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Autonomous-Car Technology May Possess a Threat to Privacy


Self-driving, which was only possible in science fiction has now become something very common, a reality for people in San Francisco. Other places too, in the US, have been testing the technology.

However, with new innovation comes its consequences. Here, it is the hefty collection of data required for autonomous vehicles to run. The enormous amount of visual and other data gathered by a fleet of vehicles driving down public streets raises the concern that people's movements may be monitored, captured, and stored by businesses, law enforcement, or nefarious individuals—including vendor employees. For commuters, pedestrians, and everyone else who uses city sidewalks and public highways, the sheer volume of this data poses a serious threat to their civil liberties and privacy.

It is a concern that through this autonomous vehicle technology, authorities or a hacker may as well hack an individual’s aggregate movement, including their commutes, outings to see friends and family, and trips to the doctor or an attorney. Pedestrians might not be aware that these vehicles can be used by law enforcement.

Apparently, autonomous vehicles depend on more than a dozen cameras and sensors placed around the vehicle to detect other vehicles, traffic signs, obstructions, and pedestrians. Since the most prominent autonomous vehicles are driven by private corporations, there are certain details that remain unclear like how detailed image recognition software is used to analyze the film of people walking along the street, if used at all. What audio-collection capabilities do these vehicles have? How long will this video be kept on file? Who is able to access it? What safeguards are in place to ensure the safety and privacy of the footage? How do these businesses abide by regional and national privacy regulations like the California Consumer Privacy Act? 

Another question that arises is the relationship between autonomous vehicle companies and law enforcement agencies. 

According to Bloomberg, at least nine search warrants were issued against a self-driving car business in Maricopa County, Arizona as well as San Francisco. The San Francisco Police Department stated in a training manual that Vice obtained in 2022 that "Autonomous vehicles are recording their surroundings continuously and have potential to help with investigative leads...investigations has already done this several times."

With the growing number of self-driving cars in the city streets that are also collecting huge chunks of data, focusing on implementing strong privacy laws has become a critical endeavour. Vehicle owners may also need to know how much video footage police are asking for and when, if ever, autonomous vehicle operators will object to unduly broad requests. With this, it has also become crucial to figure out whether police have access to archived video or real-time live feeds from the cameras on the vehicles.

One may speculate that in the coming years, cities and regulators will have to face challenging decisions pertaining to the safe operation of autonomous vehicles. It has thus become crucial that authorities take into account the potential effects on civil liberties of the massive amounts of data and video that these self-driving cars will acquire, in addition to the safety of drivers and pedestrians.

Making Self-driven Vehicles a Reality!


To make self-driving vehicles a reality and to bring them on roads, they need to be able to safely and flawlessly navigate traffic without collisions or jams. Northwestern University researchers have made this possible by developing the first decentralized algorithm with a collision-free guarantee.

The algorithm was tested in a simulation of 1,024 robots and in a throng of 100 real robots by the researchers in the laboratory. The robots carefully and efficiently followed to form a command shape.

 “If you have many autonomous vehicles on the road, you don’t want them to collide with one another or get stuck in a deadlock,” said Northwestern’s Michael Rubenstein, who led the study. 

“By understanding how to control our swarm robots to form shapes, we can understand how to control fleets of autonomous vehicles as they interact with each other.” Rubenstein, the professor who led the study is the Lisa Wissner-Slivka and Benjamin Slivka Professor in Computer Science and Mechanical Engineering in Northwestern’s McCormick School of Engineering and a member of Northwestern’s Center for Robotics and Biosystems. The paper will be published in the journal IEEE Transactions on Robotics later this month.

Rubenstein algorithm is decentralized and not centralized which makes it foolproof and safer. “If the system is centralized and a robot stops working, then the entire system fails,” Rubenstein said. “In a decentralized system, there is no leader telling all the other robots what to do. Each robot makes its own decisions. If one robot fails in a swarm, the swarm can still accomplish the task.”

But still, to move effortlessly, the robots need to communicate with each other so as not to collide and for this, the algorithm divides the ground below in grids and by using a GPS type technology the robot knows where to go - where the grid is empty and no other robot is moving to that spot.

 “The robots refuse to move to a spot until that spot is free and until they know that no other robots are moving to that same spot,” Rubenstein said. “They are careful and reserve a space ahead of time.”  
These robots interact locally to make decisions and hence do not need a central command body. This system according to Rubenstein can be very effective in driver-less cars as well as in warehouses with many robots working at the same time.