A multistep cooperative lane change strategy for connected and autonomous vehicle platoons departing from dedicated lanes

•A novel multistep cooperative mandatory platoon lane change (CMPLC) strategy is proposed for CAV platoon lane changes in mixed traffic.•Three trajectory planning models are formulated to allow CAVs to quickly create acceptable gaps and complete lane changes.•A quantitative evaluation of the impact of CAV platoon lane changes on HDVs in the target lane is conducted.•The locations of the lane change critical points are identified in different scenarios, serving as warning indicators for CAV platoons. This study presents a Cooperative Mandatory Platoon Lane Change (CMPLC) strategy for Connected and Autonomous Vehicles (CAVs) navigating from a Dedicated Lane (DL) to an adjacent General-Purpose Lane (GPL) with Human-Driven Vehicles (HDVs). The CMPLC strategy involves two lane change steps and a gap widening step. Initially, a safety criterion for lane changes is proposed, integrating the merits of the Intelligent Driver Model (IDM) and the Gipps model, to constrain the gaps that the CAVs in the platoon can safely move into. Guided by this criterion, the strategy identifies the largest gap in the GPL near the platoon as the target gap in the first step. A subset of CAVs in the platoon performs rapid lane changes into the target gap, avoiding collision with HDVs. The following gap widening step employs a trajectory planning model based on Model Predictive Control (MPC) to widen two gaps in the GPL while considering the uncertain behavior of HDVs. Simultaneously, guided by another two trajectory planning models, the remaining CAVs in the DL sequentially move into their target gaps during the second lane change step. To validate the proposed strategy, a visual simulation platform integrating SUMO and MATLAB was developed. Numerical experiments were conducted on a two-lane highway featuring a DL on the inner side and a GPL on the outer side. The experimental results show that the proposed CMPLC strategy can always achieve higher lane change success rate and shorter execution time compared to the Individual Vehicle Lane Change (IVLC) strategy, under various settings of traffic demand, platoon size, and starting location. Additionally, through numerous simulation experiments, the critical lane change points for CAV platoons in different scenarios are identified, which can serve as early warning indicators for CAV platoons to avoid unsuccessful lane changes.