Driverless cars or self-driving cars have been around for a while now. They are also known as autonomous vehicle (AV), connected and autonomous vehicle (CAV), driverless car, robo-car, or robotic car but the basic premise is the same. It is a vehicle that is capable of sensing its environment and moving safely with little or no human input. They often rely on a variety of sensors such as radar, sonar and GPS as well as advanced control systems to interpret sensory information for appropriate navigation paths, as well as obstacles and relevant signage.
However, AI could be considered as the ‘mind’ or ‘virtual driver’ of autonomous vehicles. In today’s digital world, AI is utilised heavily for accurate road navigation as well as vehicle operation. Although there are numerous types and makes of vehicles out there, Autonomous cars are considered the future of transportation. With AI-driven navigation, they are able to constantly absorb information such as road condition and live traffic updates and factor those in their decision-making process, for instance, on which routes to take.
When correctly utilised, AI is able to get from Point A to B with the best directions no matter how complex the journey may be. The addition of deep learning models creates specific, powerful maps that are able to go through a huge amount of data and images quickly to aid their navigational process. However one of the key challenges car manufacturers face is in collecting the huge amount of data required and that it is updated and accurate. Keeping these in mind, they must capitalise new modelling techniques and adopt a crowdsourcing approach to fuel AI through data collection.
Through data and the Internet of Things, most car navigation systems can amend the route immediately should any travel disruptions come up. However, they are not able to forecast how the traffic situation will change during the travel time on any possible route. This can be rectified through the use of machine learning-enabled dynamic routing, which helps AI navigation systems to actually predict how traffic will change and how the journey will be disrupted.
To enable accurate AI-driven models, a huge amount of data is required – the more data there is, the more accurate the maps will be. An enormous amount of data is required to train AI models so that they truly represent reality. Abstracting the process through AI, however, can help us achieve such “impossible” but vital tasks. With unique generative algorithms, AI can select an image and apple different stimulations to it so it is entered in the database. Hence, when an AI-enabled navigation system encounters atypical conditions on the road, it can adapt rather than lock up. This is a crucial step in helping AI not only to recognise roads but also respond to them.
As with any technology, privacy and security remains a top concern. User location information is sensitive hence car and mapmakers whom rely on such data in the development of their maps would have to keep this in mind. One solution would be the usage of privacy-aware machine learning instead. This involves AI algorithms that learn from anonymised raw data. Once trained, the models can be shared and companies can continually update and enhance the existing group of shared models with new ones as they become available.
Given the rapid advances in technology, autonomous driving is likely to become the norm in the not too distant future. Soon, navigation systems fuelled by AI will become the norm in our vehicles. As such, automakers will need to ensure that the data used to inform these systems is as detailed and accurate as possible. A variety of secure sources should be tapped on to provide data for these systems to undertake digital mapping as well. As data is increasingly sourced through new methods and sources, the future of AI assisted navigation looks brighter than ever.
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