Merging Control Research Articles

Ecological cooperative merging control of heterogeneous electric vehicle platoons.

Vehicle platooning improves energy savings via vehicle-to-vehicle (V2V) communication. Ecological cooperative adaptive cruise control (Eco-CACC) is implemented in platoons for merging task by using regrouped platoon models. The merging positions are selected in the middle and tail of an original platoon with a two-vehicle sub-platoon. The distributed nonlinear model predictive controller based on signal temporal logic (DNMPC-STL) approach is developed to model the Eco-CACC merging strategy. The performance of the Eco-CACC merging strategy is modeled by objective control for a predecessor-leader following (PLF) topology. The results demonstrate that merging positions located in the tail exhibit superior performance and can be used to improve stability, tracking performance, energy consumption efficiency and SOC of battery.

Constraint‐Driven Multilane Merging Control for Underactuated Connected and Automated Vehicle System With Mismatched Uncertainty

ABSTRACTThis article investigates the merging control for underactuated connected and automated vehicle (CAV) systems with mismatched uncertainty. The objective is to guarantee the safe and prescribed dynamic performance of the system during multilane merging. For CAV multidimensional motions, a strongly coupled vehicle dynamics with time‐varying uncertainties is constructed. For the nominal system, the control objectives are designed as system constraints, with equality constraints guaranteeing merging and time‐varying inequality constraints guaranteeing dynamic performance bounds. Based on the diffeomorphism method and bounded constraint, the system constraints are reconstructed to a unified representation. Combining system constraints and underactuated structure, the nominal controller is derived based on constraint‐following. For the uncertain system, the uncertainty is decomposed into matched and mismatched portions by orthogonal decomposition. The adaptive robust controller is designed based only on matched uncertainty. Through Lyapunov minimax analysis, the control renders the errors uniformly bounded and uniformly ultimately bounded. Simulation results show that merging and platooning are effectively achieved with the proposed control. The system has excellent transient and steady state performance even in the presence of time‐varying uncertainties.

Truck Platooning Merging Control Method in Merge Areas Based on the IDM Model and V2X Communication

Truck platooning technology has been widely studied for its advantages in traffic efficiency and energy savings. Although recent studies have made significant progress in specific scenarios, research on merging ramp trucks into the main road truck platoons remains insufficient. This paper proposes an innovative cooperative control method for merging ramp trucks and main road truck platoons based on vehicle-to-everything (V2X) technology. The method integrates the intelligent driver model (IDM), acceleration control logic for merging trucks before merging, lane-changing decision logic, steering control methods, and on-board unit (OBU) and roadside unit (RSU) communication technologies. This ensures that autonomous merging trucks can smoothly join the main road truck platoon under various insertion positions and acceleration lane lengths. After successfully joining the platoon, the merging trucks can maintain appropriate spacing and high speed, thereby improving overall traffic efficiency. Further analysis on different insertion positions and acceleration lane lengths reveals that when using the proposed method, merging trucks have the least impact on the overall platoon when they join at the head or tail of the main road truck platoon. Although the proposed method performs better with longer acceleration lanes, these lanes are costly to construct. The proposed method can also enable successful platoon joining with shorter acceleration lanes, optimizing the use of acceleration lanes to some extent.

Enhancing Work Zone Safety: Evaluating Static Merge Strategies Through Microscopic Traffic Simulation

Background The utilization of merging control has been proposed as a strategy to maximize the capacity of roads in work zone areas. The static Early Merge (EM) and static Late Merge (LM) controls are extensively implemented among these strategies. Although many studies have investigated the efficacy of these controls through the analysis of field data or microscopic traffic simulations, such comparisons are frequently conducted under different work zone conditions, which can result in inconsistent, contradictory conclusions. Materials and Methods A simulation study was carried out using the VISSIM microscopic traffic simulator within a self-developed work zone featuring a 2-to-1 lane closure setup to comprehensively assess and contrast the traffic efficiency of the EM and LM controls. Furthermore, through a comprehensive analysis and comparison of network performance within the designed work zone across various scenarios, including queue length, vehicle delays, and travel time, the significant VISSIM parameters influencing the system's performance were identified. Results The analysis revealed that the parameters CC1 (headway time) and CC2 (longitudinal following threshold) from the car-following model exert more significant influence in the simulated work zone throughput than the Safety Distance Reduction Factor (SDRF) parameter from the lane-changing model. Discussion According to the simulation findings, implementing the EM control is preferable when drivers display aggressive behavior and maintain relatively short safety distances (i.e., low CC1 values). Conversely, opting for the LM control is more advisable in work zone areas where drivers demonstrate cautious driving tendencies and maintain longer safety distances (i.e., high CC1 values). Conclusion The efficacy of static EM and LM was analyzed in a 2-to-1 lane closure work zone on a freeway using the microscopic traffic simulator VISSIM. Simulation results were compared to identify the most relevant VISSIM parameters that influence work zone throughput. Our results indicate that the parameters CC1 and CC2 from the car-following model have a more substantial impact than the SDRF parameter from the lane-changing model. In particular, our comparison results suggest that the work zone throughput decreases in both the EM and LM scenarios as the values of CC1 and CC2 increase. Additionally, SDRF has a relatively negligible effect on the network performance of both merge strategies.

Progressive virtual risk-based vehicle trajectory optimization in mixed traffic flow

Ramp-merging zones have perennially served as bottlenecks for both safety and efficiency within road traffic. Addressing the inherent uncertainties and anomalous behaviors potentially exhibited by human-driven vehicles (HDVs) in mixed traffic flow, this study introduced a distributed merging control algorithm based on virtual risk assessment. Initially, we proposed a progressive vehicle projection method based on matching degree, formulated to engender smooth longitudinal accelerations. Building upon this foundation, a virtual risk-based longitudinal planning model named V-RQM was established, furnishing longitudinal accelerations with robust stability and safety to Connected and Autonomous Vehicles (CAVs). Results from four diverse simulation experiments corroborate that V-RQM is aptly versatile, suited for conventional one-dimensional car-following environments as well as pre-merging control in heterogeneous traffic flow. The model not only mitigates oscillatory amplitudes while sustaining high traffic efficiency but also exhibits commendable resilience against uncertainties and anomalies indigenous to mixed traffic flow. This research pioneers a novel model-driven approach to ramp-merging control, setting the stage for the full exploitation of CAV capabilities.

Safe platooning and merging control using constructive barrier feedback

This paper proposes a novel formation control design for safe platooning and merging of a group of vehicles in multi-lane road scenarios. Provided a leader vehicle is independently controlled, the objective is controlling the follower vehicles to drive in the desired lane with a constant desired distance behind the neighboring (preceding) vehicle while preventing collisions with both the neighboring vehicle and the road’s edges. Inspired by the recent concept of constructive barrier feedback, the proposed controller for each follower vehicle is composed of two parts: one is the nominal controller that ensures its tracking of the neighboring vehicle; another is for collision avoidance by using divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring vehicle and road edges without compromising the nominal controller’s performance. The key contribution of this work is that the proposed control method ensures collision-free platooning and merging control in multi-lane road scenarios with computational efficiency and systematic stability analysis. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.

Platoon-based control strategy in mixed traffic to improve ramp merging efficiency and fuel reduction

Traffic congestion in ramp merge areas, which are bottleneck areas in freeway systems, has been alleviated by traditional research methods. However, with the maturation of autonomous driving technology, control methods can shift to information-based real-time control, which aids in improving ramp merging. In this paper, we propose a ramp merging control method that integrates platoon control. This method pre-allocates vehicles in mixed traffic into platoons, utilizing the lead CAV for speed guidance to lead several HDVs. The paper also introduces a scheme to redistribute downstream gaps, leveraging information sharing and automation technologies among vehicles to control the speed of mainline vehicles. The goal is to allocate excess downstream gaps to the merging area to accommodate the ramp vehicle platoon. Our simulations validate the method, comparing uncontrolled and controlled scenarios for vehicle speeds and accelerations, highlighting the potential to lower overall travel costs while maintaining flow.

Collision-Free Merging Control Via Trajectory Optimization for Connected and Autonomous Vehicles

On-ramp merging zones have long been accident-prone spots as well as major triggers of traffic congestion in freeway networks. This paper proposes a collision-free merging control for connected and autonomous vehicles based on trajectory optimization to facilitate traffic safety and efficiency. The optimization is embedded in a model predictive control algorithm with the aim of maximizing the average travel speeds of all vehicles in the planning horizon. To guarantee collision-free merging, the merging control allows only one vehicle to pass the merging zone at a time. Safety constraints, including minimum time gaps and minimum distances between vehicles, are also considered. A rolling horizon control framework is established to enable dynamic traffic control; specifically, generating optimal acceleration decisions for each time step. The optimization problem is formulated as mixed-integer linear programming to enable efficient computation. Numerical studies and sensitivity analysis were conducted to validate the effectiveness and applicability of the proposed method. The results show that the introduced safety constraints can guarantee collision-free merging. Compared to the traditional method, Ih is built on the intelligent driver model and the minimizing overall braking induced by lane-change model, the proposed optimization-based approach results in 5.0%–22.2% improvements with respect to average travel speeds during the merging process. The safety and efficiency improvements are more significant when dealing with higher demand levels. Notably, compared to the difference in initial speeds between the mainline and on-ramp vehicles, the length of the acceleration lane has a greater impact on the control performance.

A time and energy efficient merging control for platoon formation of connected and automated electric vehicles at on-ramps

A time and energy efficient merging control for platoon formation of connected and automated electric vehicles at on-ramps

Resilient Neural-Sliding-Mode Distance Regulation in Merging Control of Heterogeneous Connected Automated Vehicles Subject to Deception Attacks

Resilient Neural-Sliding-Mode Distance Regulation in Merging Control of Heterogeneous Connected Automated Vehicles Subject to Deception Attacks

Sequencing-Enabled Hierarchical Cooperative CAV On-Ramp Merging Control With Enhanced Stability and Feasibility

Sequencing-Enabled Hierarchical Cooperative CAV On-Ramp Merging Control With Enhanced Stability and Feasibility

Cooperative Merging Control Based on Reinforcement Learning With Dynamic Waypoint

Cooperative Merging Control Based on Reinforcement Learning With Dynamic Waypoint

Deep Q-Network-Enabled Platoon Merging Approach for Autonomous Vehicles

Platoon merging control of autonomous vehicles driving in a platoon formation can improve traffic efficiency. However, current platoon merging control approaches primarily rely on rules, making it challenging to achieve optimal control. In this study, we propose a platoon merging control approach based on a deep Q-network (DQN). First, we specify the state and action of the vehicle and establish a set of reward functions to ensure safe driving. Then, the DQN algorithm is used to train a neural network suitable for merging the controls of connected and automated vehicles (CAVs) to continuously approach the state-value function. Finally, we compare the proposed approach with a rule-based (RB) vehicle merging approach using a MATLAB simulation. In particular, CAVs are driven simultaneously using the two approaches in a random environment. The simulation results show that the proposed DQN-based vehicle merging approach requires less merging travel time and fewer vehicle lane change times than the RB approach. Additionally, merging can result in improved capacity in medium and high traffic densities compared with no-merging: the higher the CAV penetration rate, the larger the improvement. We verify the effectiveness of the proposed approach for different initial conditions. We suggest that the proposed method is a safe and robust method for CAV platoon merging, and that it can be applied to increase the capacity of freeways and roads.

Cooperative On-Ramp Merging Control Model for Mixed Traffic on Multi-Lane Freeways

This paper proposes a hierarchical model for cooperative on-ramp merging control (CORMC) in mixed traffic with both connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). The upper-layer of the CORMC model employs an anticipatory position searching (APS) algorithm to determine the anticipatory positions at which merging vehicles (MVs) should merge from the on-ramp lane to the adjacent mainline lane, and to assign cooperative vehicles (CVs) for each MV. A collaborative utility choice (CUC) model is presented to determine the optimal maneuver of CVs to create proper gaps for MVs. The driver compliance rate is introduced to account for CHVs’ unwillingness to follow the instructions given by the CUC model. The lower-layer comprises a cooperative merging control (CMC) model that ensures safe and smooth merging execution for MVs. Longitudinal and lane changing models are developed for mainline vehicles to facilitate an efficient and safe merging process. Simulation results show that the performance benefits of the CUC model are marginal when the CHV compliance rate is relatively low. However, the performance improvement is significant at higher compliance rates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 50%). Furthermore, the CORMC model has the potential to increase merging and mainline throughput by over 75% at sufficiently high CAV penetration rates. Comparison of three control strategies shows that the APS algorithm plays an important role in the CORMC model. A comparison with the Simulation of Urban Mobility (SUMO) indicates that the CORMC model significantly mitigates the propagation of congestion waves across varying levels of CAV penetration and on-ramp flow rates.

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

Enhancing Cooperation of Vehicle Merging Control in Heavy Traffic Using Communication-Based Soft Actor-Critic Algorithm

A promising way to improve efficiency in highway on-ramp regions is to control connected and automated vehicles (CAVs) to pass the merging section sequentially. The primary objective of this paper is to incorporate the reinforcement learning (RL) technique into vehicle merging control to achieve global cooperation. A communication protocol among RL agents is integrated with the Soft Actor-Critic (CSAC) algorithm. The parallel SAC agents with a parameter-sharing structure cooperate to optimize the common reward function. It enables CAVs to know the actions of each other so that they proactively negotiate to adjust speeds. We designed a parsimonious state representation containing crucial merging information to speed up the RL training. The effects of the merging control strategy were investigated with the simulation model in scenarios with different CAV penetration rates and traffic flow compositions. Results showed that the CSAC-based merging strategy generated collision-free merging trajectories with a short travel time while guaranteeing traffic safety in various traffic conditions. Four baselines (including two rule-based and two RL-based merging strategies) were applied in the same scenarios for comparison. It was found that the proposed merging strategy took the safest merging behaviors (zero traffic conflicts) and reduced the most significant travel time (56.9%).

Multilane freeway merging control via trajectory optimization in a mixed traffic environment

AbstractMerging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.

A simulation study of cooperative and autonomous vehicles (CAV) considering courtesy, ethics, and fairness.

Autonomous vehicles (AV) can be programmed to act cooperatively. Previous research on cooperative and autonomous vehicles (CAV) suggests they can substantially improve traffic system operations in terms of mobility and safety. However, these studies do not explicitly take each vehicle's potential gain/loss into consideration and ignore their individual levels of willingness to cooperate. They do not account for ethics and fairness either. In this study, several cooperation/courtesy strategies are proposed to address the above issues. These strategies are grouped into two categories based on non-instrumental and instrumental principles. Non-instrumental strategies make courtesy/cooperation decisions based on some courtesy proxies and a user-specified courtesy level, while instrumental strategies are based only on courtesy proxies related to local traffic performance. Also, a new CAV behavior modeling framework is proposed based on our previous work on cooperative car-following and merging (CCM) control. With such a framework, the proposed courtesy strategies can be easily implemented. The proposed framework and courtesy strategies are coded in SUMO microscopic traffic simulator. They are evaluated considering different levels of traffic demand on a freeway corridor consisting of a work zone and three weaving areas of different types. Interesting findings are drawn from the simulation results, one of which is that the instrumental Local Utilitarianism strategy performs the best in terms of mobility, safety, and fairness. In the future, auction-based strategies can be considered to model how CAV make decisions.

A Collaborative Merging Method for Connected and Automated Vehicle Platoons in a Freeway Merging Area with Considerations for Safety and Efficiency.

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following-merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.

On-Ramp Merging Strategy with Two-Stage Optimization Based on Fully Proactive and Cooperative Merging of Vehicles

The on-ramp area has been a hot spot for research as a bottleneck area. In this paper, an on-ramp cooperative merging control strategy is proposed to make full use of active cooperative vehicles, and alleviate the negative influence of merging on the main road. First, a two-stage framework is proposed to illustrate the merging strategy. At the vehicle level, the cooperative merging controllers of vehicles are designed based on an optimization algorithm that considers the penalty function. At the traffic level, the cooperative distance of the upstream and downstream vehicles at the main road target merging position are optimized to improve merging efficiency. Meanwhile, to ensure the implementation of the proposed strategy in mixed traffic, we propose a method to determine the lead vehicles based on the proximity principle. Numerical experiments were performed to verify the fully proactive and cooperative merging (FPCM) strategy, the effectiveness of the vehicle cooperative controllers, the necessity of optimizing the cooperative distance, the effect of different velocities on the main road and ramp, and the effectiveness of determining the lead vehicles. In the case of multivehicle merging, the proposed strategy can improve merging efficiency by 44.42% and save fuel consumption by 39.11%. In the case of a single merging vehicle, the optimized proposed strategy can improve merging efficiency by 48.4% and save fuel consumption by 31.4%. Finally, the simulation experiments for the different maximum speeds of vehicles show that a sufficient speed adjustment range can improve the advantages of the strategy.