Self-Driving and Connected Cars: Fooling Sensors and Tracking Drivers
summary
Remote attacks use cheap optics and hobby electronics to blind cameras and spoof LiDAR: a Mobileye-style camera is sensitive to 650 nm lasers and 940 nm LED matrices (one LED costs ~$0.06), flashes cause 2s average (up to 6s) recovery windows at ~1 m lab range, and an Ibeo LUX3 LiDAR (200 m range, 4 cm resolution) is replayed/relayed to create fake echoes at ~150–170 m and generate hundreds of tracked phantom objects. A 16‑day campus test (1.7M beacons) shows two passive sniffers capture 68k packets (3.17%) yet achieve 72% zone-level tracking; selecting eight intersections reaches ~90% road-level tracking; each sniffer is about €500.
AI-written summary · about
chapters (10)
- 0:00Automated vehicle threats
- 7:49Vehicle sensors and automation
- 9:57Remote sensor attacks
- 12:45Camera blinding attacks
- 22:33LiDAR spoofing attacks
- 36:22Privacy in connected vehicles
- 36:46Driver tracking via V2X
- 43:47Sniffing vehicle movements
- 57:31Countermeasures for tracking
- 1:05:50Q&A
related talks
40mSecuring Tesla and Mobileye from Split-Second Phantom Attacks
14mSeeing is Deceiving: Systematic Vulnerability Analysis of LiDAR-Based Autonomous...
43mConnected Cars - What could possibly go wrong?
16mPerspective-Shift Attacks Against Optical Perception Sensors: A Novel Attack...
25mAll Your GPS Are Belong To Us: Towards Stealthy Manipulation of Road Navigation Systems
26mLegal GNSS Spoofing and its Effects on Autonomous Vehicles
06mWhen Collaboration Betrays: The Evolving Attack Surface of...
09mWIP: Hijacking Attacks on UAV Follow-Me Systems in Realistic Scenarios