A Discovery to Avoid the Ultimate On-road Disaster

The human arsenal, over the years, has seen some really valuable traits, but at the same time, it still hasn’t seen an element more significant than that desire of ours to improve at a consistent clip. We can claim what we did because the stated desire has already brought the world some huge milestones, with technology appearing as a major member of the group. The reason why we hold technology in such a high regard is, by and large, predicated upon its skill-set, which ushered us towards a reality that nobody could have ever imagined otherwise. Nevertheless, if we look beyond the surface for a second, it should become clear how the whole runner was also very much inspired from the way we applied those skills across a real world environment. The latter component, in fact, did a lot to give the creation a spectrum-wide presence, and as a result, initiate a full-blown tech revolution. Of course, this revolution then went on to scale up the human experience through some outright unique avenues, but even after achieving a feat so notable, technology will somehow continue to bring forth the right goods. The same has turned more and more evident in recent times, and assuming one new discovery ends up with the desired impact, it will only put that trend on a higher pedestal moving forward.

The researching teams at University of California and Northeastern University have successfully developed a novel algorithm, which is designed to improve the safety and security of autonomous cars. You see, autonomous cars are given the capability to detect obstacles and other potential hazards by sending out radio waves and recording their reflections as they bounce off surrounding objects. However, as ingenious as it sounds, this exposes the vehicle to potential cyberattacks like spoofing an activity that involves interfering with the vehicle’s return signal to trick it into registering an obstacle in its path. This could get the car to stop without any warning, thus increasing the chance of a serious accident. Fortunately, the algorithm in question addresses that very concern. Described as “mmSpoof: Resilient Spoofing of Automotive Millimeter-wave Radars using Reflect Array,” it basically allows researchers to create a sandbox environment where they can mimic a spoofing attack. This, in turn, should help them in learning about those security loopholes that need attention to avoid the risk of a real cyberattack.

“The invention of autonomous systems, like self-driving cars, was to enable the safety of humanity and prevent loss of life,” said Dinesh Bharadia, an affiliate of the UC San Diego Qualcomm Institute (QI) and faculty member in the university’s Jacobs School of Engineering, “Such autonomous systems use sensors and sensing to deliver autonomy. Therefore, safety and security rely on achieving high-fidelity sensing information from sensors. Our team exposed a radar sensor vulnerability and developed a solution that autonomous cars should strongly consider.

Make no mistake, other researchers have in past tried to solve the stated issue, but their attempts were largely neutralized by assumptions like either the attacker can synchronize with the victim’s radar signal to launch an assault or both cars are physically connected by a cable. Anyway, the study in question also took this opportunity to reveal a new hacking technique, which uses the victim vehicle’s radar against itself. Basically, the hacker would subtly change the received signal’s parameters at “lightspeed” before reflecting it back to disguise their sabotage and make it much harder for the vehicle to filter malicious behavior. The researching team, to counter this devastating method, has coined one suggestion of using high-resolution radar that can capture multiple reflections from a car to accurately identify the true reflection. There is also room to create backup options for radar with the help of cameras and LiDAR, a technology which can record the time it takes for a laser pulse to hit an object and return to measure its surroundings.

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