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Professorship Communications Engineering
Professorship Communications Engineering

Environmental indicators for situation awareness in Autonomous vehicles

Autonomous Vehicles (AV) are the future transportation choice. Six functional automation levels specified by SAE J3016 lay a road map for AV design. Starting from level 3 of automation, AV takes care of driving environment monitoring. Information gathered from different data sources (Sensors installed in the car, V2X messages from nearby traffic, digital infrastructure) are combined, analysed by applying concepts of machine learning, artificial intelligence to assist the AV to perform any dynamic driving task.

Accurate knowledge about the environmental situation around the autonomous vehicle is a must to achieve SAE level 5 of automation. Complex nature of the environment and dynamic behaviour of the objects present in the surrounding environment give rise to uncertainty in measured data. Hence it is necessary to research and develop intelligent algorithms that assist autonomous vehicle to take right decisions under uncertainty. This research work focuses on the following aspects

    Literature survey of different environment indicators that impact the normal driving situation.
    Design of a framework for decision making algorithm by selecting some of the indicators identified as per literature survey.
    Implementation of decision making algorithm in C# in-line to the designed framework.

Type of work: Research Project

Requirements: Working independently, basic knowledge in programming language C# (.NET) or C++, Good performance in the overall course, Successful completion of the course “Optimization for non-mathematicians”

Contact: Sri Venkata Naga Phanindra Akula

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