Connected and Autonomous Vehicle's Behavior in Heterogenous Disordered Traffic in Metropolitan Cities

The government of India introduced the smart city program in 2015 to make the selected city smart and provide all the necessary services required for the public. The smart city program will become much more efficient with the introduction of artificial intelligence, information and communication technology, and sensors technology. These technologies facilitate the necessary infrastructure for the deployment of connected and autonomous vehicles (CAVs) in these cities of India. Self-driving technology has only recently been introduced in commercial vehicles however, the integration of these vehicles into a CAV ecosystem is still being tested. In a study conducted by the Victoria Transport Policy Institute in 2015, it was estimated that by 2040, 75% of all vehicles will be autonomous, hence we could soon see the implementation of Autonomous Vehicles and its integration into a network in India. However, the deployment of CAVs could be faced with several immediate challenges with respect to road users: from Vulnerable Road Users (VRUs) to other motorized vehicles. Studies on the challenges of CAV deployment have been conducted in several developing economies, but there exists a gap when it comes to understanding the potential challenges that could occur in an Indian context. Hence, this paper focuses on analyzing the behavior of CAVs in a heterogenous disordered traffic such as in Indian metropolitan cities. This analysis is based on a systematic review of existing literature and policies. This review focuses on specific aspects based on the Indian scenario such as the sensing capabilities required for efficient functioning of CAVs, driving behavior related requirements, contextual challenges of road and traffic environments and regulatory challenges. The outcome of this study is the identification of these challenges in the deployment of CAVs in Indian metropolitan cities and the formulation of possible solutions for the identified problems.