Effects of Wireless Communication on Integrated Risk Management Based Automated Vehicle

This paper describes effects of wireless communication on integrated risk management based automated vehicle. In order to improve safety and ride comfort of automated vehicle, it is important to estimate and represent surround vehicles' behavior such as yaw rate, absolute velocity and acceleration. Recent developments in vehicle onboard computers and wireless communications devices, also known as dedicated short-range communication (DSRC) devices allow the exchange of information between vehicles (V2V communications). The integration of wireless communications to conventional advanced driver assistant systems (ADAS) aims to enhance prediction performance of automated vehicles. Based on the probabilistic prediction of object vehicle using radar only or radar/V2V information fusion, desired steering angle and longitudinal acceleration for keeping the subject vehicle safe are derived from Model Predictive Control (MPC) approach. The performance of the proposed V2V/radar fusion algorithm has been verified via simulation studies.

[1]  M. Kothare,et al.  Robust constrained model predictive control using linear matrix inequalities , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[2]  Davor Hrovat,et al.  Vehicle steering intervention through differential braking , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[3]  B. Lusetti,et al.  Driver steering assistance for lane departure avoidance , 2009 .

[4]  Francois Dion,et al.  Vehicle Platoon Control in High-Latency Wireless Communications Environment , 2012 .

[5]  David Q. Mayne,et al.  Robust model predictive control of constrained linear systems with bounded disturbances , 2005, Autom..

[6]  H. Eric Tseng,et al.  A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles , 2014 .

[7]  Sterling J. Anderson,et al.  An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios , 2010 .

[8]  Kyongsu Yi,et al.  Robust Mode Predictive Control for Lane Change of Automated Driving Vehicles , 2015 .

[9]  Kyongsu Yi,et al.  An IMM/EKF Approach for Enhanced Multitarget State Estimation for Application to Integrated Risk Management System , 2015, IEEE Transactions on Vehicular Technology.

[10]  Seungwuk Moon,et al.  Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance , 2009 .

[11]  Andrea Goldsmith,et al.  Effects of communication delay on string stability in vehicle platoons , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[12]  Matthias Kranz,et al.  Sensing the environment for future driver assistance combining autonomous and cooperative appliances , 2008 .

[13]  Davor Hrovat,et al.  Vehicle Steering Intervention Through Differential Braking , 1998 .

[14]  F. Gustafsson,et al.  A new approach to lane guidance systems , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[15]  Francesco Borrelli,et al.  Robust Predictive Control for semi-autonomous vehicles with an uncertain driver model , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[16]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[17]  Raja Sengupta,et al.  Effects of vehicle-vehicle/roadside-vehicle communication on adaptive cruise controlled highway systems , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[18]  Kyongsu Yi,et al.  Lane-keeping assistance control algorithm using differential braking to prevent unintended lane departures , 2014 .

[19]  Francesco Borrelli,et al.  A linear time varying model predictive control approach to the integrated vehicle dynamics control problem in autonomous systems , 2007, 2007 46th IEEE Conference on Decision and Control.

[20]  Kyongsu Yi,et al.  Probabilistic and Holistic Prediction of Vehicle States Using Sensor Fusion for Application to Integrated Vehicle Safety Systems , 2014, IEEE Transactions on Intelligent Transportation Systems.

[21]  Fawzi Nashashibi,et al.  An application of V2V communications : Cooperation of vehicles for a better car tracking using GPS and vision systems , 2009, 2009 IEEE Vehicular Networking Conference (VNC).