Artificial intelligence and compliance should be the essential element of independent vehicle designs and functionalities.
Evidently, you must have encountered all types of drivers on the spectrum if you are a frequent Ola or Uber user. Some carefully provide a relaxing experience and a leisurely, smooth ride. Others say very little and drive assertively to show their focus on landing us swiftly to our desired destination. Depending on our moods, the climate, traffic levels, and other intervening variables, there are days when we may enjoy the original driving style and others when we prefer the latter heavily. And if a driver’s form does not suit my present preferences; it can make a ride uneasy or even agitating.
Flexiblity On Demand
The ability of a driver to make contextual modifications to the requirements of a passenger is a determinant of the passenger’s experience. Perhaps, it impacts the quality of the journey more than shock absorption, climate control, or other components that we traditionally considered to be essential. So, if autonomous vehicle technicians want a long-lasting implementation of their artificially smart “drivers,” they must provide contextual learning skills.
Engineered Safety VS Perceived
Till date, much of the talks about vehicle automation have concentrated on the engineering problems of bringing passengers from point A to point B securely, which we may possibly call Engineered Safety. However, independent riders will have to go beyond just “maintaining it between the lines”. They’ll require to act in ways that can create perceived safety — feeling secure in the midst of continually changing internal and personal circumstances.
But How Does This Make You Feel Like A Typical Traveler?
- You can trust the sensory capacities of your vehicle entirely and feel perfectly comfortable.
- Your logic will override your physical fear, so, no more resulting in white knuckles clutching the seat.
- Precise scheduling and follow ups.
- Autonomous vehicles will redefine road safety.
- Autonomous logistics can conserve the quality of goods, with precise checks until the time of delivery.
Perceived safety plays an integral part in our experience when driven by an independent or human driver, but this is not the only consideration. Our mood controls the manner we guide and influence our expectations.
Even today, we see mood-based options available in luxury vehicles, with many riders providing either a “sport” or “eco” mode based on their present choice. Imagine that you’re late running for a job. To get to office on time, you may enjoy your autonomous vehicle positioning itself more assertively in the traffic flow and passing slower cars earlier. On the other hand, if on a Sunday afternoon you’re sharing a ride with your family, you might prefer the vehicle to follow the traffic flow.
Logistics And Autonomous Vehicles
It would be sensible to expect your preferences to be met in both of these situations, whether you are riding or ridding yourself. Similarly, to provide a pleasant experience for autonomous vehicles, they need to be extremely adaptable to the passenger feedbacks.
Also considering the delivery of the varied and dynamic requirements of companies; autonomous vehicles will need the capacity to adjust their driving style depending on the payload they carry. When carrying a load of fabrics, they may want to prioritize the velocity over smoothness. But, they will have to do the opposite when selling pets, liquor, or risky products.
Artificial Intelligence, Not Automation
The capacity to engineer an independent chauffeur relies on machine learning capacities, because of the overwhelming amount of nuanced situations that may influence perceived safety and contextual comfort. Not only will autonomous vehicles need to learn the baseline comfort levels of their passengers, but also the most common variations for each person using the car. They will also need the capacity to adjust, depending on the time context on the fly.
Commuters will call for the aptitude to interact with their vehicles since there would be a human chauffeur to guide them in this teaching, which implies interacting vocally with an A.I. Siri-style, or choosing from a menu of alternatives. Moreover, it will suggest remembering and defaulting settings for particular situations or proposing those configurations when the context reappears. For example, the next time you’re late for the job, your vehicle should go back to the assertive driving style.
Drawing The Line
As producers increasingly solve the Safety Engineering problems of vehicle autonomy, it is as well essential that they tackle perceived safety and contextual comfort. These will eventually determine which autonomous vehicles will succeed and fail and how long they will take to achieve widespread acceptance.
Engineers must acknowledge the actual reality as we enter this new era of intelligent mobility; we are unlikely to become logical machines based on driverless vehicle constraints. It is, therefore, vital to make driverless vehicles more human.