Ghost in a car shell: Engineers make self-driving vehicles ‘hallucinate’ at will — MadRadar is worrying proof-of-concept that should get automotive companies on alert
Engineers at Duke University, Durham, North Carolina, have developed a system capable of manipulating automotive radar sensors, and making vehicles ‘hallucinate’ a range of scenarios. These include hiding the approach of an oncoming vehicle, making it look as if a real car has suddenly changed direction, or creating a non-existent phantom car.
Named MadRadar, this system performs tasks quickly and doesn’t require prior knowledge of the specific radar settings of the targeted vehicle.
“We can make a fake vehicle appear out of nowhere or make an actual vehicle disappear in real-world experiments,” Miroslav Pajic, the Dickinson Family Associate Professor of Electrical and Computer Engineering and lead of the team behind MadRadar said in a statement.
Real-world case studies
Radar is crucial for modern vehicles equipped with assistive and autonomous driving technologies to detect nearby moving vehicles. The variety of makes and models on the road means vehicles have slightly different operating parameters, traditionally making radar spoofing tricky.
However, MadRadar overcomes this hurdle by identifying a vehicle’s radar parameters within a quarter of a second and then initiating its own radar signals to deceive the targeted radar system.
Described as a ‘general black-box radar attack framework for automotive mmWave FMCW radars’, MadRadar can estimate the victim radar’s configuration in real-time and then execute an attack based on the estimates.
The research states, “We evaluate the impact of such attacks maliciously manipulating a victim radar’s point cloud, and show the novel ability to effectively `add’ (i.e., false positive attacks), `remove’ (i.e., false negative attacks), or `move’ (i.e., translation attacks) object detections from a victim vehicle’s scene. Finally, we experimentally demonstrate the feasibility of our attacks on real-world case studies performed using a real-time physical prototype on a software-defined radio platform.”
The researchers plan to detail their work at the Network and Distributed System Security Symposium 2024, in San Diego, California. The paper is currently available on the arXiv preprint server.
The researchers emphasize that the existence of MadRadar underscores the urgent need for manufacturers to enhance the security measures of their radar systems to protect against potential misuse.
“We’re not building these systems to hurt anyone, we’re demonstrating the existing problems with current radar systems to show that we need to fundamentally change how we design them,” Pajic concluded.
More from TechRadar Pro
Engineers at Duke University, Durham, North Carolina, have developed a system capable of manipulating automotive radar sensors, and making vehicles ‘hallucinate’ a range of scenarios. These include hiding the approach of an oncoming vehicle, making it look as if a real car has suddenly changed direction, or creating a non-existent…
Recent Posts
- How much data does your favorite messaging app collect? New study shows 90% of messaging apps now include AI that puts privacy at risk
- More than a decade later, the team behind N++ is back with a multiplayer sequel
- If Vampire Survivors and Spelunky had a baby, it’d be Messhof’s Blood Dungeon
- Grand Theft Auto VI is warping the video game release calendar
- 9 dog-care gadgets that are so clever they deserve a treat — including an ingenious on-the-go water solution and a ‘canine FitBit’
Archives
- June 2026
- May 2026
- April 2026
- March 2026
- February 2026
- January 2026
- December 2025
- November 2025
- October 2025
- September 2025
- August 2025
- July 2025
- June 2025
- May 2025
- April 2025
- March 2025
- February 2025
- January 2025
- December 2024
- November 2024
- October 2024
- September 2024
- August 2024
- July 2024
- June 2024
- May 2024
- April 2024
- March 2024
- February 2024
- January 2024
- December 2023
- November 2023
- October 2023
- September 2023
- August 2023
- July 2023
- June 2023