ROME – There is still a long way to go to achieve truly autonomous driving. In the current state of technology, human “supervision” is absolutely necessary, as indeed the same houses that adopt automated driving systems carefully recommend. And even the safety and driver assistance devices, which many vehicles are equipped with, can show limitations in certain situations. According to a study by the American Automobile Association (Aaa), the performance of advanced driver assistance technology, used to help vehicles brake automatically and stay in lane, can be compromised in moderate and heavy rain. AAA researchers found that various automatic vehicle emergency braking systems, during a simulated rain test, no longer recognized vehicles stopped in front and that lane keeping function deteriorated considerably. According to experts from the North American Federation of Automobile Clubs this could lead to dangerous situations if drivers rely too much on such systems, whose performance is generally evaluated under ideal conditions.
Adas are now becoming more and more common in new generation vehicles and automatic emergency braking is increasingly offered as a standard feature in new cars, proving to significantly reduce rear-end collisions in the various tests carried out. Indeed, in the AAA study no test car crashed into a stationary vehicle under ideal conditions, but with simulated rainfall, 17% of the tests occurred at speeds of 25mph (approximately 40km / h ), percentage climbed to 33% at speeds of 35 miles (56 km / h). In addition, during the simulated rain tests, the rest of the pavement was dry, and the researchers noted that wet roads could lead to even higher accident rates. As for lane keeping technology, in the AAA test vehicles crossed lane markers 37% of the time under ideal conditions, a rate nearly doubled (69%) once rain was added. Finally, it should be noted that a similar behavior was also detected by the Kfv (Austrian Road Safety Center) and by the UPI (Swiss Accident Prevention Office) according to which the field tests show great potential, but also the need for optimization outside the usual test scenarios, i.e. in case of rain, fog and poor visibility. (Maurilio Rigo)
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