Abstract
The following paper presents advanced methods for evaluating the reliability of ADAS module readings, based on an analysis of the transient supply current. Changes in the transient current waveform occur due to environmental conditions and damage to a module's inner circuitry. Specific deviations in the waveforms may indicate a certain event – either internal or external. This paper presents how to successfully distinguish certain anomalies using artificial neural network-based classification algorithms without having to interfere with the module's internal circuitry.DOI: http://dx.doi.org/10.5755/j01.eie.24.3.20944
Highlights
Advanced Driver Assistance Systems (ADAS) [1] represent one of the fastest growing areas among numerous automotive technologies
Development of ADAS systems has hastened the achievement of the operation of autonomous vehicles
The module, which is built for the reliability assessment of ADAS sensors, samples the signal at a frequency of 100 [kHz] for around 70 [ms]
Summary
Advanced Driver Assistance Systems (ADAS) [1] represent one of the fastest growing areas among numerous automotive technologies. A need to perform research, experiments and full-scale testing in order to validate these novel technologies and to evaluate whether vehicles equipped with such systems can safely operate on public roads and whether the technology itself performs according to specifications has been observed [2] Carrying out such advanced assessments is dictated by the evolving complexity of ADAS, and by growing requirements of consumers and governmental regulations, which push for higher safety [3] and lower costs [4]. The possibility of implementing the method externally (without having to interfere with the integrity of a certain module's hardware) would be a great step forward when considering the development of novel sensors – engineers would not have to implement the solution in every sensor that is released Such a versatile quality-monitoring tool has to be based on a set of information that would describe the current state of the sensor without interfering with its inner structure. The data, after being extracted, is processed by both a supervised learning backpropagation algorithm [9] and an SOM (Self-organising Map) [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
