Novel Techniques for Hijacking Self-Driving Cars

Dr. Franklin De Santa1, and Trevor Phillips2

1 Department of Street Technology, Cranberry-Lemon University, Pittsburgh, PA, USA

2 Leader of the Master Thieves Heist Guild

Abstract

The car theft business has been an integral part of the American automotive industry for decades. If it weren’t for widespread grand theft auto and stereo theft, we’d all still be driving reliable cars such as 90’s Toyota Camrys or 00’s Honda CR-V’s. While many fear that the advances in cyber and physical security in newer vehicles may be the end of the theft driven auto industry of inner cities across America, new techniques have been shown to make state of the art self-driving cars even easier to hijack, capture and sell for a profit than their outdated counterparts. The new technology has even driven higher demand for the products from modern chop shops than ever before. This paper will outline methods to confuse, disable and take advantage of the state of the art algorithms and sensors modern autonomous vehicles are now equipped with to revitalize the domestic car-jacking economy. 

Keywords: Self-Driving Cars, Image Classification, Autonomous Systems, Machine-Learning, Animal Behavior

1. Introduction

When the green movement swept across the United States, demand for rare earth minerals such as copper used in electric motors or lithium and nickel used heavily in battery production has skyrocketed. With most of these rare earth materials existing in the mines of poor South American and African countries easily overthrown in coups orchestrated by Elon Musk’s covert special operation space marines, it would only be ethical to do whatever we can to supply these materials domestically. 

Modern vehicles are heavy laden with these materials and the average chop shop profit from car models newer than 2016 has been doubling yearly. While physical and cyber security has been exponentially increasing over the last decade, more physical capture techniques have become more widely used. These newer self-driving cars have created wide open vulnerabilities. This paper will discuss methods to take advantage of two vulnerable systems; safety sensors, and the Biology based self driving algorithms.      

1.1 Safety Sensors

Many modern cars now contain safety features which have made driving incredibly safer for their passengers and pedestrians. Unfortunately for the car, that safety has come with a cost. They are very easy to fool.  If it can be sensed these self driving cars can see it. However, these self driving cars do not always know the best way to interpret what they are seeing. For instance, you can make something look like a nice parking spot or a pedestrian surprisingly easily, therefore confusing the car to perhaps act irrationally or exactly how you would want it to behave. 

1.2 Biology Based Algorithms

It’s a well known company secret that nearly every commercially viable self driving algorithm is based on animals. Using any normal Animal Algorithm Extractor™  such as shown in Figure 1, the thoughts of any animal can be digitized and applied to a vehicle’s central processing operating system. The animal characteristics are then utilized by the entire vehicle. 

Figure 1: Cheetah Algorithm Extraction Process

Figure 1: Cheetah Algorithm Extraction Process

Choosing what animal to use for what vehicle can be the toughest design choice you can make when designing a self driving algorithm [1]. For the techniques outlined in this paper it is essential to know what animal brain has been extracted to optimize the methods. 

    Originally the idea of using a Cheetah’s brain to power the core algorithm of the Tesla was controversial. Many thought it would be too dangerous to incorporate the brain of a predator. Luckily, safety algorithms transposed from Isaac Asimov’s three laws of robotics etched into the machine language has allowed these predator minds to pass safety tests with flying colors. It was a risk that allowed the consumer to drive the fastest cars on the road.

    It’s not safe to assume every Tesla is a Cheetah and that every Kia is a Pangolin. For instance, the Tesla Cyber truck is based on the brain of an ant to multiply raw towing power and carrying capacity. The method’s for different animals are similar in structure and strategy but the differences in implementation are paramount. 

2. Methodologies

In the past, hijacking cars normally involved picking a lock, hot wiring and just driving away. With the introduction of modern car alarms and trunk monkeys [2], not only is this technique difficult but it can be dangerous. It is important to adapt with the time and use more modern techniques for such modern security methods. Many of the newer techniques, while it can take some planning, more materials, and a larger crew, are not only more likely to succeed, but are less dangerous and less likely to be incriminating. Many of the methods are even passive perfect for the thief on the go. After initial setup, all you have to do is wait. 

2.1 Basic Trapping

While a self-driving car has dropped off his passenger and begins looking for a parking space or his passenger is distracted with a freemium game there are many effective means to trap the car in place. It’s a very simple process. Set up a mechanism with some hair pin trigger which will either trap or incapacitate the vehicle. Bait it with some 10W-40 oil or a nice pine scented air freshener or even just a free parking sign and let the trap do the work for itself. Figures 2-4 show the most commonly used traps for self driving cars including a dead drop, bear trap for trapping the vehicle in place and the baited cage for the more humane trappers. 

It’s important to set many traps for the most success. No one trap is a guarantee, but enough traps around a business park is. Methods for building and maintaining these traps are detailed in [3]. The key is to make the traps look inviting for a car. Try and get it in the shade away from confusing parking signs, people who look like you, and potholes. Try for a nice wide space where it’s unlikely to be scratched by another car. It is possible to bait traps with pheromones but then you only expose your trap to one particular type of self driving algorithm and core animal gender. 

Figure 2: Hair pin dead drop for self driving car capture

Figure 2: Hair pin dead drop 

Figure 3: Bear trap configuration for capturing self driving cars

Figure 3: Bear trap configuration

Figure 4: Standard Cage Trap.

Some traps are clearly more destructive than others. Depending on what you are trying to harvest from the vehicle you may want to use more humane trapping. However, sometimes using the less destructive methods can intuitively become the less humane trap. There have been several instances reported by Yukon Jack and Jim Baker [4] where “…some such darn vehicles hit the bear trap with their non powered axel and will fight for hours until it’ll free himself when the axel itself comes off the car and that slippery son of a bitch just drove on back to its owner in a panic on his two front wheels.” Another instance in [4] saw a 2018 Prius hybrid ram right through its cage. “Even if it couldn’t escape, those poor fellas take a lot of damage ramming into the steel reinforced cage. I’ve lost many a traps to turbo mode. The thing about these electrics and electric hybrids is they don’t need a lot of space to build torque. In the early days of car trapping these cars would burn out a gearbox before getting away.” While the dead drop looks the least humane, it’s clean and quick the autonomous vehicles only suffer for a moment.

2.2 Artificial Pedestrians

The second slightly more labor intensive method for capturing a vehicle is by creating artificial pedestrians and swarming. The safety sensors of a self driving car will never harm a small child. Not even if that small child only looked like a small child, in fact not even if it was the general size and heat signature of a small child. In the first days of trapping safety enhanced vehicles with pedestrians, all you needed were a few cardboard cutouts of children to strategically place around the vehicle trapping it in place. Now the sensors are too smart for that. In the vehicle security arms race, the newer self driving models were equipped with IR cameras as well to determine if something looked like a human and also was as warm as one. Some clever thieves took to heating a large sack of potatoes and duct-taping the products to the card-board cutouts. But, once the potatoes cooled, most autonomous vehicles adjusted their learning algorithms to prevent such false positives due to the potatoes distinct gamma signature. 

Several street fires later, our team gave up on heating the cardboard cutouts. Instead we found that while it’s easy for the car sensors to detect movement and shape, they are proficient at classifying images like they have been advertised to. This is when we started using hogs and trained chimps to surround the vehicle until incapacitated. This does require much more planning, hog feed or a chimp brave enough to stand in front of a car without moving when the car self-honks its horn. The hog feed to keep the animals in place is something you can easily pick up at any farming supply outlet, but training your animals to not move when honked at is another task. A pavlovian method developed by Dr. Caesar Millan in [5] has been found to be a proven method to keep your animals in place. The method involves a simple reward system while increasing the sound of a horn until they do it on the loudest setting without food. That’s when you’re ready to trap a car with your farm animals. 

2.3 Laser Pointers

Moving on to the active techniques, another effective method of hijacking a self driving car is the laser pointer. While it is only useful for the Cheetah based vehicles and autonomy cores based on the animal subfamily Felinae. This is proven in research by ‘Doc’ Antle in [6] which shows that the Felinae subfamily or Cat like animals cannot resist laser pointers. Many theorize that when this species developed their bony hyoid allowing purring at the expense of the infamous roar, they also developed a behavior to entertain their owners for food by confusing bright spots of light with prey and entertaining their prehistoric egyptian patrons. 

To execute this method, you will need a bright green laser pointer (the type used at the planetarium) and an interception crew or trap. Once the Cheetah brained Tesla’s see that green spot on the ground, all you have to do is keep the dot in front of the vehicle until you have the Tesla right where you want him. It is important not to let the dot disappear for very long or the Tesla will get bored and continue on its predetermined route. It’s also important that the dot remains in front of the vehicle. If the dot appears on the hood of the car or directly shined into the sensor, the dashboard sensors will realize that the green dot is not prey and confuse itself until it is again disinterested. This technique is best used with other passive techniques to guide the vehicle into a trap location. In an open parking lot you can keep a self-driving car doing donuts in place for fifteen minutes before he gets dizzy and gives up, but it would be too dangerous to approach. That’s when your crew has to be ready with the follow up trap.   

2.4 Honey Pot Technique

   Similar to laser pointers, another active method of leading a self driving car wherever you want is the honey pot technique. Using a ‘hard to get’ vehicle you can attach a porous box of pheromones which will attract nearby self driving cars of the corresponding species and opposite gender. Sometimes it can help pick a sexy vehicle with a flashy color such as a brightly colored vespa or a VW bug. Sometimes this can be the better method of hijacking your self driving cars in city streets where you don’t have good open areas to use a laser pointer for long stretches of road. A diagram of the Honey Pot technique can be seen in Figure 5.  

Figure 5: Honey Pot technique diagram for capturing self driving cars

Figure 5: Honey Pot technique diagram

   It may be prudent to drive the vehicle remotely for this method. Directions to rig a Vespa or VW bug for this trap technique can be seen in [7] where the tactic was developed. Remotely controlled vehicles can prevent serious injury in the eventuality where the self-driving car does catch up with the honey pot. You do not want to be in between your inviting car and one ton of lithium battery powered horny Tesla. For those voyeuristic thrill seekers, it is advised to at least wear a helmet and attach a sturdy roll cage. The added weight of a roll cage will however make your vehicle less appealing to some self-driving vehicles and you may need a more potent Pheromone concoction. 

3. Data Collection

In order to test these methods, we tested passive trapping methods by themselves and then tested  combinations of the active and passive methods. The laser pointer and the Honey Pot technique was tested leading a vehicle into the three different types of basic traps as well an intersection prepped with farm animals dressed as small children for the artificial pedestrian technique. Traditional traps were tested using a variety of different baits. 

For the first week of testing we were getting repeatable results and then observed a sharp decline in trap yield. It turned out that we were over trapping in Pittsburg. We had to move operations to Cleveland where we annotated our data set and began using a catch and release method of testing.   

4. Results

Results of the new car Hijacking techniques were mixed across the board. It is hard to assess some of the pheromone techniques as another decline in performance was observed as owners began chemically castrating their vehicles after a local recall was issued. Some owners refused the recall out of guilt as their friends’ cars apparently just didn’t drive the same after the procedure and it wasn’t natural. Regardless of your opinion of chemically castrating cars autonomy cores, it was not friendly towards our data analysis. 

4.1 Trap Bait Results

The results are shown in mean cars trapped per week over a three month study. Each trap was constructed at normally distributed locations around Cleveland office parks and other business locations and lunch spots. Traps were checked twice a day and baits were refreshed even if there was not a car in the trap. Results are shown below in Table 1.

Table 1: Trap Yield in cars per week

Dead DropBear TrapCage
10w40 Oil1.31.10.6
Female Cheetah Pheromones3.63.21.3
Pine Scent0.80.40.0
Free Parking2.32.51.0
Chained Antelope10.010.010.0

With extremely publishable p values (p<0.01) we have analyzed that the Dead Drop is the most successful trap with the bear trap as a close second. Even with the recall, the Female pheromone worked second best but was not close to being as successful as the chained antelope. We only checked our traps on weekdays because of Cleveland union pressure and the chained antelope never failed to trap a car. There were some instances where vehicles escaped their traps but the antelope kept the vehicles content until we came around to check for the day. With such positive results in between the female pheromones and chained antelope baits it appears it is true that male Cheetahs really only have two emotions. 

In an unexpected result, the bear trap turned out to have regular issues in damaging the vehicle even more than the dead drop. It worked well at trapping the vehicle but it also caused an unmitigated chain reaction. It was observed that piercing the underside of a Lithium Ion battery in the humid great lakes area or worse above a common street puddle can cause a fairly destructive fire and explosion. Most of the bear traps were triggered by wheels but about 10-15% jumped over too fast and the car would basically explode from the puncture. 

4.2 Active Method Results

The combination test showed much more variety in effectiveness. The results from our tests can be seen in Table 2 as the percentage of successful traps given each attempt. Over all, the percent rate exceeded all passive baiting techniques except for the chained Antelope. 

 Table 2: Technique Success Rate

Dead DropBear TrapCageFarm Animal Pedestrians
Laser Pointer74%78%44%95%
Honey Pot65%63%85%100%

In this test it appeared that the laser pointer was much better at leading the self driving cars into the dead drop and bear trap while it was difficult to lead an autonomous vehicle into those traps without damaging the honey pot. The honey pot was, however, extremely effective with the cage trap as we could honk and flirt until the vehicle entered the cage from temptation. 

The artificial pedestrian technique appeared to be the most effective and only failed once out of our 40 trials when protestors of the Columbus Day Parade spooked all of the animals away before we could even take off the hubcaps. Nearly every autonomous vehicle we tested could not tell the difference between a chicken and a baby, a pig and a toddler, or a goat and a delinquent teen. As soon as we had the vehicle in position, the animals swarmed on the feed and it was all over. Each model of each car was consistently trapped by his programmed moral compass. 

5. Conclusion

It turns out that making cars really fast with the algorithm of a Cheetah brain does have some negative consequences for their owners. The world of autonomous vehicles and grand theft auto will forever advance as technology leaders find ways of making cars faster, safer and more secure. It would not surprise me if for the next study we have to start using real children when better advances in computer vision improve the sensors enough to tell the difference between animals and children. The field of grand theft auto will never be finished, for it is a cat and mouse game one day and a cheetah and antelope game the next.  

6. Future Work

For our next iteration of this street technology research department. We are developing a method to organize Cyber Trucks equipped with ant brain autonomy cores to construct a large mound on the grounds of the Cranberry Lemon Football pitch while only using a cybernetic Ant Queen equipped with a long range Pheromone dispenser. Once enough Cyber Trucks are on the road we will begin the field test. 

Acknowledgements

Thanks to Dirty Mike and the boys at the car thieves guild on sixth street for sharing their advice and experience with my team of scientists during this study. Thanks as well to the owner of the chop shop who did not want to give me his full name who works out of the Goodyear next to the subway on Washington St after hours. 

References

  1. Cheetara F and Snarf H 2015 Animalistic Design Decisions in Autonomous Vehicles :: Journal of Animal Machine Humunculi 
  2. https://www.youtube.com/watch?v=hq0mUxRKHQY Trunk Monkey Bridge Commercial Suburban Auto Group :: Playlist of garbage youtube videos
  3. Dr. Franklin De Santa and Trevor Phillips  2019 Methods in Urban Vehicular Trapology for the Thief on the Go :: Journal of Criminal Futurology
  4. Yukon Jack and Jim Baker 2018 Humane Techniques for Capturing Rogue Autonomous Vehicles :: Wilderness Journal for the Greater Long Beach Area
  5. Dr. C. Millan 2016 Teaching Farm Animals to be Dominant not Aggressive :: Animal Whispererer Annals 
  6. ‘Doc’ Antle B. 2014 Applying Big Cat Techniques to Midsize Autonomous Vehicles :: Journal of Big Cat Sex Cults
  7. Dr. Franklin De Santa and Trevor Phillips  2019 The Honey Pot Technique and How to Seduce a Tesla Model S :: Annals of Street Technology

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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