Menglu Gu

Connected and Autonomous Vehicles (CAVs) are emerging as a potential solution to merging safety problems. However, in mixed traffic scenarios where CAVs coexist with Human-driven Vehicles (HVs), challenges arise due to the lack of proactive cooperation and the limited length of the acceleration lane, complicating the merging processes of CAVs. In these cases, CAVs should actively seize the transit opportunity and perform safe and efficient merging. Failure to do so can lead to decreased traffic efficiency, increased fuel consumption and emissions, compromised self-merging capacities, and heightened crash risk. Therefore, this paper employs the roadside unit and proposes a two-level hierarchical self-merging strategy for CAVs to increase the merging efficiency while ensuring high safety. Since the Surrogate Safety Measures (SSM) can formulate reliable safety assessment and identify the merging conflict risk by setting appropriate threshold, the upper level uses a novel SSM-based method (i.e., the MInimum Acceleration Rate, MIAR) to determine the Merging Sequence (MS). A Theoretical Model for Merging Safety (TMMS) is developed to estimate the MIAR value, and the MIAR threshold is determined using Signal Detection Theory (SDT). At the lower level, the strategy recommends the optimized merging maneuvers, pre-generated using Sequential Quadratic Programming-Model Predictive Control (SQP-MPC), based on the determined MS and the CAV’s velocity. A case study at a real-world freeway merging area demonstrates the effectiveness of the MIAR in measuring merging conflict risk. Besides, numerous simulations are conducted, and the results demonstrate that the proposed strategy significantly improves merging success rate and overall traffic efficiency.

This paper presents a trajectory planning framework for an automated merging vehicle on the freeway acceleration lane when it interacts with human-driven mainline vehicles. This is mainly ascribed to the prediction of the average accelerations of the mainline vehicles and a polynomial merging trajectory planning method. The ERT (Extremely Randomized Trees) classifier was utilized to predict the average accelerations of the preceding and following vehicles on the mainline during the merging interaction process based on a naturalistic dataset at the merging zone. The accuracy of the acceleration prediction model for the preceding vehicle on the mainline is 97.7%, and for the following vehicle, it is 96.1%. In addition, an ERT-QP trajectory planning model considering the results of the ERT model and the necessary constraints was proposed based on the Quintic Polynomial (QP) function. The remaining distance in the acceleration lane is considered to guarantee that the lane-crossing behavior occurs before the end of the acceleration lane. The cost of the QP function was formulated based on the stability, duration, terminal speed, and distances between the merging vehicle and the target lane vehicles. At last, a simulation merging scenario on a freeway acceleration lane was established based on the SUMO (Simulation of Urban Mobility) simulation platform and the MATLAB algorithm control platform. The simulation results showed that the ERT-QP trajectory planning model proposed performed better compared to the embedded SL2015 model in SUMO, with a more stable merging trajectory, a shorter merging duration, and a higher merging terminal speed.

Numerous traffic accidents and near-crashes are caused by lane-changing maneuvers. A merging vehicle (MV) on a freeway acceleration lane has to merge into the faster-travelling mainline stream before the end of the acceleration lane (AL). With the space constraint and speed difference pressure, freeway acceleration lane merging is more complicated and riskier compared with discretionary lane changes. Adequately characterizing the driving behaviors in different merging scenarios and in different sections of the AL can help understand the mechanism of the merging process and benefit merging decision assistance technology development. To resolve this issue, a data acquisition platform was established to acquire natural merging process data. Statistical analysis results demonstrated that the existence of the leading vehicle on the mainline had no significant effect on the merge position of the MV. The average acceleration or speed of the MV in the “Merge” events was relatively larger than that in the “Wait” events. As the MV neared the end of the AL, the risk it could bear increased, and the acceptable lag gap threshold and time-to-collision threshold decreased. The main challenge of a merging decision model lies in how to balance the risk acceptance behavior and the spatial constraint of the AL. Based on the analysis, an adaptive boosting (Adaboost) model was established to model the merging decision of the MV considering the current and historical data of the environment, and the effect of AL. The overall accuracy of the model turned out to be 98.9%.

The driver of a merging vehicle must account for both the lane‐changing risk and the remaining distance in the acceleration lane () during a merging decision‐making process. To investigate the impact of on merge decisions, a typical freeway merging section was selected and monitored with a millimetre radar and a high‐resolution digital camera, which were mounted on the guardrail in the gore area. More than 2000 merging vehicles were captured during the data collection process. The effects of the surrounding vehicles on the merging behaviour were analyzed, and a merging strategy model considering that was based on the random forest algorithm was constructed. The results show that the following vehicle in the target lane is the main factor that affects the merging behaviour of the merging vehicle. When the decision time window was set to 0.6 s, the proposed merging decision model could distinguish ‘Merge’ events and ‘Wait’ events with accuracies of 97.2% and 89.4%, respectively. The overall accuracy of the model was 94.9%, which was 3.9% higher than for a corresponding merging decision model that excluded influence. The proposed merging decision model can aid merging processes and give cues for human‐like merge decisions of automated vehicles.