Sensor Data Integrity Verification for Autonomous Vehicles Using Spread 3D Dither QIM

Abstract

An autonomous vehicle is a modern-day cyber-physical system with multiple sensors connected over its internal network. Many crucial features of the autonomy depend on the data from these sensors that get transmitted over the vehicle network to reach the data processing central modules. Protecting and verifying the sensor data integrity as it traverses the vehicle network is essential for the proper functioning of the autonomous vehicle. We propose a novel approach called spread dither 3D quantization index modulation (QIM) to verify the integrity of LiDAR sensor data used in autonomous vehicle applications. We propose a framework to verify the sensor data integrity in time-critical autonomous vehicle applications based on a data hiding technique called dither modulation. The sensor data is embedded with a watermark using the proposed spread 3D dither modulation at the sensor domain. The embedded sensor data can be directly used by the data processing algorithms due to very low embedded induced distortion. The sensor data integrity can be verified by a parallel process that can decode the embedded sensor data to detect and localize the tampering. Our proposed countermeasure framework is verified with real-life LiDAR data frames against the simulated transmission layer insider attacks on sensor data such as object insertion and deletion. The dither QIM method’s optimum parameter values are also deduced through multiple experiments.