Application of MFFC and Edge Detection for Remote Driven Vehicles through Matlab

oai:ojs.portalgaruda.org:article/159Speech recognition is a rapidly emerging technology in Human Computer Interaction HCI. It has many applications as we use it from search engines to the device control it serves many areas we interact every day from dawn to dusk. Along with the uses we have many limitations in speech processing such as Language barrier, Accent and Noise, so to implement the speech processing we have many challenges. To enable the advantages of this speech processing most of the leading software companies like Apple, Microsoft and Google are continuously evolving their speech enabled applications. The speech processing eases the physically challenged people’s interaction with the devices and makes them productive. The Idea of the automatically driven cars is introduced by Google and Audi, but they are not acceptable in most of the cases because of lacking trust in current technology. Thus we here worked on the remote driven vehicle in a more secured method using the Mel Frequency cepstral Coefficient. The secure driving of the vehicle can be ensured by the remote driver. This technique is very rapid and more reliable for the speech detection thus the remote driver can use the MFCC and the video from the vehicle needed to be broadcasted to the remote driver that can be done an IP camera running on a data network. And the instructions can from the remote driver can be sent to the vehicle by an app created with python that connected to a micro controller. To minimize the limitations in the remote drive the vehicle must be enabled with automatic braking when obstacle approaches which can be done by the Ultrasonic sensors that do the distance estimation. The remote drivers usually have a very limited view of road they drive and they must get the precise edges of the road this can be achieved by processing the stream of images to calculate the edge in Matlab