Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection

Localization in high-level Autonomous Driving (AD) systems is highly security critical. Recently, researchers found that state-of-the-art Multi-Sensor Fusion (MSF) based localization is vulnerable to GPS spoofing, which can cause road hazards such as driving off road or onto the wrong way. In this work, we perform the first exploration of using Lane Detection (LD) to detect and correct deviations caused by such attacks and design a novel LD-based system-level defense, LD3. We evaluate LD3 on real-world sensor traces and find that it can achieve effective and timely detection against the state-of-the-art attack with 100% true positive rates and 0% false positive rates. Results show that LD3 can be highly effective at steering the AD vehicle to safely stop within the current traffic lane. We implement LD3 on 2 open-source AD systems and validate its end-to-end defense capability using an industry-grade AD simulator and also in the physical world with a real vehicle-sized AD R&D vehicle.