSTA) - CCF-A

• Multimedia

  • ACM International Conference on Multimedia (ACM MM) - CCF-A

(back to top)

Relevant Journals

• IEEE Transactions on Information Forensics and Security (TIFS) - CCF-B / JCR Q1
• IEEE Transactions on Intelligent Transportation Systems (TITS) - CCF-B / JCR Q1
• IEEE Transactions on Intelligent Vehicles (TIV) - JCR Q1
• IEEE Transactions on Vehicular Technology (TVT) - JCR Q2
• IEEE Transactions on Reliability (TR) - CCF-C / JCR Q2

(back to top)

Relevant Competitions

• World Intelligent Driving Challenge, WIDC
• China International Autopilot Challenge, CIAC
• Intelligent Vehicle Future Challenge, IVFC
• Kaggle
• Tianchi

(back to top)

Papers

• Survey
• Scenario Generation
• Datasets
• Perception

Survey

• Visually Adversarial Attacks and Defenses in the Physical World: A Survey. [pdf]

  • Xingxing Wei,Bangzheng Pu, Jiefan Lu, Baoyuan Wu. arXiv, 2023.

• Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey. [pdf]

  • Pranav Singh Chib, Pravendra Singh. arXiv, 2023.

• Deep learning for safe autonomous driving: Current challenges and future directions. [pdf]

  • Khan Muhammad, Amin Ullah, Jaime Lloret, Javier Del Ser, Victor Hugo C. de Albuquerque. IEEE Transactions on Intelligent Transportation Systems, 2021.

• Deep learning-based autonomous driving systems: A survey of attacks and defenses. [pdf]

  • Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han. IEEE Transactions on Industrial Informatics, 2021.

• Autonomous driving security: State of the art and challenges. [pdf]

  • Cong Gao, Geng Wang, Weisong Shi, Zhongmin Wang, Yanping Chen. IEEE Internet of Things Journal, 2021.

Camera Attack and Defense

• Attacking vision-based perception in end-to-end autonomous driving models. [pdf] [code]
  • Adith Boloor, Karthik Garimella, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang. Journal of Systems Architecture, 2020.

Lane Detection Attack and Defense

• Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection. [pdf]

  • Junjie Shen, Yunpeng Luo, Ziwen Wan, Qi Alfred Chen. arXiv, 2023.

• Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving. [pdf] [note]

  • Xingshuo Han, Guowen Xu, Yuan Zhou*, Xuehuan Yang, Jiawei Li, Tianwei Zhang. ACM International Conference on Multimedia, 2022.

• Too Good to Be Safe: Tricking Lane Detection in Autonomous Driving with Crafted Perturbations. [pdf]

  • Pengfei Jing, Qiyi Tang, Yuefeng Du, Lei Xue, Xiapu Luo, Ting Wang, Sen Nie, Shi Wu. Usenix Security, 2021.

• Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack. [pdf]

  • Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jia, Xue Lin, Qi Alfred Chen. Usenix Security, 2021.

LiDAR Attack and Defense

• You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks. [pdf]
  • Yulong Cao, S. Hrushikesh Bhupathiraju, Pirouz Naghavi, Takeshi Sugawara, Z. Morley Mao*, Sara Rampazzi. Usenix Security, 2023.
• Who Is in Control? Practical Physical Layer Attack and Defense for mmWave-Based Sensing in Autonomous Vehicles. [pdf]
  • Zhi Sun, Sarankumar Balakrishnan, Lu Su, Arupjyoti Bhuyan, Pu Wang, Chunming Qiao. IEEE Transactions on Information Forensics and Security (TIFS) 2021.

Multi-Sensor Fusion Attack and Defense

• Security Analysis of {Camera-LiDAR} Fusion Against {Black-Box} Attacks on Autonomous Vehicles. [pdf]

  • R. Spencer Hallyburton, Yupei Liu, Yulong Cao, Z. Morley Mao, Miroslav Pajic. Usenix Security, 2022.

• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks. [pdf]

  • Yulong Cao, Ningfei Wang, Chaowei Xiao, Dawei Yang, Jin Fang, Ruigang Yang, Qi Alfred Chen, Mingyan Liu, Bo Li. S&P, 2021.

Trajectory Prediction Attack and Defense

• Vehicle Trajectory Prediction Works, but Not Everywhere. [pdf]
  • Mohammadhossein Bahari, Saeed Saadatnejad, Ahmad Rahimi, Mohammad Shaverdikondori, Amir Hossein Shahidzadeh, Seyed-Mohsen Moosavi-Dezfooli, Alexandre Alahi. CVPR 2022.

System Testing

• Mind the gap! a study on the transferability of virtual vs physical-world testing of autonomous driving systems. [pdf]
  • Andrea Stocco, Brian Pulfer, Paolo Tonella. IEEE Transactions on Software Engineering (TSE), 2022.
• DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing.
• Testing the safety of self-driving vehicles by simulating perception and prediction. [pdf]
  • Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat & Raquel Urtasun. ECCV, 2020.
• AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems. [pdf]
  • International Symposium on Software Reliability Engineering (ISSRE), 2020.
• Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios. [pdf]
  • Baiming Chen, Xiang Chen, Qiong Wu, Liang Li. IEEE Transactions on Intelligent Transportation Systems (TITS), 2021.
• DeepRoad: GAN-based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems. [pdf]
  • MengshiZhang, Yuqun Zhang, Lingming Zhang, Cong Liu, Sarfraz Khurshid. International Conference on Automated Software Engineering (ASE), 2018.
• DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars. [pdf]
  • Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray. International Conference on Software Engineering (ICSE), 2018.
• Systematic Testing of Convolutional Neural Networks for Autonomous Driving. [pdf]
  • Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia. arXiv, 2017.

Scenario Generation

• A survey on safety-critical driving scenario generation—A methodological perspective. [pdf]

  • Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao. IEEE Transactions on Intelligent Transportation Systems (TITS), 2023.

• Online Adaptive Generation of Critical Boundary Scenarios for Evaluation of Autonomous Vehicles. [pdf]

  • Junjie Zhou, Lin Wang, Xiaofan Wang. IEEE Transactions on Intelligent Transportation Systems (TITS), 2023.

• SceGene: Bio-Inspired Traffic Scenario Generation for Autonomous Driving Testing. [pdf]

  • Ao Li, Shitao Chen, Liting Sun, Nanning Zheng, Masayoshi Tomizuka, Wei Zhan. IEEE Transactions on Intelligent Transportation Systems (TITS), 2022.

• Test Scenario Generation and Optimization Technology for Intelligent Driving Systems. [pdf]

  • Jianli Duan, Feng Gao, Yingdong He. IEEE Intelligent Transportation Systems Magazine, 2020.

• Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method. [pdf]

  • Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.

Anomaly Detection

• Detecting the Anomalies in LiDAR Pointcloud. [pdf]
  • Chiyu Zhang, Ji Han, Yao Zou, Kexin Dong, Yujia Li, Junchun Ding, Xiaoling Han. arXiv, 2023.

End to End

• Adversarial Driving: Attacking End-to-End Autonomous Driving. [pdf]
  • Han Wu, Syed Yunas, Sareh Rowlands, Wenjie Ruan, Johan Wahlström. IEEE Intelligent Vehicles Symposium, 2023.

Other

• Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems. [pdf]
  • Cumhur Erkan Tuncali, James Kapinski, Hisahiro Ito, Jyotirmoy V. Deshmukh. Annual Design Automation Conference, 2018.

Datasets

• AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving. [pdf]

  • Ahmed Rida Sekkat, Rohit Mohan, Oliver Sawade, Elmar Matthes, Abhinav Valada. arXiv, 2023.

• ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving. [pdf]

  • Zizhang Wu, Xinyuan Chen, Hongyang Wei, Fan Song, Tianhao Xua. arXiv, 2023.

• SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving. [pdf]

  • Jiawei Hou, Qi Chen, Yurong Cheng, Guang Chen, Xiangyang Xue, Taiping Zeng, Jian Pu*. arXiv, 2023.

• A Survey on Datasets for Decision-making of Autonomous Vehicle. [pdf]

  • Yuning Wang, Zeyu Han, Yining Xing, Shaobing Xu*, Jianqiang Wang*. arXiv, 2023.

• CityPersons: A Diverse Dataset for Pedestrian Detection. [pdf]

  • Shanshan Zhang, Rodrigo Benenson, Bernt Schiele. CVPR, 2017.

• Are we ready for autonomous driving? The KITTI vision benchmark suite. [pdf]

  • Andreas Geiger, Philip Lenz, Raquel Urtasun. CVPR, 2012.

Acknowledgement

• Organizers: (Aaron Wu)
• This project is inspired by awesome-php and awesome-computer-vision.