Overcoming Challenges in Connected Autonomous Vehicles Development: Open Source Vehicular Data Analytics Platform 2019-01-1914
Data Science and Machine Learning are buzzwords 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. Analyzing big data on the basis of internet 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 aid 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 big data and is able to make best-suited decisions, is progressing in ways that is fundamental to the growth of automotive industry. The paper envisages the future of connected and- autonomous-vehicles (CAVs) as computers-on-wheels. These are pictured as sophisticated systems with sensors on board as data sources and a lot of other functions and running services to support autonomous-driving. These services are considered to be computationally expensive. The unit performing on-board has limited computing resources while on the other hand, the cloud-based architecture has unconstrained resources, but it suffers from extended unexpected latency that requires large-scale internet data transfer. To deal with this conflicting scenario, Open-Vehicular-Data-Analytics- Platform (Open VDAP) for CAVs may be used. This allows CAVs to detect dynamically status of each service, computation overhead and the optimal-offloading-destination such that each service could be finished within an acceptable-latency. Open VDAP is an open-source platform that offers free APIs and real-field vehicle data to researchers and developers in the community, allowing them to deploy and evaluate applications in real environment.