%0 Electronic Article %A Senlin Cheng %A Chuanhai Wang %A Shuangteng Zhang %A Ruixue Zong %A Xinli Deng %K highway lane-change %K driving styles %K individualised driving-aid strategy %K personalised driving assistance strategy %K driving assistant system %K risk assessment method %K adaptive model predictive control algorithm %K different drivers %K vehicle dynamic characteristics %K Taken Matlab/Simulink software %K personalised driving model %K personalised lane-change %K control strategy %K personalised driving criterion %K personalised driving assistant strategy %K driving safety %K driving risks %X The driving assistant system is conducive to reduce the accidents caused by operational mistakes in the complex environment of lane-change on the highway. However, the adaptability between the system and drivers has not been taken into consideration. To solve this problem, this study put forward a personalised driving assistant strategy in the process of highway lane-change. In the study, as a methodology based on the measured data of vehicle dynamic characteristics and personalised driving characteristics, it established a criterion for the personalised driving model, dissected the factors affecting driving safety and put forward a risk assessment method for individualised driving according to the criterion. Taken Matlab/Simulink software as a simulation experiment platform, it verified the rationality and feasibility of the individualised driving-aid strategy of highway lane-change with the aid of the adaptive model predictive control algorithm. The study results show that the personalised driving criterion and risk assessment method proposed in this study can effectively distinguish the driving styles and driving risks of different drivers, and the personalised driving assistance strategy not only respects different drivers’ individualised operation styles but also effectively controls driving risks. %T Study on control strategy for personalised lane-change on highway %B The Journal of Engineering %D November 2018 %V 2018 %N 16 %P 1724-1730 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=p0hn9nh1q1e4.x-iet-live-01content/journals/10.1049/joe.2018.8269 %G EN