On-ramp merging strategy for connected and automated vehicles based on complete information static game

Improper handling of vehicle on-ramp merging may hinder traffic flow and contribute to lower fuel economy,while also increasing the risk of collisions.Cooperative control for connected and automated vehicles(CAVs)has the potential to significantly reduce negative environmental impact while also improve driving safety and traffic efficiency.Therefore,in this paper,we focus on the scenario of CAVs on-ramp merging and propose a centralized control method.Merging sequence(MS)allocation and motion planning are two key issues in this process.To deal with these problems,we first propose an MS allocation method based on a complete information static game whereby the mixed-strategy Nash equilibrium is calculated for an individual vehicle to select its strategy.The on-ramp merging problem is then formulated as a bi-objective(total fuel consumption and total travel time)optimization problem,to which optimal control based on Pontryagin's minimum principle(PMP)is applied to solve the motion planning issue.To determine the proper parameters in the bi-objective optimization problem,a varying-scale grid search method is proposed to explore possible solutions at different scales.In this method,an improved quicksort algorithm is designed to search for the Pareto front,and the(approximately)unbiased Pareto solution for the bi-objective optimization problem is finally determined as the optimal solution.The proposed on-ramp merging strategy is validated via numerical simulation,and comparison with other strategies demonstrates its effectiveness in terms of fuel economy and traffic efficiency.

Precise vehicle ego-localization using feature matching of pavement images

Purpose–Precise vehicle localization is a basic and critical technique for various intelligent transportation system(ITS)applications.It also needs to adapt to the complex road environments in real-time.The global positioning system and the strap-down inertial navigation system are two common techniques in thefield of vehicle localization.However,the localization accuracy,reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding,vision enhancement and automatic parking.Aiming at the problems above,this paper aims to propose a precise vehicle ego-localization method based on image matching.Design/methodology/approach–This study included three steps,Step 1,extraction of feature points.After getting the image,the local features in the pavement images were extracted using an improved speeded up robust features algorithm.Step 2,eliminate mismatch points.Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust.Step 3,matching of feature points and trajectory generation.Findings–Through the matching and validation of the extracted local feature points,the relative translation and rotation offsets between two consecutive pavement images were calculated,eventually,the trajectory of the vehicle was generated.Originality/value–The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.