Understanding human-driver decision making (such acceleration or deceleration) in complex traffic environments is imperative for improving driver assistance systems and accelerating the development of autonomous vehicles. In many cases a human driver performs different tasks alongside driving simultaneously and the current real-world data might not be capturing all the possible driving decisions that a human driver could potentially undertake in certain traffic scenarios. Many situations are safety-critical and capturing the data in a naturalistic traffic environment or in a controlled experiment would be hard. It would also be hard to independently capture data on such driving scenarios due to the rarity of the situations in the existing datasets and the amount of data collection efforts that will be required to procure more data. Therefore, to be able to capture the rarer and unseen driving tasks and to be able to augment the existing information or data on the different types of driving decisions that a driver undertakes on-road, we propose to use a meta-inverse reinforcement learning-based approach.