Remote Attacks on Automated Vehicles Sensors : Experiments on Camera and LiDAR

Autonomous automated vehicles are the next evolution in transportation and will improve safety, traffic efficiency and driving experience. Automated vehicles are equipped with multiple sensors (LiDAR, radar, camera, etc.) enabling local awareness of their surroundings. A fully automated vehicle will unconditionally rely on its sensors readings to make short-term (i.e. safety-related) and long-term (i.e. planning) driving decisions. In this context, sensors have to be robust against intentional or unintentional attacks that aim at lowering sensor data quality to disrupt the automation system. This paper presents remote attacks on camera-based system and LiDAR using commodity hardware. Results from laboratory experiments show effective blinding, jamming, replay, relay, and spoofing attacks. We propose software and hardware countermeasures that improve sensors resilience against these attacks.

[1]  R. O. Carter,et al.  Potential use of near, mid and far infrared laser diodes in automotive LIDAR applications , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[2]  Jana Dittmann,et al.  Security threats to automotive CAN networks - Practical examples and selected short-term countermeasures , 2008, Reliab. Eng. Syst. Saf..

[3]  Wei Zhang,et al.  Tracking and Pairing Vehicle Headlight in Night Scenes , 2012, IEEE Transactions on Intelligent Transportation Systems.

[4]  Alberto Broggi,et al.  Extensive Tests of Autonomous Driving Technologies , 2013, IEEE Transactions on Intelligent Transportation Systems.

[5]  Daisuke Inoue,et al.  Demonstration of In-Car Doppler Laser Radar at 1.55 $ \mu\hbox{m}$ for Range and Speed Measurement , 2013, IEEE Transactions on Intelligent Transportation Systems.

[6]  Tomas Olovsson,et al.  Towards designing secure in-vehicle network architectures using community detection algorithms , 2014, 2014 IEEE Vehicular Networking Conference (VNC).

[7]  Dariu Gavrila,et al.  Ieee Transactions on Intelligent Transportation Systems the Benefits of Dense Stereo for Pedestrian Detection , 2022 .

[8]  Li Bai,et al.  A Sensor Fusion Framework Using Multiple Particle Filters for Video-Based Navigation , 2010, IEEE Transactions on Intelligent Transportation Systems.

[9]  George Vosselman,et al.  Recognizing basic structures from mobile laser scanning data for road inventory studies , 2011 .

[10]  Hui Kong,et al.  Generalizing Laplacian of Gaussian Filters for Vanishing-Point Detection , 2013, IEEE Transactions on Intelligent Transportation Systems.

[11]  Hovav Shacham,et al.  Comprehensive Experimental Analyses of Automotive Attack Surfaces , 2011, USENIX Security Symposium.

[12]  Takashi Naito,et al.  Multiband Image Segmentation and Object Recognition for Understanding Road Scenes , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  Sahil Singla,et al.  Seam Reconstruct: Dynamic scene stitching with Large exposure difference , 2009, 2009 Second International Conference on the Applications of Digital Information and Web Technologies.

[14]  Thomas B. Moeslund,et al.  Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey , 2012, IEEE Transactions on Intelligent Transportation Systems.

[15]  Yili Liu,et al.  Investigation of Driver Performance With Night Vision and Pedestrian Detection Systems—Part I: Empirical Study on Visual Clutter and Glance Behavior , 2010, IEEE Transactions on Intelligent Transportation Systems.

[16]  C. Stiller,et al.  3D perception and planning for self-driving and cooperative automobiles , 2012, International Multi-Conference on Systems, Sygnals & Devices.

[17]  Marcus Obst,et al.  Multi-sensor data fusion for checking plausibility of V2V communications by vision-based multiple-object tracking , 2014, 2014 IEEE Vehicular Networking Conference (VNC).

[18]  Matti Valovirta,et al.  Experimental Security Analysis of a Modern Automobile , 2011 .

[19]  Toby P. Breckon,et al.  Automatic Road Environment Classification , 2011, IEEE Transactions on Intelligent Transportation Systems.

[20]  Steven E. Shladover,et al.  Potential Cyberattacks on Automated Vehicles , 2015, IEEE Transactions on Intelligent Transportation Systems.

[21]  Christof Paar,et al.  Security in Automotive Bus Systems , 2004 .

[22]  Jason Yosinski,et al.  Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Concetto Spampinato,et al.  Adaptive Background Modeling Integrated With Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection , 2011, IEEE Transactions on Intelligent Transportation Systems.

[24]  Frank Kargl,et al.  Revisiting attacker model for smart vehicles , 2014, 2014 IEEE 6th International Symposium on Wireless Vehicular Communications (WiVeC 2014).

[25]  Wenyuan Xu,et al.  Security and Privacy Vulnerabilities of In-Car Wireless Networks: A Tire Pressure Monitoring System Case Study , 2010, USENIX Security Symposium.

[26]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[27]  William H. Steier,et al.  Photorefractivity in vanadium‐doped ZnTe , 1992 .