Autonomous vehicles are the next revolution in the automobile industry and they are expected to revolutionize the future of transportation. Understanding the scenario in which the autonomous vehicle will operate is critical for its competent functioning. Deep Learning has played a massive role in the progress that has been made till date. Semantic Segmentation, the process of annotating every pixel of an image with an object class, is one crucial part of this scene comprehension using Deep Learning. It is especially useful in Autonomous Driving Research as it requires comprehension of drivable and non-drivable areas, roadside objects and the like. In this paper semantic segmentation has been performed on the Indian Driving Dataset which has been recently compiled on the urban and rural roads of Bengaluru and Hyderabad. This dataset is more challenging compared to other datasets like Cityscapes, since it is based on unstructured driving environments. It has a four level hierarchy and in this paper segmentation has been performed on the first level. Five different models have been trained and their performance has been compared using the Mean Intersection over Union. These are UNET, UNET+RESNET50, DeepLabsV3, PSPNet and SegNet. The highest MIOU of 0.6496 has been achieved. The paper discusses the dataset, exploratory data analysis, preparation, implementation of the five models and studies the performance and compares the results achieved in the process.

The startup focuses on developing self-driving technology for unstructured environment conditions and India's road network is full of such environments. In the thick of it is founder and CEO Sanjeev Sharma, whose interest in the field of robotics was born way back in 2009, when he watched the videos of Team MIT at the 2007 DARPA Urban Challenge. With time, he knew that he wanted to hone in on research to enable autonomous driving in the most difficult traffic environmental scenarios, but it wasn't until 2014, when Sharma deferred his PhD at the University of Massachusetts for a year, that he established Swaayatt Robots. Fast forward eight years and, despite knowing much more about autonomous mobility than in 2014, safety continues to be a huge challenge. Even before we think of the purchasing and operational cost, we're quite some time away from solving for driver safety in an uncontrolled and unstructured environment -- but Swaayatt Robots is trying to fix that.

Artificial Intelligence (AI) powered solutions may soon make roads in India safer to drive. A unique AI approach that uses the predictive power of AI to identify risks on the road, and a collision alert system to communicate timely alerts to drivers, to make several improvements related to road safety, is being implemented in Nagpur City with the objective of bringing a significant reduction in the number of accidents. A project, 'Intelligent Solutions for Road Safety through Technology and Engineering' (iRASTE), has been launched to identify potential accident-causing scenarios while driving a vehicle and alert drivers about the same with the help of the Advanced Driver Assistance System (ADAS). The project will also identify'grey spots', i.e., by data analysis and mobility analysis by continuously monitoring dynamic risks on the entire road network. Grey spots are locations on roads, left unaddressed could become black spots (locations with fatal accidents).

My first encounter with a semi-autonomous vehicle was with a Nissan Leaf, nearly four years ago in the US. It was a nervous experience in the beginning when you take your hands completely off the wheel and see the car automatically following the road or the car ahead. But within minutes you get used to it and start relaxing behind the wheel. But immediately after, the car jumped lanes for no reason. I couldn't understand initially and thought it could be part of the route optimization algorithm.

Computer Vision Guided Deep Learning Network & Machine Learning Techniques to build Fully-Functional Autonomous Vehicles. "If you recognize that self-driving cars are going to prevent accidents, AI will help to reduce one of the leading causes of death in the world." If ride-on-demand services such as Uber & Ola have made a revolution in the idea of conveyance, self-driving vehicles are going to be the next renaissance shaking up the whole transportation industry. This new idea is on its way to become a multi trillion-dollar business -- bigger than Amazon and Walmart combined. According to the World Economic Forum, this big leap in the auto industry will deliver $3.1 trillion annually by reducing number of crashes, need for emergency services, saving man-hours, cost of car ownership & also indirect savings from shorter commutes and less carbon emissions. On top of that, there are endless design possibilities, once you eliminate the need for a steering wheel and a driver.

The population of India is the second-largest for any country in the world, trailing only China. The country also has the world's second-largest road network, following the United States. However, the combination of population and road network also leads to a number of traffic accidents in the South Asian country, with over 200,000 deaths caused as a result in 2013, according to estimates by the World Health Organization. One of the factors that lead to road accidents in the country is collisions between vehicles and animals. And while most other animals are chased away from roads, cows -- which are venerated by many Hindus, who form over 80 percent of India's population of over 1.2 billion -- are usually given a free rein to do as they will.