1. What is the importance of adaptability in a model, and what unprecedented issues may arise by allowing a car to drive itself with an inputted model only?
Autonomous driving, a revolutionary idea within machine learning, has been improved at an exponential rate for the last few decades. A vast number of different models exist, however what most models retain in commonality is the training process they require. The reason for this being that the models must not only prioritize performance, but adaptability as well. A car can encounter many different styles of road’s, facing different conditions. A model only inputted and not trained will meet its limitations eventually, whether it be factors like the angle of a road, the speed limit on a road, or an object scattered on the road, this is why a trained and adaptable model is crucial.
2. How does variety in roads and their conditions create limitations on customers and the deployment of fully autonomous vehicles?
Autonomous driving has made significant strides towards daily consumer usage. The greatest limitation with a self-driving vehicle however is variability and a model's ability to recognize different structures instantaneously. Machine reaction rates can be drastically better than a human's. A human's average reaction time to a visual stimulus is about ¼ of a second, and in most driving accidents fractions of a second can be the difference between evading an impact, or causing a crash. So why have autonomous vehicles not been implemented worldwide? Although the reliability of the vehicle may not be of main concern in these scenarios, being able to react to objects, animals, or people on the road at a similar reaction rate of a human is pivotal. Weather and geography can also play a crucial role in self-driving. A car's ability to correct hydroplaning, manage sliding on snow, and navigate tight corners is a prerequisite when distributing a new technology as important as this. Once all variables are accounted for and the car's adaptability is nearly identical to a human's then autonomous driving may become an ordinary part of travel.
3. What problem is the AWS Deep Racer trying to solve? Who will benefit if the problem is solved, and how will the model help?
Ever since Karl Benz invented the Motorwagen in 1886 traveling was revolutionized and has remained unchanged for over a century. Although driving has never been safer with more safety regulations than ever in history, thousands of fatal crashes continue to occur on a yearly basis. What autonomous driving is trying to accomplish is the improvement of vehicles dependability, precision, and the diminishment of human-error accidents. Drivers provoke most of their mistakes when driving due to carelessness and distractions. With a computer automated driving system these obstacles could be mitigated. Unlike humans, machines do not become distracted changing the station on the radio, or become drowsy after driving extensive hours. That is why, in a world where we are on the move the majority of the day, autonomous driving is the solution we need for reduced accidents and overall safer travel.
4. Running an overfitted model can be problematic. What types of biases may arise when testing autonomous driving on different tracks/roads?
Different obstacles require distinct, precise maneuvers to evade. A vehicle must be able to maintain composure and differentiate instantly amongst objects it has been trained with. For example, an animal may suddenly appear on a blind turn where the car’s visibility is reduced. When the animal comes into view the vehicle must be able to determine not only that an object is in the way but what it is. The main reason for this being that different animals may require different movements. A smaller animal many times can be run over in a worst case scenario. However, a larger animal like a deer must be evaded at all costs to ensure the passengers safety. This is why overfitting animals, or weather conditions can become dangerous. Another example of this can be seen during winter months. A car may be able to relate sunny weather to safe roads, this however changes when the temperature is below freezing and icy roads must be accounted for. Being able to accurately make decisions from many factors is what it takes for a vehicle to be able to drive itself better than any person. What might we do to mitigate future biases with autonomous driving performance. There are a plethora of things that can be done to mitigate any biases that may arise when training a vehicle model. What it really comes down to however is the model's accuracy when adapting to different situations. To mitigate bias we must increase the rate of recognition, an example of this can be seen when a vehicle encounters a person. Not all people look the same so the model must be able to differentiate what is a person. If the model encounters a child it must recognize it as a human although it may not look like an adult. This can only be done through extensive training points that the data can use to quickly make decisions on certain situations it encounters.
5. Who might be negatively impacted by your analysis? This person or persons might not be directly considered in the analysis, but they might be impacted indirectly.
New technologies implemented to the public do not always have only positive impacts for everyone. Even when Henry Ford increased the accessibility of the motorized vehicle for common consumers to enjoy, fatal motorized crashes only grew exponentially. Just like a new technology, automated cars can not positively impact every potential party at stake. A potential risk when incorporating self-driving vehicles is job loss, specifically occupations as simple as delivery services or trailer drivers. Ever since companies like Amazon have taken control, delivery has become part of our everyday lives. What most people do not realize however is that these companies employ millions to keep their distribution running smoothly. With access to self-delivery vehicles that do not require a person to conduct, many companies will turn to saving more profits by cutting workers. Although examples like this may appear to be an issue that is a century away it is important to keep in mind there are a lot more people at stake when it comes to innovation. Being able to control interactions between people-driven cars and autonomous vehicles can also lead to negative impacts. Taking these things into consideration is needed in order to transition into a new technology.