Data-Driven User Behavior Evaluation
The ADAS systems are not fully automated. These systems have a number of limitations related to the context where they can operate. Previous studies have demonstrated that the drivers’ understanding and adoption of these systems is not definite and may vary from full technology acceptance to complete ignorance. Therefore, in-depth understanding and interpretation of driver behavior and needs regarding the use of ADAS can significantly help developers to reflect on and improve the systems to meet the users’ expectations.
Recently, the availability of data coming from the in-vehicle sensors network has increased significantly. The amount of received data potentially enables the in-depth quantitative driver behavior evaluation in a time-efficient and reliable way. Moreover, the ability of vehicle sensors and actuator data to synchronize the driver and system performance and assess the driving conditions in the moment of driver-system interaction can contribute to the comprehensive context-aware ADAS evaluation.
Developing methods for objective assessment of driver behavior is a task with a high level of complexity. This process requires (i) investigation of the driver behavior assessment area where vehicle data can be useful; (ii) identification of the influencing factors for evaluating ADAS functions; (iii) definition of the relevant data for the data-driven driver behavior evaluation; (iv) investigation of the ways to improve the feasibility of vehicle data.
The research presented in this thesis focuses on the understanding of vehicle data applicability in user-related studies. The core of this research is the methodology for objective ADAS evaluation and a mixed-method approach that helps to integrate the quantitative methodologies into existing, mainly qualitative, evaluation practices.
The conducted research revealed that vehicle data offers the possibility to determine individual user behavior, and to describe, categorize, and compare this to the average within a group. All of the above mentioned makes the applicability of vehicle data for user-related studies meaningful.
Chalmers, Industri- och materialvetenskap, Produktutveckling
Big Data Usage Can Be a Solution for User Behavior Evaluation: An Automotive Industry Example
Procedia CIRP, 72,; Vol. Volume 72(2018)p. 117-122
Paper i proceeding
BIG DATA ANALYSIS AS A NEW APPROACH FOR USABILITY ATTRIBUTES EVALUATION OF USER INTERFACES: AN AUTOMOTIVE INDUSTRY CONTEXT
Proceedings of the DESIGN 2018 15th International Design Conference,; (2018)p. 1651-1662
Paper i proceeding
Orlovska, J., Novakazi, F., Bligård, LO., Karlsson, I.C.M., Wickman, C. and Söderberg, R. - Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS). Naturalistic Driving Study for ADAS Evaluation.
Informations- och kommunikationsteknik
Övrig annan teknik
Chalmers tekniska högskola
VDL, Hörsalsvägen 7, Chalmers
Opponent: Dr. Fredrik Berglund, Systemite AB, Göteborg, Sverige