Enhanced Reliability of ADAS Sensors Based on the Observation of the Power Supply Current and Neural Network Application
Advanced Driver Assistance Systems (ADAS) are essential parts for developing the autonomous vehicle concept. They cooperate with different on-board car equipment to make driving safe and comfortable. There are many ways to monitor their behaviour and assess their reliability. The presented solution combines the versatility of applications (it can be used with almost any kind of sensors), low cost (data acquisition using this method requires only a simple electronic circuit) and requires no adjustments of the sensor’s software or hardware. Using this type of analysis, one can determine the device’s family, find any over- and under-voltages that can damage the sensor or even detect two-way CAN communication malfunctions. Since the data acquired is complex (and can be troublesome during processing) – one of the best solutions is to cope with the problem by using a variety of neural networks.
KeywordsADAS Predictive maintenance Neural network CAN Data acquisition
This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project “Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model” (Grant Agreement No. 612207) and research work financed from the funds for science in the years 2016–2017, which are allocated to an international co-financed project (Grant Agreement No. 3491/7.PR/15/2016/2).
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