This thesis aims to predict energy (fossil fuel and electric) consumption of internal combustion and hybrid vehicles. This thesis is in association with Wireless cars. Accurate prediction of energy consumption in vehicles is vital, as it can pave the way for a more sustainable future. Despite its criticality, accurate predictions of energy consumption are a challenging task. Several factors which impact energy consumption, i.e., average speed, trip duration, etc. , are not available at the beginning of the trip. To use such kinds of features to the full extent, we will be using multitask learning methods. The dataset provided by the company covers different aspects, including GPS information, energy consumption, time, and vehicle configurations which suggests multitask learning as an intriguing technique to approach it. Multitask learning uses a shared feature space wherein information is shared between multiple relevant tasks, helping to predict energy consumption accurately. 

Multitask learning (MTL) is susceptible to two crucial issues, namely task dominance and conflicting gradients between different tasks. Previous studies have addressed these issues separately , but we propose a unified framework to tackle these problems simultaneously in this thesis. In the proposed framework we are addressing the issue of task dominance model using Gradient Normalization (GradNorm)  while the issue of conflicting gradients is solved using the Projecting conflicting gradient (PCGrad) technique. Experimental results have shown the success of this method in comparison with other state-of-the-art methods.

Apart from creating unified architecture, we are also analyzing the behavioral pattern of the MTL model. This experiment was performed to check which tasks provide the maximum contribution to help improve the overall performance.

Apart from the two contributions, we have also performed an additional experiment of task dominance analysis where we have given an equal budget to the main task and also to the auxiliary tasks. The motivation to perform this experiment is to create a main task dominant MTL model, which can take advantage of multitask learning, and improve the performance of the main task simultaneously. 

All the novelties presented in this thesis indicate the potential of multitask learning techniques and their future applicability in the vehicular domain.