Development Of An Uncrewed Vehicle Prototype Using Deep Learning And IOT

Driverless cars used to be something you'd only see in movies, but they're now becoming a reality in real life, not just in science fiction. Driver error is to blame for 95% of all road accidents. Most autonomous vehicles would be able to avoid human errors because to advances in deep learning technology. However, India's roadways are not as suitable to self-driving automobiles as their western counterparts. Roads in metropolitan areas are overburdened by traffic. Furthermore, in India, reckless driving is not pre-empted by a driverless automobile because it does not respect traffic rules, jump signals, or travel in allocated lanes. Using our prototype and several algorithms such as pothole detection, lane detection, object detection, and others that have been trained in a simulated environment, the probable solution to some portions of the problem can be obtained. A camera in the prototype takes real-time data, which is then analysed with the assistance of a computer utilising deep learning and image processing to achieve the best possible outcomes. After attaining ideal results, the algorithms can be put into practise for commercial application. If not for public use right away, autonomous cars must be allowed for private use first, then gradually integrated into public use, such as on-demand taxis and long hauling. Reduced road accidents, adherence to speed limits, and lane driving are just a few of the advantages that autonomous vehicles will provide.