Source URL: https://www.wired.com/story/emergency-vehicle-lights-can-screw-up-a-cars-automated-driving-system/
Source: Wired
Title: Emergency Vehicle Lights Can Screw Up a Car’s Automated Driving System
Feedly Summary: Newly published research finds that the flashing lights on police cruisers and ambulances can cause “digital epileptic seizures” in image-based automated driving systems, potentially risking wrecks.
AI Summary and Description: Yes
Summary: The provided text discusses new research on the vulnerabilities of automated driving systems to certain light signals, particularly focusing on Tesla’s Autopilot. Key findings reveal potential system deficiencies that could affect driver safety and performance, prompting significant concerns for cybersecurity in the context of advanced driver assistance technologies.
Detailed Description: The text highlights critical insights concerning the intersection of cybersecurity, machine learning, and automotive safety. It specifically addresses how flaws in object detection can impact the functioning of advanced driving assistance systems (ADAS), particularly in recognizing emergency vehicles.
– The research, conducted without testing on specific ADAS like Tesla’s Autopilot, used off-the-shelf automated driving systems to simulate potential vulnerabilities.
– Object detection technology was tested through open-source algorithms, although its direct application in commercial systems remains uncertain.
– The motivation for the study stemmed from various collisions involving Teslas and stationary emergency vehicles, emphasizing the safety implications of flashing lights on these systems.
– The National Highway Traffic Safety Administration’s investigation and subsequent recall of Tesla’s Autopilot software underline governmental concerns about attention management and control by drivers using such systems.
– The text notes that other manufacturers’ ADAS, such as GM’s Super Cruise and Ford’s BlueCruise, incorporate mechanisms that mandate driver attention, potentially mitigating risks observed with Tesla’s system.
In summary, this research signals a rising need for rigorous cybersecurity measures and safety checks within AI-driven technologies in automotive applications, highlighting vulnerabilities that could compromise user safety and regulatory compliance. The findings urge professionals in AI, cloud, and infrastructure security to assess and fortify the resilience of machine learning algorithms utilized in autonomous driving systems.