Effects of perturbed depth sensors in autonomous ground vehicles

Cybersecurity of autonomous vehicles is a pertinent concern both for defense and also civilian systems. From self-driving cars to autonomous Navy vessels, malfunctions can have devastating consequences, including losses of life and infrastructure. Autonomous ground vehicles use a variety of sensors to image their environment: passive sensors such as RGB(-D) and thermal cameras, and active sensors such as LIDAR, radar, and sonar. These sensors are either used alone or fused to accomplish the basic mobile autonomy tasks: obstacle avoidance, localization, mapping, and, subsequently, path-planning. In this paper, we will provide a qualitative and quantitative analysis of the effect of perturbed sensing capability of depth sensing, focusing on LIDAR, and the subsequent effects on navigation and path planning in the presence of obstacles. Aspects that will be investigated include complexity of the perturbation and effect on the autonomous operations. This work will lay a foundation for developing robust autonomy algorithms that are secure against possible degraded or inoperable sensors.

[1]  Emil C. Lupu,et al.  GhostBuster: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing , 2020, ArXiv.

[2]  Jonathan Petit,et al.  Remote Attacks on Automated Vehicles Sensors : Experiments on Camera and LiDAR , 2015 .

[3]  Hafiz Malik,et al.  LiDAR Data Integrity Verification for Autonomous Vehicle , 2019, IEEE Access.

[4]  Jun-Da Chen,et al.  3D pulsed chaos lidar system. , 2018, Optics express.

[5]  Kaiqi Xiong,et al.  A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles , 2020, Comput. Secur..

[6]  Li Chen,et al.  Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression , 2017, ArXiv.

[7]  Ashish Kapoor,et al.  AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles , 2017, FSR.

[8]  B. Prabhakaran,et al.  A study on lidar data forensics , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[9]  Qihe Liu,et al.  Review of Artificial Intelligence Adversarial Attack and Defense Technologies , 2019, Applied Sciences.

[10]  Yongdae Kim,et al.  Illusion and Dazzle: Adversarial Optical Channel Exploits Against Lidars for Automotive Applications , 2017, CHES.

[11]  Hafiz Malik,et al.  LiDAR Data Integrity Verification for Autonomous Vehicle Using 3D Data Hiding , 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

[12]  Qi Alfred Chen,et al.  Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures , 2020, USENIX Security Symposium.

[13]  Chen Yan.  Can You Trust Autonomous Vehicles : Contactless Attacks against Sensors of Self-driving Vehicle , 2016 .

[14]  Kenneth T. Co,et al.  Object Removal Attacks on LiDAR-based 3D Object Detectors , 2021, Proceedings Third International Workshop on Automotive and Autonomous Vehicle Security.

[15]  Kevin Fu,et al.  Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving , 2019, CCS.

[16]  Vrizlynn L. L. Thing,et al.  Autonomous Vehicle Security: A Taxonomy of Attacks and Defences , 2016, 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).