Autonomous Car Driving Using Deep Learning
Narayana Darapaneni, Pratosh Raj R, Anwesh Reddy Paduri, Emmanuel Anand, Kumar Rajarathinam, Prem Thomas Eapen, Karthik Sethuraman, Sharath Krishnamurthy
Abstract
Autonomous driving is a hot topic in the area of artificial intelligence and machine learning where numerous researches is being conducted to make driverless vehicles mainstream. A successful autonomous driving system should contain accurate results, interpretability (for safety and reliability), and cost-effective. The core of autonomous driving is a union of perception (conscious about surroundings- roads, obstacles, etc.) and decision making (making decisions to drive corresponding to the perceived environment). Problems with the current innovations, too complex model architecture to achieve the state-of-the-art results which makes it too expensive and hard to interpret. In this paper, we show that using simple models (vanilla UNet/FCN for perception and linear algebra techniques for decision making) which are easy to interpret and produce a cost-effective system, attains good results. Our idea serves for level 1 autonomous driving which can be further scaled up for higher levels.