ADAS Evolution: LSTM-Driven Autonomous Adaptation to Individual Driver Preferences

作     者：Y. Wang N. Ota S. Debernard J-C. Popieul 

作者机构：Univ. Polytechnique Hauts-de-France CNRS UMR 8201 – LAMIH - Laboratory of Industrial and Human Automation Control Mechanical engineering and Computer science F-59313 Valenciennes France University of Tsukuba Tsukuba Japon 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2024年第58卷第30期

页      面：230-235页

主　　题：ADAS Intelligent Vehicles Machine Learning LSTM Human-Machine Cooperation 

摘      要：The ongoing advancement of advanced driver assistance systems (ADAS) has led to notable improvements in vehicle safety and efficiency within the automotive industry (Kala, 2016). Nevertheless, these systems also present new challenges and demands for developers, especially concerning driver trust and acceptance, which are vital for the safety and efficiency of collaborative human-machine driving. In response to these problems, the CoCoVéIA project proposes an innovative approach in which the driver assistance system (ADAS) integrates continuous learning of individual driving preferences, to boost driver confidence and acceptance. At the core of this method is the use of autonomous learning algorithms, particularly long short-term memory (LSTM) networks, which are adept at learning and adjusting to different driving preferences in real-time. This paper confirms the efficacy of LSTM algorithms in precisely learning and predicting driving preferences through experimental data, showcasing their ability to accommodate a variety of driver behaviors and preferences.