Connected Automated Vehicles Research Articles

Recent Studies in the Literature on Nonlinear Control of Platoons and Transitional Maneuvers

Vehicle Platoons consist of a leader vehicle and a group of connected automated vehicles (CAV) that follow the leader at a predetermined inter vehicle distance and move together at a certain common speed. This topic has been studied extensively in the literature by academicians in recent years. Therefore, a review of the literature on this topic is needed to understand which problems need to be solved and which issues need further study on vehicle platoons. For this reason, this study was conducted to reveal the latest situation on vehicle platoons with existing studies.

An optimal lane configuration management scheme for a mixed traffic freeway with connected vehicle platoons

With the emergent connectivity, both Connected Automated Vehicles (CAVs) and Connected Regular Vehicles (CRVs) have the capability to form platoons that can be defined as connected vehicle platoons, thereby enhancing the capacity of freeways. To fully leverage the innovations of CAVs and CRVs in improving traffic flow mobility, dedicated lanes with distinct vehicle type authority are proposed. Existing studies suggest that the mobility performance of a road section with different lane authority configurations is significantly influenced by the compatibility between lane configurations and traffic flow characteristics, such as penetration rate of connected vehicles (i.e. CAVs and CRVs) and arrival rate per lane. Motivated by the above research need, this paper proposes an optimal lane configuration management scheme to maximize discharge flow in a two-lane freeway mixed traffic environment. To begin with, four lane configuration strategies with different distinct vehicle type authorities are introduced. Analytical models are provided to mathematically derive the discharge flow of given four lane configuration strategies, according to the investigation of single-lane capacity and upstream arriving traffic demand. By configuration strategy-based analytical models, the optimal lane configuration management scheme is proposed. We then conduct numerical analysis to validate the effectiveness of the proposed analytical models of lane configuration strategies under various penetration rates of CAVs and CRVs. As the result indicated, several important factors have significant impacts on the optimal lane configuration management scheme, such as traffic demand and its allocations on each lane, and CAVs/CRVs penetration.

Safety-oriented dynamic speed harmonization of mixed traffic flow in nonrecurrent congestion

Freeway nonrecurrent congestion adversely affects collision risks, travel time, emissions, and fuel consumption. Optimizing mixed flow involving Human-Driven Vehicles (HDVs) and Connected Automated vehicles (CAVs) is expected to exist as a challenging issue for a long time prior to full adoption of CAVs. To address this, a safety-oriented Dynamic Speed Harmonization (DSH) strategy for mixed traffic flow in nonrecurrent congestion is proposed in this study. This research utilizes a Deep Reinforcement Learning (DRL) framework to investigate the effects of DSH on the measures of effectiveness in microscopic simulation. A holistic evaluation of HDVs and CAVs integrated with DSH highlights the synergies and trade-offs across different metrics. The results reveal that the implementation of DSH can further improve safety and enhance mobility with increasing CAV penetration rates. While special events may exacerbate congestion, their impact can be mitigated to some extent through speed controls. Spatiotemporal patterns of speed variations at the bottleneck demonstrate that the DRL controller has the capability to dampen oscillations and smoothen traffic flow. A series of sensitivity analyses also indicate the adaptability of the agent under adverse weather scenarios, and the differences of surrogate safety measurements in response to various parametric thresholds.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system, a convergence of traffic stream is anticipated in the foreseeable future, where both connected automated vehicle (CAV) and human driven vehicle (HDV) will coexist. In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system, we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference (FVD) model. Subsequently, adopting the linear stability theory, we propose the linear stability condition for the model through using the small perturbation method, and the validity of the heterogeneous model is verified by comparing with the FVD model. Through nonlinear theoretical analysis, we further derive the KdV-Burgers equation, which captures the propagation characteristics of traffic density waves. Finally, by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow, the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated. Subsequent analysis reveals emergent traffic phenomena. The experimental findings demonstrate that as CAV permeability increases, the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation, leading to enhanced stability of the traffic system. Furthermore, higher initial traffic density renders the traffic system more susceptible to congestion, resulting in local clustering effect and stop-and-go traffic phenomenon. Remarkably, the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing. Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow. This research contributes to providing theoretical guidance for future CAV applications, aiming to enhance urban road traffic efficiency and alleviate congestion.

Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

Guest Editorial Special Issue on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV)–Part I

Guest Editorial Special Issue on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV)–Part I

Ethics and Cooperative Optimization Scheme for CAVs with Velocity-Prioritized Emergency Vehicles at Signal-Free Intersections

This study investigates collaborative route mapping for a collection of connected automated vehicles (CAVs) traversing multi-lane pathways, with a particular emphasis on intersections without traffic signals. A distinctive feature of our research is the prioritization of emergency vehicles, such as ambulances and fire engines, according to their velocity demands. Additionally, a comprehensive analysis of the ethical ramifications of implementing such a prioritised system is engaged, while it is notable that in most of the existing studies, the ethical evaluation of intelligent transportation modalities is neglected. From a technical aspect, trajectories for CAVs in current models tend to be either inflexible or congruent with the collective formation of the fleet. Concurrently, the pre-set velocities for CAVs frequently demonstrate a lack of adaptability, which impedes cohesive operation. Moreover, there exists a pronounced deficiency in catering to the prioritization needs of emergency vehicles within these models. The current research endeavours to address these shortcomings by formulating the intersection management as an optimal control problem (OCP). This formulation enables CAVs to modulate their velocities in response to practical objectives, sidestepping fixed lane regulations at junctions. The control framework also encompasses autonomous vehicles of diverse sizes and velocities. An Adaptive Stepwise Optimization (ASO) methodology is proposed to augment the efficacy of the OCP resolution.

Eco-driving strategy for connected automated vehicles in mixed traffic flow

Mixed traffic flow is a prevalent phenomenon in the trend of connected automated vehicles (CAVs), where a diverse set of road users, including cars, motorcycles, bicycles, pedestrians, and even animals, share the road infrastructure. This coexistence poses a range of challenges, not limited to traffic safety, efficiency, and environmental sustainability. Compared with the traditional traffic streams, the controllability of connected and automated vehicles within mixed traffic offers new possibilities for eco-driving. As CAV technologies continue to flourish, this study explores the imperative of constructing eco-roads within a mixed traffic framework and optimizing eco- driving strategies to enhance vehicle energy efficiency and reduce emissions. We extended the concept of mixed traffic flow to incorporate scenarios involving animal crossings, introducing an eco-road-based green mixed traffic model. Analyzing the driving behaviors of both autonomous and manually-driven vehicles within a vehicular network ecosystem, we proposed an eco- road driving model that includes vehicle-following and lane-changing behaviors. From the perspective of dynamic programming, we conducted a discrete analysis to create an energy- saving driving model apt for mixed traffic conditions, with Q-Learning serving as the optimal solver. We further validated our theoretical framework through simulations conducted on eco- roads in Shanghai, taking into account the inherent risks brought about by the crossing of wildlife. Our empirical results indicated that the recommended energy-saving strategies could potentially reduce fuel consumption by 6–11%. Interestingly, the energy-saving effects are amplified with an increasing density of networked autonomous vehicles (CAVs) within the mixed traffic environment. Our findings strengthen the feasibility of our model and the efficacy of our algorithmic approach, confirming that the described driving strategies hold great promise for significantly improving energy efficiency in the domain of connected vehicles under the premise of ensuring the safety of driving and wildlife.

Analysis of the impact of cyberattacks on the lane changing behavior of connected automated vehicles

The characteristics of connected automated vehicles (CAVs) make them more susceptible to various cyberattacks, resulting in traffic congestion, delays, and even serious traffic accidents. In recent years, the safety issues of CAVs have become a hot research topic. The current research mostly focuses on studying and evaluating the impact of cyberattacks on the car-following behavior of single lane vehicles. However, lane changing behavior is also one of the most common traffic behaviors on the road, and compared to car-following behavior, lane changing is more complex, vehicles are more likely to collide with surrounding vehicles, and traffic accidents are more likely to occur. Therefore, it is necessary to explore the impact of cyberattacks on vehicles in lane changing environments. The purpose of this study is to investigate the effect of cyberattacks on the lane-changing behavior of two-lane vehicles and evaluate the security. First, this study establishes a framework for dynamics under cyberattacks by combining the intelligent driver model (IDM) and the minimizing overall braking induced by lane changes (MOBIL) model. Then, the traffic effects of different severity of attacks on the vehicle are simulated under three attack scenarios (bogus information, replay/delay and collusion cyberattacks), respectively. Finally, we use the conflict measures to analyze the collision risk of various cyberattacks, including time-to-collision (TTC), time-integrated time-to-collision (TIT) and the standard deviation (SD) of velocity. The simulation results indicate that different cyberattacks may cause changes in lane-changing time and refuse to change lanes. In addition, the CAV platoon of two lanes will lead to varying degrees of speed oscillations and collision risks. Among these, collusion attacks cause the most serious damage. These findings help to better understand the impact of cyberattacks on CAVs and lay the foundation for enhanced cybersecurity.

A new control strategy of CAVs platoon for mitigating traffic oscillation in a two-lane highway

With the development of autonomous driving technology, designing connected automated vehicles (CAVs) control strategies to mitigate traffic oscillations has become a hot topic. Most current control strategies are mainly oriented to single-lane highway scenarios and do not consider vehicles' lane-changing behaviors. To address the gap, this paper proposes the Follower-Stopper-Platoon (FSP) strategy, which attempts to mitigate traffic oscillation by controlling a platoon of CAVs in a two-lane scenario. Firstly, based on the Follower-Stopper (FS) control and considering CAVs' platoon behaviors, this paper proposes the FSP strategy and compares it with two comparative strategies, the Baseline and FS strategies. Then, a two-lane mixed traffic flow cellular automata model is developed and used to verify the effectiveness of the FSP strategy. Finally, this study demonstrates the efficacy of the FSP strategy by designing simulation experiments and analyzes the effect of CAVs' platoon size on the control effect. The result shows that (1) the FSP strategy can overcome the shortcomings of the FS control and will increase traffic flow speed while mitigating traffic oscillations. (2) In a two-lane scenario, as penetration rates of CAVs and traffic densities increase, the FSP strategy's advantages in mitigating traffic oscillations, reducing energy consumption and pollutant emissions, and improving speed and passenger comfort are gradually apparent. (3) Under the FSP strategy, the larger the maximum size of the CAVs platoon, the better the performance of the mixed traffic flow in terms of traffic efficiency, stability, fuel consumption, and pollutant emission.

Stability analysis and connected vehicles management for mixed traffic flow with platoons of connected automated vehicles

Recently, the market has witnessed the emergence of intelligent vehicles equipped with diverse functionalities. Among these are connected automated vehicles (CAVs) boasting a comprehensive platooning capability, and connected vehicles (CVs), which essentially refer to human-driven vehicles (HVs) equipped with connectivity features. As a result, CAVs, CVs, and regular human-driven vehicles (RVs) will form a mixed CAV-CV-RV flow to affect stability of existing traffic flow, which is an important characteristic of traffic flow operations. This paper investigates stability of this new mixed CAV-CV-RV flow, explores effect of CVs on stability, and proposes CV management strategy aimed at achieving a stable traffic flow. We firstly establish definition of the novel mixed flow comprising CAVs, CVs, and RVs. Subsequently, we present a general analytical approach for evaluating stability of this mixed flow. Then we undertake an empirical study that incorporates car-following models, validated using real experiments involving the aforementioned vehicle types. Based on findings from our stability analysis, we proceed to propose a CV management strategy designed to guarantee stable conditions within this mixed traffic flow. Results indicate the effectiveness of the proposed general analytical method in assessing influences of various system factors on stability of this new mixed flow. Subsequent to an application of the analytical method, empirical study uncovers a noteworthy finding that CVs prove to be more effective than CAVs in ensuring stable conditions within the mixed flow. Then we can manage market proportion of CVs in order to achieve stable mixed flow, according to varying CAV penetration rates. Specifically, some interesting lessons are learned based on the empirical study: (i) to guarantee stable operations of mixed traffic flow, about one third of CVs are needed to be among HVs, before CAVs running on road (CV-RV mixed flow); (ii) management strategy for stable flow requires an increase of minimum CV proportion among HVs from 33% to 55%, as CAV penetration rates increase up to 50%; (iii) when CAV penetration rates are greater than 50%, the required minimum CV proportion for ensuring traffic flow stability decreases with the increase of CAV penetration rates.

Connected and automated vehicle loading system for improving operational inefficiency from human driven vehicle in roll-on/roll-off port operations

ABSTRACT This study aims to identify the non-value-adding activities during vehicle stowage operations in automobile terminals and propose a connected automated vehicle (CAV) loading system, a self-driving-car-loading system. Furthermore, the productivity of the CAV loading system is compared with the current loading system. A simulation model of an actual loading system was developed using the software FlexSim. The simulation results showed that the walking time of workers, operation time of shuttle vans, and waiting time occupied a large part of the cycle time in the current operation system. The proposed CAV loading system has eliminated these inefficiencies, and increased productivity by 26.78%. This is the first study to (1) present a self-driving-car-loading system in a simulated automobile terminal of a real-world size, and (2) propose a CAV loading system. Results provide useful insights for the integration of self-driving technology into future automobile port operations.

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.

Modeling the Effect of the Platoon Size of CAVs on Mixed Traffic Flow: A Cellular Automaton Method

This study proposes a cellular automaton model incorporating the platoon size of connected automated vehicles (CAVs) to examine their impact on mixed traffic flow. First, vehicles are classified into three modes, human-driven vehicles (HDVs), adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC), by considering the characteristics of the car-following behavior. Second, the CACC is further subdivided into interplatoon and intraplatoon car-following modes due to the limitations of the platoon size of CAVs. Then, cellular automaton rules are developed for each of these four modes. Finally, numerical simulation experiments are conducted to analyze the influence of the penetration rate and platoon size of CAVs on mixed traffic flow. The results demonstrate that (1) the simulation results closely align with the theoretically derived outcomes, with an error rate of only 0.46% at a penetration rate of 100%; (2) when the penetration rate of CAVs reaches 100%, increasing the platoon size further enhances the traffic capacity; and (3) the optimal platoon size is determined to be seven CAVs under moderate traffic density.

Nash double Q-based multi-agent deep reinforcement learning for interactive merging strategy in mixed traffic

The interaction between ramp and mainline vehicles plays a crucial role in merging areas, especially in the mixed-traffic environment. The driving behaviours of human drivers are uncertain and diverse, and the uncertainty makes it more complex for connected automated vehicles (CAV) to plan trajectories and merge into the mainline. To overcome this problem, a interactive merging strategy based on multi-agent deep reinforcement learning (MADRL) is designed, enabling the ramp vehicle (CAV) to consider the dynamic reaction of mainline vehicles. There are two agents in our interactive strategy, one of which is to predict and analyse the behaviour of mainline vehicles (human-driven vehicles, HDV, or non-connected vehicles). The other is created for exploring optimal merging actions of ramp vehicles. Firstly, game theory is used to model the competitive behaviours between ramp and mainline vehicles, and the Nash equilibrium of joint actions guides the ramp vehicle to learn best response to the mainline vehicle. Secondly, the Nash double Q algorithm is developed to ensure the outputs of Q networks are trained to efficiently converge to the Nash equilibrium point. The trained Q networks are then used for online control. Finally, our strategy is compared with single RL and existing MADRL algorithms in real on-ramp scenarios. Simulations show our strategy to be successful in coordinating both vehicles via analysis of human drivers, resulting in improved driving performance in terms of global safety, efficiency, and comfort.

Effects of Connectivity and Automation on Saturation Headway and Capacity at Signalized Intersections

This paper analyzes the potential effects of connected and automated vehicles on saturation headway and capacity at signalized intersections. A signalized intersection is created in Vissim as a testbed, where four vehicle types are modeled and tested: (I) human-driven vehicles (HVs), (II) connected vehicles (CVs), (III) automated vehicles (AVs), and (IV) connected automated vehicles (CAVs). Various scenarios are defined based on different market-penetration rates of these four vehicle types. AVs are assumed to move more cautiously than HVs. CVs and CAVs are supposed to receive information about the future state of traffic lights and adjust their speeds to avoid stopping at the intersection. As a result, their movements are expected to be smoother with a lower number of stops. The effects of these vehicle types in mixed traffic are investigated in relation to saturation headway, capacity, travel time, delay, and queue length in different lane groups of an intersection. A Python script code developed by Vissim is used to provide the communication between the signal controller and CVs and CAVs to adjust their speeds accordingly. The results show that increasing CV and CAV market-penetration rate reduces saturation headway and consequently increases capacity at signalized intersections. On the other hand, increasing the AV market-penetration rate deteriorates traffic operations. Results also indicate that the highest increase (80%) and decrease (20%) in lane-group capacity are observed respectively in a traffic stream of 100% CAVs and 100% AVs.

Can the introduction of CAVs mitigate social dilemmas causing traffic jams on highways?

This study explored whether the introduction of connected automated vehicles (CAVs) into human-driven vehicles (HVs) can mitigate the social dilemma caused by defective HVs that seek to overtake other vehicles by making frequent lane changes. In this regard, a cellular automata traffic model based on the revised S–NFS model, dealing with a mixed flow field composed of CAVs and HVs with multiple lanes, was established. We perused precise game theoretical analysis on the basis of the result obtained from traffic flow analysis through multi-agent simulation (MAS). As a result, the social efficiency deficit, quantifying the extent of the social dilemma behind such a specific traffic flow system, elucidates that the introduction of CAVs does not eliminate the social dilemma caused by defective HVs unless CAVs account for the vast majority of the mixed flow.

Freeway capacity modeling and analysis for traffic mixed with human-driven and connected automated vehicles considering driver behavior characteristics

Future freeway traffic flow will include both Human-driven Vehicles (HV) and various sizes of Connected Automated Vehicles (CAV) platoons. When following CAV platoons, HV drivers may change the car-following distance for psychological reasons, which could further affect the road capacity under mixed traffic flow. In this paper, we constructed a capacity model that considered the spatial distribution characteristics of mixed traffic flow and the behavioral traits of HV drivers when they follow CAV platoons. We surveyed 331 HV drivers using a questionnaire. 51.1% of drivers opted to increase the car-following distance when following a CAV platoon, and the amount of change in distance increased in direct proportion to the size of the platoon. A Structural Equation Model (SEM) based on survey data was developed to examine the connection between psychological variables and HV drivers’ driving behaviors. These results of the questionnaire were introduced into the capacity model construction, and the trends of capacity with CAV penetration, maximum platoon size, platoon intensity, and time headways were revealed. The results show that previous studies overestimated the capacity under mixed traffic flow, especially when the maximum platoon size is equal to or greater than 6 or the CAV penetration is between [0.6, 0.8], the overestimation of the capacity is higher. CAV penetration, maximum platoon size, and platoon intensity are positively correlated with capacity, while time headway is negatively correlated with capacity. The capacity growth is not significant when CAV penetration is between (0, 0.4]. Six is the ideal size of CAV platoons under mixed traffic flow when considering the impact of CAV platoons on HV drivers’ driving behaviors. By taking measures to promote CAV aggregation and making platoon intensity greater than 0, the capacity will be improved more significantly. For a specific car-following type, a different value of time headway will lead to a different capacity, but the impact trends will be similar.

HYDRO-3D: Hybrid Object Detection and Tracking for Cooperative Perception Using 3D LiDAR

3D-LiDAR-based cooperative perception has been generating significant interest for its ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues that can be problematic for single-vehicle perception. Despite its effectiveness in overcoming various challenges, cooperative per-ception's performance can still be affected by the aforementioned issues when Connected Automated Vehicles (CAVs) operate at the edges of their sensing range. Our proposed approach called HYDRO-3D aims to improve object detection performance by explicitly incorporating historical object tracking information. Specifically, HYDRO-3D combines object detection features from a state-of-the-art object detection algorithm (V2X-ViT) with historical information from the object tracking algorithm to infer objects. Afterward, a novel spatial-temporal 3D neural network performing global and local manipulations of object-tracking historical data is applied to generate the feature map to enhance object detection. The proposed HYDRO-3D method is comprehensively evaluated on the state-of-the-art V2XSet. The qualitative and quantitative experiment results demonstrate that the HYDRO-3D can effectively utilize the object tracking information and achieve robust object detection performance. It outperforms the SOTA V2X-ViT by 3.7% in AP@0.7 of object detection for CAVs and can also be generalized to single-vehicle object detection with 4.5% improvement in AP@0.7.

Autonomous Vehicle-Loading System Simulation and Cost Model Analysis of Roll-On, Roll-Off Port Operations

The gradual commercialization of entirely autonomous vehicles is expected to bring numerous benefits, such as structural transformation in the industry. Specifically, in maritime transportation, automobile terminals that import and export finished autos are seen to transform their current loading system into a CAV (connected automated vehicle)-loading system to accommodate autonomous vehicles. In this study, the impact of introducing a CAV-loading system to a roll-on, roll-off (RORO) ports was investigated. Simulation models were developed to test the performance of the terminal with the CAV-loading system. Then, a cost model was developed to determine the economic benefits of the CAV-loading system. The results in this study revealed that operating costs were reduced by 90%, while terminal operations were significantly improved. In addition, the study revealed that using the CAV-loading system resulted in a 12% reduction in CO2 emissions compared to that using the current loading system. The originality of this study lies in its transformative potential for an industry that heavily relies on human labor and has limited mechanization and automation. This study provides significant implications for incorporating autonomous vehicles in planned automobile terminal operations.