To comprehensively coordinate tracking capability

fuel economy

driver comfortable response and car-following safety

a vehicular multi-objective adaptive cruise control(ACC)algorithm is designed

and an integrated longitudinal kinematic model of ACC system including vehicle model and its relationship with preceding vehicle is established. The quadratic objective functions that consider the contradictions between minimal tracking error

low fuel consumption and driver dynamic car-following behavior are developed

and the linear constraints that ensure dynamic car-following

desired comfortable response and driving safety are designed. Following model predictive control theory

the design of multi-objective ACC algorithm can be transformed into an online quadratic programming problem with multi-constraints. Adopting feedback correction mechanism

the modeling mismatch and external disturbances are greatly weakened to improve the control system robustness. Vector relaxation factors are introduced to deal with the non-feasible solution from hard constraints in the optimization process. The simulations show that the proposed algorithm can reduce fuel consumption by 9.3% and decrease tracking error index by 21.7% compared with LQR case in preceding vehicle cycle scenario

thus a good vehicle tracking is realized and the driver desired car following characteristics are satisfied.