Driving comfort assessment is a prerequisite to improve the journey experience for the drivers as well as the passengers. In this work, we proposed an advanced approach for the measurement of driving comfort in real-time. Different types of environmental features are considered along with the traditionally used Comfort Index (CI), and an Environment-specific Comfort Index (EsCI) is proposed. EsCI is also inversely proportional to the drivers’ comfort level, just like CI. We also developed an android application named QDCL (Quantification of Driver Comfort Level) for overall data collection and computation of EsCI from the same. A series of driving experiments at different times of the day and different traffic conditions have been performed in Indian urban road scenarios to assess the performance of QDCL and the relevance of EsCI. We extended the work by studying the effects of different external stimuli on the computed driving comfort level. The performance of EsCI is observed to outperform the traditionally used CI (Comfort Index) in terms of accuracy for the quantification of overall driving comfort.