TY - GEN
T1 - Problem formulation improvement for multi-vehicle collision avoidance and impact mitigation
AU - Yuan, Ye
AU - Lu, Xiao Yun
AU - Wang, Jianqiang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/26
Y1 - 2015/8/26
N2 - Multi-vehicle longitudinal collision avoidance is a long-standing topic in vehicle control and Active Safety. In our previous work we formulated the multi-vehicle collision avoidance and impact mitigation problem assuming V2V (vehicle-to-vehicle communication) as a finite time horizon Model Predictive Control (MPC) problem. We intended to use the relative kinetic energy between the approaching vehicles as the measure of potential collision impact, which forms a quadratic objective function. However, the constraint representing vehicle approaching was not formulated appropriately, which caused a very stringent constraint to the feasible could set in the optimization at each time step of the sequential quadratic programming resulted from the MPC process. In this paper, we propose two improvements to the problem formulation: one is in the objective function and the other is in constraints. Performance comparisons between those algorithms are conducted to analyze the pros and cons of those two improvements through simulations.
AB - Multi-vehicle longitudinal collision avoidance is a long-standing topic in vehicle control and Active Safety. In our previous work we formulated the multi-vehicle collision avoidance and impact mitigation problem assuming V2V (vehicle-to-vehicle communication) as a finite time horizon Model Predictive Control (MPC) problem. We intended to use the relative kinetic energy between the approaching vehicles as the measure of potential collision impact, which forms a quadratic objective function. However, the constraint representing vehicle approaching was not formulated appropriately, which caused a very stringent constraint to the feasible could set in the optimization at each time step of the sequential quadratic programming resulted from the MPC process. In this paper, we propose two improvements to the problem formulation: one is in the objective function and the other is in constraints. Performance comparisons between those algorithms are conducted to analyze the pros and cons of those two improvements through simulations.
UR - http://www.scopus.com/inward/record.url?scp=84951081375&partnerID=8YFLogxK
U2 - 10.1109/IVS.2015.7225797
DO - 10.1109/IVS.2015.7225797
M3 - Conference contribution
AN - SCOPUS:84951081375
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 889
EP - 894
BT - IV 2015 - 2015 IEEE Intelligent Vehicles Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Intelligent Vehicles Symposium, IV 2015
Y2 - 28 June 2015 through 1 July 2015
ER -