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Improved automotive self learning system using hypothesis test triggered forgetting to adapt to change points

: Heinrichs, Robert; Fritzsche, Martin; Radusch, Ilja


IEEE Intelligent Transportation Systems Society -ITSS-; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Intelligent Vehicles Symposium, IV 2014. Vol.1 : June 8 - 11, 2014, Dearborn, Michigan, USA
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-3637-3
ISBN: 978-1-4799-3639-7
Intelligent Vehicles Symposium (IV) <2014, Dearborn/Mich.>
Conference Paper
Fraunhofer FOKUS ()
frequency measurement; histograms; learning systems; probability distribution; sensors; vehicles; weight measurement

Current advanced driver assistance systems (ADAS) measure and evaluate every environment scenario in each trip from scratch. They are not able to draw on information of previous trips because currently a memory function in production vehicles does not exist. Learning systems for vehicles were proposed to aggregate information gathered by driving routes multiple times. This information can be used to create a learning map and enable highly automated driving. Learned information can represent a certain state of the environment captured by sensors like speed limits or geo-positions of roadworks. Changes in the vehicle relevant environment will affect vehicle perception and may affect the distribution of measured variables over time. The instant where the underlying distribution of a variable changes is denoted as change point. This can invalidate parts of the acquired knowledge and was not sufficiently dealt with in previous publications in the automotive field. The ability to detect the mentioned change points is essential for the necessary ongoing update of the learning map by devaluing or removing any invalidated knowledge from memory. For this purpose we propose a statistical hypothesis test and investigate its application for a prototypic ADAS: a Curve Warning Assistance System. The formulated test is able to detect change points faster and more robust than previously proposed algorithms. The ability to aggregate information in a learning map and to handle information actualization and devaluation represents a major building block for future highly automated driver assistance systems.