t’s 2019 already, and we still haven’t had a glimpse of this behemoth technology, that was until a few years ago being touted as the next big thing. Sure, self-driving technology isn’t a cake-walk and it takes years of study, research and experimental procedures to be able to create something like this, but the earliest self-sufficient and truly autonomous cars appeared in the 1980s, with Carnegie Mellon University's Navlab and ALV projects in 1984 and Mercedes-Benz and Bundeswehr University Munich's Eureka Prometheus Project in 1987. And 30 years later, we still haven’t cracked the code.
Now a self-driving car, in the most simplistic terms, means an autonomous car, also known as a robot car or driverless car, is a vehicle that is capable of sensing its environment and moving with little or no human input. These cars combine sensors and software to control, navigate, and drive the vehicle.
The question that naturally follows here is: How do they work exactly? Now most self-driving cars create and maintain an internal map of their immediate surroundings with the help of sensors, such as radar or sonar. This helps in creating a real-time situation and getting an idea of how the route is going to be. Next, the software processes these inputs, generates a course and sends signals to the system’s ‘actuators’, which are in control of the acceleration, braking and steering.
Various other programs are coded within this software which help in better and efficient navigation. These include obstacle avoidance algorithm, traffic estimation and alternate route mapping, predictive modelling and definitive object distinction (differentiating between a motorbike and a cycle). All this ensures a smooth route follow-up as well as adherence to traffic rules.
Since we now have a general idea of how they work out, let’s come back to the pertinent question here. To answer why this technology is still in trial and testing phase, we need to understand how the cost and benefits or self-driving cars are largely hypothetical. First and foremost is safety. One of the most debatable real-time situation in which self-driving cars fail miserably is the choosing between two lives. Let me elaborate this through an example:
If while driving, a person was supposed to suddenly appear in the middle of the road, the car is left with two choices. The first is to stay on the route, and cause potential harm (even death in certain cases) to the person and save the owner. The second option is to immediately alter its route and swerve right/left and put the owner in harm’s way. Either way, there is a possibility of loss of one life, at worst. No amount of coding or algorithm analysis can come up with the answer to this dilemma. For it’s not about just making a choice. It’s about weighing the importance of one life over another.
The second impact of commercially viable self-driving cars is the loss of employment in the driving sector. Millions of employed drivers will go jobless, if self-driving cars do hit the streets. This leads to mass hysteria and global chaos. Until and unless, the drivers have an alternate career possibility, throwing them out on the streets is not an option at all.
There are a lot more factors which enunciate the reason as to why driverless cars are taking so long to hit the streets. But these are minor errors and performance issues. A few tweaks here and there, and the problem’s resolved. But first, we have to tackle safety and equity efficiently. Only then can we move to the next phase of experimental dealings, wherein we can explore the vast possibilities and potential advantages that this technology has to offer. Who knows even in the near future, we might even have a Transformers-like scenario with our cars coming to life. Until then, let’s just be satisfied with the present tools at our disposal. For in the words of Peter Gabriel, “All of these cars were once just a dream in somebody’s head.”