Autonomous cars or self-driving cars rely on a spinning type of radar system called LIDAR

Today, thousands of autonomous cars, also known as self-driving cars are on the roads. Besides, that number is anticipated to climb up, and autonomous vehicles (AVs) are expected to constitute 25% of cars worldwide in 2035. However, can AVs prevent traffic accidents? Results show that the use of AVs will stay as a concern, and it will be hard to either champion or oppose them. From a positive perspective, AVs will contribute to traffic safety, increase economic and social benefits, and contribute to environmental protection. However, from an opposing point of view, AVs would be hacked, expose our data to third parties, cause liability problems, increase carbon emissions into the atmosphere, risk our health, and constitute a financial burden on economies.

Many vehicles on the road today have driver assistance technologies, which help to save lives and prevent injuries on our nation's roads. While some driver assistance technologies are designed to warn you if you're at risk of an impending crash, others are designed to take action to avoid a crash.

The continuing evolution of automotive technology, including driver assistance technologies and automated driving systems, aims to deliver even greater safety benefits.

Driver-assist technologies have reached the tipping point and are poised to take control of most, if not all, aspects of the driving task. Proponents of automated driving (AD) are enthusiastic about its promise to transform mobility and realize impressive societal benefits. This paper attempts to carefully examine the potential of AD to realize safety benefits, challenge widely-held assumptions, and delve more deeply into the barriers that are hitherto largely overlooked. As automated vehicle (AV) technologies advance and emerge within a ubiquitous cyber-physical world they raise additional issues that still need to be adequately defined, let alone researched. Issues around automation, sociotechnical complexity, and systems resilience are well-known in the context of aviation and space. There are important lessons that could be drawn from these applications to help inform the development of automated driving.

A collaboration of researchers from the U.S. and Japan has demonstrated that a laser attack could be used to blind autonomous cars and delete pedestrians from their view, endangering those in its path, according to a press release.

Autonomous or self-driving cars rely on a spinning type of radar system called LIDAR that helps the vehicle sense its surroundings. Short for Light Detection and Ranging, the system emits laser lights and then captures its reflections to determine the distances between itself and the obstacles in its path.

How can a laser attack be thwarted?

This is the first report of a LIDAR system being spoofed in any way to prevent it from detecting obstacles. A fair degree of accuracy is needed to time the laser signal towards the LIDAR sensor to spoof; however, the data needed to synchronize this is available publicly from LIDAR manufacturers, one of the researchers associated with the study said in a press release.

The researchers carried out these tests to help build a more reliable system for sensors. Manufacturers of these systems could now make additions to their software to be able to detect instances of such an attack and switch to an alternate system of obstacle detection. Alternatively, the hardware could also be improved in the future to defend them from such attacks.

With the rise in autonomous car systems, an attack on a LIDAR system is a plausible way to confuse cars, especially when humans are no longer at the wheel. The result of such attacks could be catastrophic, and efforts must be made in the early stages to minimize or eliminate such vulnerabilities.

The research findings were published in the pre-print website arxiv.org and will also be presented at the 2023 USENIX Security Symposium.