Humans as a species travel. We have done so in the past during our evolution. We Homo Sapiens have migrated over continents from Africa to Asia on foot. I know it sounds crazy. But it is true. Frankly speaking, I do not see us taking a break from our urge and need to travel. We have traveled in the past. We as a species travel during our present. And we will continue to travel. What I am trying to get at is that though our habit to travel remains the same, our mode of transport has not. We started on foot. Then we invented the wheel. Used horse-carts and bullock-carts. Soon, in the industrial age, we came across the first motor vehicle.
In recent years, cars have developed a lot both in terms of safety and features. There are gas-powered cars, which are slowly being reduced. There are hybrid cars. Now, more recently, there are fully electric cars, and usually people tend to have three types of opinion on them. You love it, hate it, or don’t mind it.
Driving has always been one of those things that looks simple from the outside. This naive opinion changes quickly once you actually do it. You realize how much attention and awareness go into it. It involves the body, the mind, and even a certain emotional presence. Most of us learn to drive as teenagers, and we grow into it until it becomes part of everyday life. Driving can seem like a task or a chore, but it also represents independence, responsibility, and sometimes even identity. Now, with artificial intelligence making its way into vehicles, the experience and meaning of driving are changing. Not all at once, but slowly — piece by piece — the role of the human driver is being reworked.
This essay looks at driving as a practice, not just a physical action. That means thinking about the habits, the ways people pay attention, the informal rules of the road, and the meaning we attach to being behind the wheel. I’ll look at how AI shifts perception, navigation, decision-making, reaction time, and responsibility, and also how the cultural meaning of driving might shift over time. The point is not just that cars are becoming automated, but that our relationship to driving itself is evolving.
Driving is a skill. It is something you improve at by doing it. When people talk about practice theory, they say that ordinary activities are shaped by habits, physical skills, objects, and social expectations (Schatzki, 2001). Driving fits this perfectly. You learn how far to turn the steering wheel without serious thinking or calculating. You simply turn the wheel based on perception. You watch the road but also the edges of your vision. You pick up signals and indications from other drivers. Not spoken ones, but subtle things, like the way a car slightly shifts before changing lanes. The eye contact. You understand when someone is actually going to stop at a stop sign and when they are pretending to. Driving to a certain extent requires and encompasses embodiment, meaning the body learns to respond almost instinctively. At the same time, AI converts this into a digital representation, where road situations are translated into surface data models instead of bodily experience. On the road, when you drive, you observe the vehicles around you. You observe how the drivers are driving them. You can actually perceive or feel if someone is in a hurry from their rash driving. In response, you try to maintain a certain distance from them. You become more cautious because of their driving. But can an AI perceive this? AI does not have instincts. It has barely any capacity to interpret traffic emotions.
When AI systems take over parts of driving, they break the driver’s continuous awareness into specialized functions. Cameras and sensors detect lanes. Software based on neural networks interprets road patterns. Actuators apply steering and braking. The smooth, all-at-once flow of human driving becomes a sequence of technical processes. No thinking, no instincts, just pure code execution. In theory, this should be better. Machines don’t get tired or exhausted. They don’t get distracted by looking at their phones. But on the contrary, machines also don’t interpret context the way we do. A person can tell the difference between a pedestrian walking casually and one who is about to step into traffic. AI sometimes can’t. All you can do is hope that the concoction of sensors and algorithms predicts its environment accurately. There were documented cases where early autonomous systems struggled to classify and recognize their environment correctly, such as not being able to identify a pedestrian pushing a bicycle versus riding it (Boudette, 2018). Even though technology has advanced and has become more accurate, it still serves as an example that AI can be very sharp and very blind at the same time.
AI has also changed navigation. It is no longer a skill but has transformed into a service. Now, navigation apps like Google Maps and Waze do more than show directions — they predict traffic by analyzing data from millions of devices and satellites. In autonomous vehicles, navigation merges with motion prediction and control systems. The driver’s role becomes more like communication with the interface rather than full operational command. When this becomes normal, the everyday skill of navigation fades. Mobility becomes something you receive, not something you perform or control.
Due to AI, judgment has transformed into calculations. AI replaces everyday driving judgment with algorithmic modeling. It predicts outcomes based on patterns from training data (Bojarski et al., 2016). When something falls outside those patterns, the system can hesitate or fail. What happens when the AI fails mid-journey in high-speed traffic conditions? What happens if the driver of that car does not possess driving skills and is completely reliant on AI? Are these questions a good enough reason for a person to still have driving skills and pass a driving test to be allowed to drive a completely autonomous vehicle?
This also affects authorship and responsibility. When a human drives, the human is responsible. When an AI drives, accountability spreads across engineers, corporations, and legal frameworks. It also opens the possibility of misinformation about how safe or capable these systems really are through corporate messaging and advertising.
AI brings along a lot of benefits, but just like a coin, there is always another side to it, which are its risks. AI can react faster than humans in emergencies (NHTSA, 2020). But the 2018 Uber crash happened because the system misinterpreted what it was seeing (NTSB, 2019). This is where regulation becomes necessary. Not just to prevent accidents, but to define who is responsible when they happen. One of the biggest concerns with AVs is that they provide a false sense of security. “With increasing automation, drivers may become disengaged, which can manifest as drivers take their hands off the wheel, eyes off the road, or minds wander away from driving.” (Garbarine) The Tesla crash in 2015 is an example of this situation. “The NHTSB report also found that the driver did not initiate any braking, steering, or other actions to avoid the collision.” (Harris et al.) The glitches in the software and algorithms are also a concerning factor that raises eyebrows (National Accident Helpline). Suppose the driver is not paying attention to the road; that’s bad in itself. But if the system is faulty on top of that, it will be disastrous. An autonomous car was involved in another incident in 2019. Within minutes, a few people had hacked the self-driving car. This is a serious issue. Hackers might take over the vehicle as a result, steal personal data, track down locations, cause traffic jams, or use the vehicle as a mobile bomb. When we talk about AI, we should not forget about its vulnerabilities. This all changes how and to what extent we trust our vehicles.
I am a car enthusiast. I love to go on drives. I can confidently say that driving is tied to identity and emotion. Late-night drives, road trips, and the sense of freedom behind the wheel are cultural symbols. As AI grows, driving may become less about personal autonomy and more about being transported — similar to how wearable devices like Apple Watch and brain-computer interaction, such as AI reasoning, shift awareness of one’s own body and decision-making toward technological systems, respectively. I feel that driving will become like horseback riding. Once cars became common, horses moved from a daily necessity to a hobby and cultural practice. Driving could follow the same path. It may survive mainly in leisure contexts, especially as digital mobility services expand.
As I conclude, I would like to say that AI is changing perception, navigation, decision-making, and cultural meaning. Driving may move from an everyday necessity to an optional activity. The experience does not disappear — it changes form, shaped by infrastructure, software, and social expectations. The feedback loop between humans and machines will define what driving becomes next.