Cooperative merging control strategy of connected and automated vehicles on highways

To improve traffic performance when on-ramp vehicles merge into the mainstream,a collaborative merging control strategy is proposed to determine the merging sequence and trajectory control of vehicles.Merging trajectory planning takes the minimization of vehicle acceleration as the optimization objective.Either the variational method or the quadratic programming method is utilized to determine arrival time,optimal time and control variables for each vehicle.As a supplement,the adaptive cruise control(ACC)model is used to calculate each control variable in each time interval on special occasions.Simulation results show that the cooperative merging control strategy outperforms the optimal control strategy.The root mean square(RMS)of acceleration and the root mean square error(RMSE)of time headway are significantly decreased,with the reductions up to 90.1%and 25.2%,respectively.Under the cooperative control strategy,the difference between the average speed and desired speed consistently approaches zero.In addition,few or no collisions occur.To conclude,the proposed strategy favours the improvements in passenger comfort,traffic efficiency,traffic stability and safety around highway on-ramps.

Model predictive control for adaptive cruise control with multi-objectives: comfort,fuel-economy,safety and car-following

For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.