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Driverless Vehicles Can Be Tricked into Disastrous Driving Behaviour, Finds US Study

by Coffee Table Science

Driverless or autonomous cars are becoming popular these days. They were designed keeping in mind the safety of drivers and people on the road. However, researchers at the University of California, Irvine (UCI) have identified a possible risk that could trick the collision-avoidance system of driverless cars leading to an inevitable accident. Collision avoidance is installed to prevent or decrease the chances of a collision. It monitors the vehicle’s speed, the speed of the counter vehicle and the distance between the two to avoid a crash. The system contains various sensors and technologies, including radars, cameras and lasers, to spot an imminent collision. 

According to the researchers, the system could be deceived causing an undesirable driving behaviour or abrupt halt by the objects placed on the roadside. “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,” said Qi Alfred Chen, UCI professor of computer science. “Vehicles can’t distinguish between objects present on the road by pure accident or those left intentionally as part of a physical denial-of-service attack. Both can cause erratic driving behaviour.”

Testing Procedure
Chen and his colleagues focused their research on identifying security vulnerabilities of a planning module to know how exposed it is to accidents. A planning module is a part of a software code that builds the collision-avoidance system to control the motion of driverless cars. This component checks the decision-making process of the vehicle while changing lanes, cruising, slowing or stopping down.

“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,” said Ziwen Wan, UCI PhD student in computer 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.”

The researchers designed a tool named PlanFuzz which can efficiently identify vulnerabilities or risk possibilities of accidents in popularly used automated driving systems. They used it to analyze the three different parameters of behavioural planning implementations of open-source as well as industry-grade autonomous driving software systems, Apollo and Autoware as shown in the video demonstration.
During the experiment, the researchers observed that bicycles and cardboard boxes positioned on the roadside caused vehicles to stop permanently on empty intersections and thoroughfares. In another experiment, automatic cars whose collision system detected a nonexistent risk did not change lanes as expected.

“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,” said Chen. “But the overly conservative behaviours 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.”

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