|
|
|
Two-mode adaptive cruise control design with
humans’ driving habits consideration |
| LUO Li-hua, GONG Li-long, LI Ping, WANG Hui |
| 1. The State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China |
|
|
|
Abstract In order to design an adaptive cruise control (ACC) system which emulates humans’ driving behavior, a two-mode ACC algorithm in model predictive control (MPC) framework was proposed according to the analysis of humans’ driving habits. In this algorithm, fast approaching mode, which is activated when the interdistance is much larger than the desired value, is designed to approach the preceding vehicle quickly when safety and comfort are guaranteed; once the inter-distance is regulated to round the desired value, the ACC vehicle switches to the steady following mode to adapt the spacing and speed safely, reposefully and comfortably. The switching strategy between the two control modes is designed based on fuzzy logic, to imitate humans’ deciding which mode is activated, and smooth the switching responses. The simulation shows that the proposed ACC algorithm not only provides safe and comfortable driving, but also performs natural behaviors to human drivers, and therefore it will be more acceptable to human drivers and lead to the increased usage rate of ACC systems.
|
|
Published: 01 December 2011
|
|
|
考虑驾驶员行驶特性的双模式自适应
巡航控制设计
为了设计一个能够模拟驾驶员行驶行为的汽车自适应巡航控制(ACC)系统,通过分析驾驶员的日常行驶特性,在模型预测控制的框架基础上提出一个双模式的自适应巡航控制策略：当远离前车时,ACC系统采取快速接近模式,在满足安全舒适的前提下以尽量短的时间减小两车间距；一旦车间距调整至期望值附近时,ACC系统切换到平稳跟车模式,使车辆安全、平稳、舒适地对前车进行跟车；为使2种模式切换时能平滑过渡,利用模糊理论设计了2个模式的切换规则来模拟驾驶员的决策过程.通过仿真试验,证实了该双模式ACC控制策略在满足行驶过程中的安全性以及舒适性的同时,有效地反映了驾驶员的日常行驶习惯,更易于被驾驶员接受,有利于提高ACC系统的使用率.
|
|
| ［1］ RAZA H, IOANNOU P. Vehicle following control design for automated highway systems ［J］. IEEE Control Systems Magazine, 1996, 16(6): 43-60.
［2］ VAHIDI A, ESKANDRIAN A. Research advances in intelligent collision avoidance and adaptive cruise control ［J］. IEEE Transactions on Intelligent Transportation Systems, 2003, 4(3): 143-153.
［3］ RAJAMANI R. Vehicle dynamics and control ［M］. New York: Springer, 2006: 153-181.
［4］ ZHANG Jianlong, IOANNOU P. Longitudinal control of heavy trucks in mixed traffic: environmental and fuel economy considerations ［J］. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(1): 92-104.
［5］ NARANJO J E, GONZALEZ C, GARCIA R, et al. Adaptive fuzzy control for intervehicle gap keeping ［J］. IEEE Transactions on Intelligent Transportation Systems, 2003, 4(3): 132-142.
［6］ LUO Lihua, LIU Hong, LI Ping, et al. Model predictive control for adaptive cruise control with multiobjectives: comfort, fueleconomy, safety and carfollowing ［J］. Journal of Zhejiang UniversityScience A, 2010, 11(3): 191-201.
［7］ MOON S, YI K. Human driving databased design of a vehicle adaptive cruise control algorithm ［J］. Vehicle System Dynamics, 2008, 46(8): 661-690.
［8］ YI K, CHUNG J T. Nonlinear brake control for vehicle CW/CA systems ［J］. IEEE Transactions on Mechatronics, 2001, 6(1): 17-25.
［9］ LIANG Chiying, PENG Huei. Optimal adaptive cruise control with guaranteed string stability ［J］. Vehicle System Dynamics, 1999, 32(4/5): 313-330.
［10］ CHEN Xi, LI Zhaohua, YANG Jing, et al. Nested tabu search (TS) and sequential quadratic programming (SQP) method, combined with adaptive model reformulation for heat exchanger network synthesis (HENS) ［J］. Industrial & Engineering Chemistry Research, 2008, 47(7): 2320-2330.
［11］ ZADEH L A. Fuzzy Sets ［J］. Information and Control, 1965, 8(3) : 338-353. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
