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Abstract. Every autonomous vehicle has an analytic framework which monitors the decision making of the vehicle to keep it safe. By tweaking the FMEA (Failure Mode Effect Analysis) framework and applying this to the decision system will make significant increase in the quality of the decisions, especially in series of decision and its overall outcome. This will avoid collisions and better quality of decision.The proposed methodology uses this approach to identify the risks associated with the best alternative selected. The FMEA requires to be running at real time. It has to keep its previous experiences in hand to do quick/split time decision making. This paper considers a case study of FMEA framework applied to autonomous driving vehicles to support decision making. It shows a significant increase in the performance in the execution of FMEA over GPU. It also brings out a comparison of CUDA to TPL and sequential execution.

This article illustrates a technique for tracking longitudinal wheel slips in real time using an embedded microcontroller to map current consumption against real-time current consumed by the engine. This system can be used and operated separately of each other on more than one wheel. To detect wheel slippage, a predefined slip curve mapped to a specific DC electric motor is mapped against the current consumed by the same operational motors. This paper also recommends a convenient control algorithm to calculate its slippage of the wheel in real time. This approach is implemented using distinct load and terrain on a planetary exploration robot.

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