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Autonomous vehicles can be tricked into dangerous driving behavior

Researchers in UCI’s Division of Laptop Science arrange a course on the UCLA campus to check the reactions of driverless automobiles to extraordinary objects being positioned on the aspect of the street. Their examine discovered that containers, bicycles, trash cans and site visitors cones could cause a driverless car to halt abruptly, probably making a hazard and impacting supply of passengers and items. Credit: Ziwen Wan / UCI

When a driverless automobile is in movement, one defective determination by its collision-avoidance system can result in catastrophe, however researchers on the University of California, Irvine have recognized one other doable danger: Autonomous automobiles might be tricked into an abrupt halt or different undesired driving habits by the position of an extraordinary object on the aspect of the street.

“A box, bicycle or traffic cone may be all that is necessary to scare a driverless vehicle into coming to a dangerous stop in the middle of the street or on a freeway off-ramp, creating a hazard for other motorists and pedestrians,” stated Qi Alfred Chen, UCI professor of laptop science and co-author of a paper on the topic introduced just lately on the Community and Distributed System Safety Symposium in San Diego.

Chen added that automobiles cannot distinguish between objects current on the street by pure accident or these left deliberately as a part of a bodily denial-of-service assault. “Both can cause erratic driving behavior,” stated Chen.

Chen and his group targeted their investigation on safety vulnerabilities particular to the planning module, part of the software program code that controls autonomous driving programs. This element oversees the car’s decision-making processes governing when to cruise, change lanes or decelerate and cease, amongst different features.

“The vehicle’s planning module is designed with an abundance of caution, logically, because you don’t want driverless vehicles rolling around, out of control,” stated lead writer Ziwen Wan, UCI Ph.D. scholar in laptop science. “But our testing has found that the software can err on the side of being overly conservative, and this can lead to a car becoming a traffic obstruction, or worse.”

Credit: University of California, Irvine

For this challenge, the researchers at UCI’s Donald Bren College of Info and Laptop Sciences designed a testing instrument, dubbed PlanFuzz, which may routinely detect vulnerabilities in broadly used automated driving programs. As proven in video demonstrations, the group used PlanFuzz to guage three totally different behavioral planning implementations of the open-source, industry-grade autonomous driving programs Apollo and Autoware.

The researchers discovered that cardboard containers and bicycles positioned on the aspect of the street prompted automobiles to completely cease on empty thoroughfares and intersections. In one other take a look at, autonomously pushed automobiles, perceiving a nonexistent menace, uncared for to vary lanes as deliberate.

“Autonomous vehicles have been involved in fatal collisions, causing great financial and reputation damage for companies such as Uber and Tesla, so we can understand why manufacturers and service providers want to lean toward caution,” stated Chen. “But the overly conservative behaviors exhibited in many autonomous driving systems stand to impact the smooth flow of traffic and the movement of passengers and goods, which can also have a negative impact on businesses and road safety.”

Becoming a member of Chen and Wan on this challenge have been Junjie Shen, UCI Ph.D. scholar in laptop science; Jalen Chuang, UCI undergraduate scholar in laptop science; Xin Xia, UCLA postdoctoral scholar in civil and environmental engineering; Joshua Garcia, UCI assistant professor of informatics; and Jiaqi Ma, UCLA affiliate professor of civil and environmental engineering.

B-GAP: A simulation method for training autonomous vehicles to navigate complex urban scenes

Extra data:
Paper hyperlink: Ziwen Wan et al, Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks, (2022)

Autonomous automobiles might be tricked into harmful driving habits (2022, May 26)
retrieved 26 May 2022

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