| Abstract: | The fast-paced growth of artificial intelligence provides unparalleled
opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of
these tasks is to ensure that vehicles are autonomous, intelligent and able to grow
their repository of information. Machine learning has recently been implemented
in wireless networks, as a major artificial intelligence branch, to solve historically
challenging problems through a data-driven approach. In this article, we discuss
recent progress of applying machine learning into vehicle networks for intelligent
route decision and try to focus on this emerging field. Deep Extreme Learning
Machine (DELM) framework is introduced in this article to be incorporated in
vehicles so they can take human-like assessments. The present GPS compatibility
issues make it difficult for vehicles to take real-time decisions under certain conditions. It leads to the concept of vehicle controller making self-decisions. The
proposed DELM based system for self-intelligent vehicle decision makes use of
the cognitive memory to store route observations. This overcomes inadequacy
of the current in-vehicle route-finding technology and its support. All the relevant
route-related information for the ride will be provided to the user based on its
availability. Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained. During investigation, it has been
observed that proposed framework has the highest accuracy rate with 70% of
training (1435 samples) and 30% of validation (612 samples). Simulation results
validate the intelligent prediction of the proposed method with 98.88%, 98.2%
accuracy during training and validation respectively. |
