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Perception Safety Requirements and Multi Sensor Systems for Automated Driving Systems

Volvo Technology AB-Hans-Martin Heyn
Comentor AB-Jörgen Trygvesson
  • Technical Paper
  • 2020-01-0101
To be published on 2020-04-14 by SAE International in United States
One major challenge designing vehicles with automated driving systems (ADS) at SAE Level 4 and 5 is the deduction of technical requirements for the perception system given a set of safety requirements. For example, the safety requirements can only be fulfilled by redundancy in the sensor hardware. It is however difficult to specify the amount of redundancy that is required in the sensor system for safe ADS operation. Already nominal ADS operation might require redundancy. Consequently, the use of redundant data must be carefully analyzed to decide if it is available for safety argumentation in the case it is already used for nominal operation. Today the safety requirements for advanced driver assistance systems (ADAS) allow automatic driving relying on suitable perception systems. Their safety case usually argues that in case of a failing sensor array, the human driver is always ready to take control of the vehicle. This argumentation is not possible when developing L4 or higher automation. The paper investigates prerequisites for applying a systematic methodology for analyzing redundancy in a multi-sensor system in…
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Data-Driven Methods for Classification of Driving Styles in Buses

Volvo Technology AB-Magnus Svensson
Halmstad University-Stefan Byttner
Published 2012-04-16 by SAE International in United States
Fuel consumption and vehicle breakdown depend upon the driving style of the driver, for example, hard driving style leads to more wear and consequently more failures of vehicle components. Because of this, it is important to identify and classify the driver's driving style in order to give the driver feedback through a driver assistance system. The driver would then be able to detect and learn to avoid a driving style that is not appropriate. The input data is provided by different sensors installed in the vehicle, where different drivers and driving routes have been measured. The data is subjectively classified into two different driving styles: normal and hard. Hard driving style can be characterized, for example, by rapid acceleration and braking. Since it is not trivial to build a model which is able to distinguish hard driving from normal, a data mining approach has been employed. In the paper, several classifiers are compared (including e.g. neural networks and decision trees) and a discussion is made on the advantages and disadvantages of the different methods.
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Ion Current Sensing for HCCI Combustion Feedback

Volvo Technology AB-Petter Strandh, Magnus Christensen
Dept. of Automatic Control, Lund Institute of Technology-Johan Bengtsson, Rolf Johansson
Published 2003-10-27 by SAE International in United States
Measurement of ion current signal from HCCI combustion was performed. The aim of the work was to investigate if a measurable ion current signal exists and if it is possible to obtain useful information about the combustion process. Furthermore, influence of mixture quality in terms of air/fuel ratio and EGR on the ion current signal was studied. A conventional spark plug was used as ionization sensor. A DC voltage (85 Volt) was applied across the electrode gap. By measuring the current through the gap the state of the gas can be probed. A comparison between measured pressure and ion current signal was performed, and dynamic models were estimated by using system identification methods.The study shows that an ion current signal can be obtained from HCCI combustion and that the signal level is very sensitive to the fuel/air equivalence ratio. The most important result from this study is that the ion current signal proved to be an excellent indicator of the actual combustion timing which is crucial piece of information for HCCI control.
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