Skip to main content

Hackers can fool self-driving car sensors into evasive action

The laser ranging (LIDAR) systems that most self-driving cars rely on to sense obstacles can be hacked by a setup costing just US$60, a security researcher has told IEEE spectrum. According to Jonathan Petit, principal scientist at software security company Security Innovation, he can take echoes of a fake car, pedestrian or wall and put them in any location. Using such a system, which he designed using a low-power laser and pulse generator, attackers could trick a self-driving car into thinking somethin
September 8, 2015 Read time: 2 mins
The laser ranging (LIDAR) systems that most self-driving cars rely on to sense obstacles can be hacked by a setup costing just US$60, a security researcher has told 6781 IEEE spectrum.

According to Jonathan Petit, principal scientist at software security company Security Innovation, he can take echoes of a fake car, pedestrian or wall and put them in any location. Using such a system, which he designed using a low-power laser and pulse generator, attackers could trick a self-driving car into thinking something is directly ahead of it, forcing it to slow down.

In a paper written while he was a research fellow in the University of Cork’s Computer Security Group and due to be presented at the Black Hat Europe security conference in November, Petit describes the system he built with off the shelf components that can create the illusion of an obstacle anywhere from 20 to 350 metres from the LIDAR unit and make multiple copies of the simulated obstacles, and even make them move.

While the short-range radars used by many self-driving cars for navigation operate in a frequency band requiring licencing, LIDAR systems use easily-mimicked pulses of laser light to build up a 3-D picture of the car’s surroundings and were ripe for attack.

“I can spoof thousands of objects and basically carry out a denial of service attack on the tracking system so it’s not able to track real objects,” Petit told IEEE spectrum. I don’t think any of the LIDAR manufacturers have thought about this or tried this.”

For more information on companies in this article

Related Content

  • Polarised imaging gives enforcement clarity
    February 6, 2020
    Polarised imaging advances have finally allowed ITS technology to catch up with previously unenforceable international bans on smoking in cars, says Sony’s Stephane Clauss
  • Traffic lights: There’s a better way ..
    July 9, 2014
    .. say researchers at Massachusetts Institute of Technology (MIT) who have developed a means of computing optimal timings for city stoplights that they say can significantly reduce drivers’ average travel times. Existing software for timing traffic signals has several limitations, says Carolina Osorio, an assistant professor of civil and environmental engineering at MIT and lead author of a forthcoming paper in the journal Transportation Science that describes the new system, based on a study of traffic
  • Self-driving car start-up raises major investment
    August 24, 2016
    Quanergy Systems, which makes solid state LiDAR sensors and smart sensing solutions used in self-driving cars, has raised US$90 million from investors including Sensata Technologies, Delphi Automotive, Samsung Ventures, Motus Ventures and GP Capital. Since launching in 2012, Quanergy has developed a compact, low-cost, automotive grade solid state LiDAR sensor, the S3 solid state LiDAR and is aggressively working to commercialise these sensors for advanced driver assistance systems (ADAS) and autonomous d
  • Bolt partners with Tartu University on self-driving tech
    September 5, 2019
    Ride-sharing company Bolt has joined forces with the University of Tartu (UT) in Estonia to develop technology for SAE Level 4 autonomous vehicles (AV). The partners intend to carry out AV pilots in urban areas and integrate AVs onto Bolt’s on-demand transportation platform by 2026. Jevgeni Kabanov, chief product officer at Bolt - formerly Taxify - says: “Rather than developing our own vehicle, the goal of this project is to build our self-driving technology with a focus on software and maps, on top of ex