Autonomous Connected Vehicles: Overview of Application of Open Source Vehicular Data Analytics Platform 2019-01-1083
Data Science and Machine Learning areplayig a key role in our everyday lives, as is evident from its applications, such as voice recognition features in vehicles and on cell phones, automatic facial and traffic sign recognition etc. Analysing big data on the basis of searches, pattern recognition and learning algorithms provides deep understanding of the behaviour of processes, systems, nature, and ultimately the people. The already implementable idea of autonomous driving is nearly a reality for many drivers today with the help of lane keeping assistance and adaptive cruise control systems in the vehicle.The drift towards connected, autonomous, and artificially intelligent systems that constantly learns from the big data and is able to make best suited decisions is advancing in ways that are fundamentally important to many automotive industries. The paper envisages the future of connected and autonomous vehicles (CAVs)as computers on wheels. These are pictured as sophisticated systems with on-board sensors as data sources and a lot of other functions and services running to support autonomous driving. These services are considered to be computationally expensive. The on-board computation unit has limited computing resources. On the other hand, the cloud-based architecture has unconstrained resources but it suffers from unexpected extended latency that leads to the large-scale Internet data transmission. To deal with this dilemma, Open Vehicular Data Analytics Platform(OpenVDAP) for CAVs may be used.This allows CAVs to dynamically detect status of each service, computation overhead and the optimal offloading destination so that each service could be finished within an acceptable latency. OpenVDAP is an open-source platform that offers free APIs and real-field vehicle data to the researchers and developers in the community, allowing them to deploy and evaluate applications on the real environment.