Merging Vehicle Research Articles

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Advances in autonomy offer the potential for dramatic positive outcomes in a number of domains, yet enabling their safe deployment remains an open problem. This work's motivating question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The work formalizes this <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalable supervision</i> problem by considering remotely located human supervisors and investigating how autonomous agents can cooperate to achieve safety. This article focuses on the safety-critical context of autonomous vehicles (AVs) merging into traffic consisting of a mixture of AVs and human drivers. The analysis establishes high reliability upper bounds on human supervision requirements. It further shows that AV cooperation can improve supervision reliability by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">orders of magnitude</i> and counterintuitively requires fewer supervisors (per AV) as more AVs are adopted. These analytical results leverage queuing-theoretic analysis, order statistics, and a conservative, reachability-based approach. A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale. While this work focuses on AVs, the scalable supervision framework may be of independent interest to a broader array of autonomous control challenges.

Graphical Animations of an Autonomous Vehicle Merging Protocol

Graphical Animations of an Autonomous Vehicle Merging Protocol

A Game Lane Changing Model Considering Driver’s Risk Level in Ramp Merging Scenario

A ramp merging decision as an important part of the lane change model plays a crucial role in the efficiency and safety of the entire merging process. However, due to the inevitability of on-ramp merging, the limitations of the road environment, and the conflict between the merging vehicle and the following vehicle on the main road, it is difficult for human drivers to make optimal decisions in complex merging scenarios. First, based on the NGSIM dataset, a gain function is designed to represent the interaction between the ego vehicle (EV) and the surrounding vehicles, and the gain value is then used as one of the characteristic parameters. The K-means algorithm is employed to conduct a cluster analysis of the driving style under the condition of changing lanes. This paper models the interaction and conflict between the ego vehicle (vehicle merging) and the mainline lagging vehicle as a complete information non-cooperative game process. Further, various driving styles are coupled in the ramp decision model to mimic the different safety and travel efficiency preferences of human drivers. After EV decision-making, a quintic polynomial method with multi-constraints is proposed to implement merging trajectory planning. The proposed algorithm is tested and analyzed in an on-ramp scenario, and the results demonstrate that drivers with different driving styles can make correct decisions and complete the ramp merging. The changing trend of the speed and trajectory tests are also in line with the features of the driver’s driving style, offering a theoretical foundation for individualized on-ramp merging decisions.

Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks

Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic breakdown. Therefore, ramp meters have been used to regulate the traffic flow from the ramps to maintain stable traffic flow on the mainline. However, existing traffic breakdown prediction models do not consider on-ramp traffic flow. In this paper, an algorithm based on artificial neural networks (ANN) is developed to predict the probability of a traffic breakdown occurrence on freeway segments with merging traffic, considering temporal and spatial correlations of the traffic conditions from the location of interest, the ramp, and the upstream and downstream segments. The feature selection analysis reveals that the traffic condition of the ramps has a significant impact on the occurrence of traffic breakdown on the mainline. Therefore, the traffic flow characteristics of the on-ramp, along with other significant features, are used to build the ANN model. The proposed ANN algorithm can predict the occurrence of traffic breakdowns on freeway segments with merging traffic with an accuracy of 96%. Furthermore, the model has been deployed at a different location, which yields a predictive accuracy of 97%. In traffic operations, the high probability of the occurrence of a traffic breakdown can be used as a trigger for the ramp meters.

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%).

Evaluation of a Dynamic Lane Assignment on Urban Freeways by Using a Microscopic Traffic Flow Simulation

At a merging section of freeways, the mainline traffic flow conflicts with the traffic flow from an onramp, which often causes traffic congestion. To mitigate this conflict, it is important to dynamically manage the lane utilization on the mainline at bottleneck points. Recently, connected and automated vehicles (CAVs) are expected to be widely used on freeways. Guiding CAVs to change lane to avoid conflicts with merging vehicles may alleviate traffic congestion. In this paper, we propose a novel dynamic lane guidance system that can dynamically manage the lane changes in the merging sections with urban freeway ramps. In the proposed system, CAVs are guided to change lane at the lanes upstream of bottlenecks according to traffic conditions, which are detected by the following two methods: 1) cross-sectional vehicle speed information obtained from traffic detectors and 2) vehicle speed information obtained from driving trajectories based on probe data. Then, the effectiveness of the proposed dynamic lane guidance system is evaluated by a microscopic traffic simulator, Vissim, which is calibrated to reproduce the observed traffic states based on full vehicle trajectory data collected at the section near the Tsukamoto merge area on the Hanshin Expressway Route 11 Ikeda Line in Japan. As a result, the obtained results showed that dynamic lane guidance is effective in reducing overall travel time, thus demonstrating the usefulness of dynamic lane guidance. It was also found that traffic control based on partial vehicle information, such as probe data, is sufficiently effective.

A novel merging strategy model considering the remaining distance in the acceleration lane

AbstractThe 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.

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.

Safe autonomous lane changes and impact on traffic flow in a connected vehicle environment

Despite the recent advancements in autonomous vehicle technologies, performing safe lane changes and merging in dense traffic environments remains an open challenge. One important question is how to find a space to merge into without placing any vehicle in a collision-prone situation. Moreover, what is the traffic flow impact of doing so? To tackle this issue, we start by adopting a safety definition based on a worst-case braking scenario. We then propose a decentralized controller-agnostic cooperative approach for lane changes that relies only on Vehicle-to-Vehicle communications and guarantees safety. In our method, the merging vehicle operates as having two possible leaders, one in its own lane and one in the destination lane, till the lane change maneuver is completed. Furthermore, the future following vehicle in the destination lane cooperates by acting as if the merging vehicle has already changed lanes. The merging vehicle only performs the lane change maneuver when the gaps are safe. Vehicle-level simulations are used to evaluate the approach in detail for a single lane change maneuver. Then, the method’s impact on highway safety and traffic flow is assessed using microscopic traffic simulations based on the commercial software VISSIM. We simulate three types of vehicles: adaptive cruise control equipped vehicles, autonomous vehicles, and connected and autonomous vehicles. The various simulations allow us to identify which improvements are brought by cooperative lane changes. Results indicate that connected and autonomous vehicles can achieve almost zero collision risk in congested and uncongested scenarios while improving traffic flow by 26% in the congested case. However, energy consumption increases significantly in the uncongested scenario. Moreover, our method outperforms an existing cooperative lane-changing method regarding safety and traffic flow. Our simulations with mixed traffic suggest that the proposed method is advantageous only at high penetrations of autonomous vehicles or in congested situations.

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.

Deep Reinforcement Learning With NMPC Assistance Nash Switching for Urban Autonomous Driving

Deep Deterministic Policy Gradient (DDPG) is a promising reinforcement learning technique with the potential to resolve complicated tasks and handle high-dimensional state/action spaces. However, it suffers from sample inefficiency, requiring a high number of training samples. To speed up the training, we propose -annealing and Q-learning switching methods to aid the training of DDPG with Nonlinear Model Predictive Control (NMPC) controller to solve priority calculation and merging of autonomous vehicles at roundabouts. We further expand the Q-learning switch with double replay memory and Nash Q-value updates. The performance of these switching methods are compared to DDPG and demonstrate that Nash switch outperforms other methods. To reduce conservativeness, we test training using variable traffic density. We test three selection methods inside Q-learning and show constant threshold switch has at least ten times higher mean reward for 50 episodes training. We also compare Q-learning with NMPC and PID assistance and show that NMPC has 114% higher mean reward. We compare Q-learning switch and novel Nash switch method under noise-free and noisy input conditions to prove an increase of 35% mean reward and decrease of 4% std for Nash updates. We analyze efficacy of Q-learning and Nash switch approaches w.r.t NMPC and demonstrate comparable performance between Nash switch and NMPC. We juxtapose driving results of switch Q-learning and Nash switch with DDPG algorithm to prove Nash switch strategy has higher overall performance. Finally, we compare Nash switch's performance with DDPG for highway merging scenario which shows 159% higher mean reward.

Spring–Mass–Damper-Based Platooning Logic for Automated Vehicles

This paper applies a classical physics-based model to control platooning autonomous vehicles (AVs) in a commercial traffic simulation software. In the spring–mass–damper (SMD) model, each vehicle is assumed as a mass coupled with its preceding vehicle with a spring and a damper: the spring constant and damper coefficient control spacing and speed adoption between vehicles. Limitations on the platooning-oriented communication range and number of vehicles in each platoon are applied to the model to reflect real-world circumstances and avoid overlengthened platoons. The SMD model controls both intra-platoon and inter-platoon interactions. Initial evaluation of the model reveals that it does not cause a negative spacing error between AVs in a harsh deceleration scenario, guaranteeing safety. Besides that, the SMD model produces a smaller positive average spacing error than the VISSIM built-in platooning module, which prevents maximum throughput drop. The simulation result for a regular highway section reveals that the proposed platooning algorithm increases the maximum throughput by 10%, 29%, and 63% under 10%, 50%, and full market penetration rate (MPR) of AVs, respectively, with a 0.5 s response time. A merging section with different volume combinations on the main section and merging section and different MPRs of AVs is also modeled to test inter-platoon spacing policy effectiveness in accommodating merging vehicles. Travel time reductions of 20% and 4% are gained under a low MPR of AVs on the main lane and merging lane, respectively. Meanwhile, a more noticeable travel time reduction is observed in both main lane and merging lanes and under all volume combinations in higher AV MPRs.

Expected Safety Performance of Different Freeway Merging Strategies in an Environment of Mixed Vehicle Technologies

This study evaluates different proposed merging solutions that reduce the conflict between merging vehicles and mainline traffic within a mixed traffic environment using a safety measure to see which strategy might work better than others under specific traffic conditions. The mixed traffic includes various percentages of driver-operated vehicles (DVs) and connected autonomous vehicles (CAVs). The probability of noncompliance (PNC) is selected as a surrogate safety measure to assess the strategies. A MATLAB program is developed to simulate various traffic conditions at a merging area and to calculate the PNC merging for the different merging strategies. In addition, to examine the relationship between PNC and collision frequency at the merging area, the collision data at 15 merging ramps in Ottawa were collected to examine the relationship between PNC values obtained from the simulation for the case of a full-DV vehicle fleet and no management strategy (current conditions) and actual safety performance. The results confirmed the validity of PNC as a surrogate safety measure that is correlated to expected collision frequency at merge areas. By simulating all proposed merging management strategies, the results of this study showed a general trend of decreasing PNC and, hence, improved safety performance since the CAV penetration rate increases even when no management strategy is used or under the do-nothing option. However, most merging strategies had better expected safety performance than the do-nothing option, which indicates the value of implementing a merging management strategy, especially during the period of transition from a full-DV to a full-CAV fleet.

Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

Conflict Analysis for Cooperative Maneuvering With Status and Intent Sharing via V2X Communication

In this paper, we establish the concept of conflict analysis and demonstrate its applicability to aid the decision making of vehicles at different levels of automation and cooperation. In particular, we assume that the participating vehicles are equipped with vehicle-to-everything (V2X) communication and study cooperative maneuvers under status sharing and intent sharing. Conflict-free maneuvering strategies are developed and communication requirements for such strategies are determined. To demonstrate the developed framework, we investigate a scenario with an ego vehicle merging to a main road while another vehicle is approaching on that road. The results are demonstrated by both experimental data using real vehicles on a test track and simulations based on real highway data.

The Impact of Three Specific Collaborative Merging Strategies on Traffic Flow

On-ramps are considered to be one of the common traffic bottlenecks. In order to improve the operation efficiency of on-ramps, scholars worldwide have proposed various vehicle merging strategies. In this study, we designed different rules to express three collaborative strategies and studied their impact on on-ramp systems. Cellular automata models were used to simulate the systems under different situations, and the average speed and traffic flow rate of both the main roads and ramps were analyzed. The results show that (1) all the three merging strategies give excessive “priority” to the merging vehicle, leading to a severe reduction in the traffic performance of the main road; (2) nevertheless, these strategies have different effects on the entire system with a one-lane or two-lane main road. Due to the lane-changing behavior, the system with a two-lane main road has more advantages than that featured with a one-lane road, making the former system performing better than the latter under the same strategies; (3) the vehicles on the ramp and main road affect each other, and as the vehicle entering probabilities become large, the traffic flow rate on the main road decreases whereas that on the ramp increases. However, the effect is not unlimited, the flow rate on both roads finally reaches a stable level (forming a “platform”); and (4) large values of the merging safety distance parameter decrease the flow rate of the entire system. All the previous results provide a deep understanding of the impact of the three merging strategies on traffic flow, contributing to the design of on-ramp systems that have better operation efficiency and low levels of congestion.

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning.

The time that a vehicle merges in a lane reduction can significantly affect passengers' safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology.

A Game-Theory-Based Approach to Modeling Lane-Changing Interactions on Highway On-Ramps: Considering the Bounded Rationality of Drivers

In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy.

Adaptive boundary observer for ARZ traffic flow model with domain and boundary uncertainties

This paper studies the problem of the adaptive boundary observer design for the Aw-Rascle-Zhang (ARZ) traffic flow model, which is subject to both relaxation time uncertainty in the domain and boundary input disturbance. The boundary input disturbance comes from merging vehicles’ velocities at the downstream on-ramp, and we scale the disturbance by a low-pass filter based on the ordinary differential equation (ODE). Then, the ARZ model with the domain uncertainty and boundary input disturbance can be linearized to a coupled ODE-PDE system. Based on the swapping transformation, an adaptive boundary observer with least-squares type parameter estimation is designed to estimate the traffic states, the domain uncertainty, and the boundary input disturbance, simultaneously. The exponential convergence conditions w.r.t. the observer feedback gains are given by employing the Lyapunov technique. Finally, the simulation results are presented to illustrate the effectiveness of the designed adaptive boundary observer.

Optimal Merging Control of an Autonomous Vehicle in Mixed Traffic: an Optimal Index Policy

We consider the problem of a single Autonomous Vehicle (AV) merging into traffic consisting only of Human Driven Vehicles (HDVs) with the goal of minimizing both the travel time and energy consumption of the entire group of vehicles involved in the merging process. This is done by controlling only the AV and determining both the optimal merging sequence and the optimal AV trajectory associated with it. We derive an optimal index policy which prescribes the merging position of the AV within the group of HDVs. We also specify conditions under which the optimal index corresponds to the AV merging before all HDVs or after all HDVs, in which case no interaction of the AV with the HDVs is required. Simulation results are included to validate the optimal index policy and demonstrate cases where optimal merging can be achieved without requiring any explicit assumptions regarding human driving behavior.