Introduction
Over the past decade, the evolution of self-driving cars has accelerated. Most vehicles on the roads today have some form of automation. Since self-driving cars can theoretically react faster than human drivers and do not get behind the wheel drunk, do not write text messages while driving, and do not get tired, they should significantly improve the safety of vehicles. However, the biggest problem for the automotive industry is related to the handling of unforeseen situations that arise in extreme cases.
Discussion
The moral dilemma is based on the idea that artificial intelligence in self—driving vehicles cannot conclude several favorable outcomes – or, for that matter, the “least bad” effects. A classic example is an autonomous vehicle that decided to drive off the road, possibly killing the driver inside, to avoid a collision with a school bus full of children (Stilgoe, p. 639). People use common sense to cope with unexpected driving phenomena: a deer runs out on the highway. Flooding makes it difficult or impossible to move along the road. Cars are floating, trying to climb the icy hill (Lobanova & Evtiukov, p. 364). Unfortunately, no one knows how to embed common sense into vehicles or computers. Instead of common sense capabilities, automatic control developers should anticipate and encode every possible situation. Machine learning can only help if manufacturers expect every case and provide training examples for every possible situation.
Another problem for self-driving cars is that computer vision systems are prone to errors since they can be deceived in a way that people cannot be deceived. For example, researchers have shown that minor changes to the speed limit sign can cause a machine learning system to think that the sign says 85 miles per hour, not 35 miles per hour (Stilgoe, p. 637). Similarly, some hackers tricked the Tesla autopilot into changing lanes by using brightly colored stickers to create a fake road (Stilgoe, p. 641). In both cases, these changes deceived cars but not people. These are just a few ways an attacker can confuse cars or trucks, forcing them to move off the road or collide with obstacles. Thus, self-driving vehicles cannot be wholly relied on since their systems can be deceived.
There is also an opinion that self-driving cars are safer than people-driven cars. A person causes the vast majority of road accidents. After all, most accidents were caused by some human error, whether it was speeding, reckless driving, inattention, or, even worse, drunk driving. It is estimated that fully automated vehicles can reduce the number of road accidents by 90% (Lobanova & Evtiukov, p. 365). On the other hand, self-driving cars are purely analytical, relying on cameras, radars, and other sensors for navigation. However, for example, in actual driving, many Tesla owners report that shadows, for instance, from tree branches, are often perceived by their cars as natural objects (Nees, p. 63). In the case of an Uber test vehicle that struck a pedestrian, the vehicle’s object recognition program first classified the pedestrian as an unknown object, then as a vehicle, and finally as a bicycle (Nees, p. 66). Thus, autonomous cars are still no more careful on the road than human-controlled cars.
Conclusion
There are good reasons to believe self-driving cars will be safer than human drivers. They never get tired, do not write messages behind the wheel, and do not drive for a drink. There are also good reasons to believe they will no longer be safe. Nobody knows how to turn common sense into computers. There is good reason to think that some types of autonomous vehicles may not be able to cope with unforeseen situations on the road (Kirszner and Mandell, 2020). Therefore, people should still be wary of self-driving cars.
Works Cited
Kirszner, Laurie G., and Stephen R. Mandell, editors. Practical Argument: A Text and
Anthology. 4th ed. Bedford/St. Martin’s, 2020.
Lobanova, Yuliia, and Sergey Evtiukov. “Role and Methods of Accident Ability Diagnosis in Ensuring Traffic Safety.” Transportation Research Procedia, no. 50, 2020, pp. 363-372.
Nees, Michael A. “Safer than the Average Human Driver (Who is Less Safe than Me)? Examining a Popular Safety Benchmark for Self-driving Cars.” Journal of safety research, no. 69, 2019, pp. 61-68.
Stilgoe, Jack. “How Can We Know a Self-driving Car is Safe?.” Ethics and Information Technology, vol. 23 no. 4, 2021, pp. 635-647.