Proactive Scheduling and Resource Management for Connected Autonomous Vehicles: A Data Science Perspective

Carpooling and the ride-sharing idea are currently resolving many issues faced by modern societies. The issues regarding the overuse of oil, traffic jams, inefficient use of time, pollution due to overuse of vehicles on the road, and health problems. It is also expected that ride-sharing and carpooling will be more efficient for autonomous vehicles because of their unmanned nature and full-fledged autonomy. When unmanned cars will do the responsibility of carpooling and ride-sharing or car-hailing, many issues regarding booking rides, location sharing, payment handling, and privacy issues must be improved. To cope with these issues, mainly concerning the scheduling of resources, we need effective scheduling techniques to handle all kinds of emotional problems and provide a pollution-free and accident-free environment on autonomous vehicles’ roads. Among other approaches, we feel that Data Science provides a perfect opportunity to leverage machine learning models to classify and see what parameters can encourage people to opt for a move towards connected autonomous vehicles. In this paper, we discuss autonomous vehicles, Vehicle-as-a-Service, and their role in reducing CO2 emissions. The dataset used in this study gives insights into the city of Chicago’s taxi trips. The dataset includes data about taxi trips, their respective duration, and anonymized data about the passengers. We also discuss some studies by a taxonomy that will identify the gap of an optimal incentive mechanism that will influence users to join carpooling in autonomous cars instead of having their vehicles.