A Deep Reinforcement Learning Framework for Optimizing Fuel Economy of Vehicles

Abstract: Machine learning (ML) has experienced immense growth in the last decade; applications of ML are found in nearly every sphere of society. We gather a vast amount of data during our day-to-day operations, and the machines use this data to make intelligent decisions for us. Transport is an essential part of our life. Since early times it has been a critical requirement of human beings, and as we progressed, its need has risen tremendously. Nowadays, we cannot think of a life without any means of transportation. A vehicle is the primary means of transportation in the modern industrial world. Most of the world's population uses a vehicle for transportation needs. The internal combustion engine (ICE) has been the vehicle's primary power source since the early days. Approximately 99% of world transport uses internal combustion engine-based vehicles. As with every other device, Internal combustion technology has seen enormous improvement during the past half a century. These gradual improvements have made these ICEs better than ever before. But as we have progressed, we have also been exposed to the harms of combustion-based power sources like ICEs. Their effect on the environment has been damaging. The future way is to limit the use of hydrocarbon-based fuels for combustion. As it's nearly impossible to eradicate the use of these fuels instantly as most of the population is deeply dependent on them, the best way to proceed is to find alternate transportation sources like EVs. But as these technologies are developing, it's of great importance; we use our modern ML-based technologies to make the current ICE-based vehicles as fuel-efficient as possible so that this transition to electric-based vehicles becomes smooth and the effect of ICEs is minimized on the environment. The thesis presents a deep reinforcement learning (DRL) based technique, which can develop an optimal policy to effectively reduce vehicle fuel consumption by providing intelligent driving inputs to the vehicle. The study shows that it is possible to use (DRL) methods to improve fuel efficiency in a simulated vehicular environment. Moreover, the models seem to develop new types of operational vehicle policies that are very unconventional but result in improved fuel efficiency in simulated environments. Although these cannot be implemented in the current form in the actual environment, modified versions of these policies may be able to work in real-life scenarios.