---

Abstract:
Design of the controller in an adaptive cruise control (ACC) system is a critical part which prevents the collision between the host vehicle and the front vehicle. In ACC, system starts to work from an initialization state to a stable state after a short time. Passing from the initialization state to the stable state has not been considered in the previous designs. However for a suitable design, the initialization states that can cause a collision and also the unstable states should be considered. Therefore in this paper first by improving the reference signal and then by using a PID controller and optimizing its gains with BFO optimization algorithm, the system will consider the unstable initialization states. Simulation results show that by improving the reference signal and optimizing the gains of PID controller, not only the collision before the stable state is prevented, but also the performance of the controller is improved 74.2% in safety constraint and 15% in comfort constraint in the specific profile of the front vehicle speed that has several driving situations like hard stop and stop & go.

Keywords: Intelligent Transportation Systems, Adaptive Cruise Control, PID Controller, Safety and Comfort Constraint

References:
[1]   Peters, George A., and Barbara J. Peters. "Automotive vehicle safety", CRC Press, 2003.   Google Scholar
[2]   Winner, Hermann, et al. "Adaptive cruise control system aspects and development trends," No. 961010. SAE Technical Paper, 1996.   Google Scholar
[3]   Bishop, Richard. "Intelligent vehicle technology and trends", 2005.    Google Scholar
[4]   Nagappan, S. "Adaptive Cruise Control: Laser Diodes as an Alternative to Millimeter-Wave Radars." Ward's Auto Electronics| September/October, 2005.   Google Scholar
[5]  Naranjo, José E., et al. "ACC+ Stop&go maneuvers with throttle and brake fuzzy control." IEEE Transactions on Intelligent Transportation Systems, pp. 213-225, 2006.   Google Scholar
[6]   Naranjo, José E., et al. "Adaptive fuzzy control for inter-vehicle gap keeping." IEEE Transactions on Intelligent Transportation Systems, pp. 132-142, 2003.   Google Scholar
[7]   Ganji, Behnam, et al. "Adaptive cruise control of a HEV using sliding mode control." Expert Systems with Applications 41.2, pp. 607-615, 2014.  Google Scholar
[8]  Milanés, Vicente, et al. "Comparing fuzzy and intelligent PI controllers in stop-and-go manoeuvres." IEEE Transactions on Control Systems Technology, pp. 770-778, 2012.   Google Scholar
[9]  Rosenfeld, Avi, et al. "Learning Drivers’ Behavior to Improve Adaptive Cruise Control." Journal of Intelligent Transportation Systems, pp. 18-31, 2015.   Google Scholar
[10]   Piccinini, Giulio Francesco Bianchi, et al. "Driver's behavioral adaptation to Adaptive Cruise Control (ACC): The case of speed and time headway." Journal of safety research 49, 2014.   Google Scholar
[11]   Moon, Seungwuk, Ilki Moon, and Kyongsu Yi. "Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance." Control Engineering Practice, pp. 442-455, 2009.    Google Scholar
[12]   Martinez, John-Jairo, and Carlos Canudas-de-Wit. "A safe longitudinal control for adaptive cruise control and stop-and-go scenarios." IEEE Transactions on Control Systems Technology, pp. 246-258, 2007.   Google Scholar
[13]   Batayneh, Wafa, et al. "Fuzzy-Based Adaptive Cruise Controller with Collision Avoidance and Warning System." Mechanical Engineering Research 3.1, 2013.   Google Scholar
[14]  Alonso, Luciano, and Juan Pérez-Oria. "Genetic Optimization of Fuzzy Adaptive Cruise Control for Urban Traffic." Fuzzy Modeling and Control: Theory and Applications, pp. 255-271, 2014.    Google Scholar
[15]   Sathiyan, S. Paul, and S. Suresh Kumar. "Optimised fuzzy controller for improved comfort level during transitions in Cruise and Adaptive Cruise Control Vehicles." IEEE International Conference on Signal Processing and Communication Engineering Systems (SPACES), pp. 86-91, 2015.   Google Scholar
[16]  R. Rizvi, et al, “Fuzzy Adaptive Cruise Control system with speed sign detection capability.” IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 968-976, 2014.   Google Scholar
[17]   Cherian, Merry, and S. Paul Sathiyan. "Neural Network based ACC for Optimized safety and comfort." International Journal of Computer Applications 42, no. 14, 2012.   Google Scholar
[18]   Zhao, Dongbin, et al. "Full-range adaptive cruise control based on supervised adaptive dynamic programming." Neurocomputing 125, pp. 57-67, 2014.   Google Scholar
[19]    Shakouri, P., et al. "Adaptive cruise control system: Comparing gain-scheduling PI and LQ controllers." tc 2.1, 2011.   Google Scholar
[20]    Rajamani, Rajesh. Vehicle dynamics and control. Springer Science & Business Media, 2011.   Google Scholar
[21]   Xiong, Lu, et al. "Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation." Vehicle system dynamics, pp. 831-846, 2012.   Google Scholar
[22]   M. Short, M. J. Pont, Q. Huang, “Simulation of vehicle longitudinal dynamics.” Safety and Reliability of Distributed Embedded Systems, 2004.   Google Scholar
 

---

 Cite this paper as:
S. M. Mohtavipour , H. Darvish Gohari , H. S. Shahhoseini . "Improvement of Adaptive Cruise Control Performance by Considering Initialization States." Universal Journal of Control and Automation 3.3 (2015) 53 - 61. doi: 10.13189/ujca.2015.030303