Connected And Automated Vehicles Research Articles

Trustworthiness Assessment for Crowdsourcing-Based Citywide Parking Availability Sensing via Connected and Automated Vehicles

Real-time status acquisition of parking spaces is highly valuable for an intelligent urban parking system. Crowdsourcing-based parking availability sensing via connected and automated vehicles (CAVs) provides a feasible method with the advantages of high coverage and low cost. However, data trust issues arise from incorrect detection and incomplete information. This paper proposes a trustworthiness assessment method for crowdsourced CAV data considering different impact factors, such as the distance between the CAV and the target parking space, line abrasion, scene complexity, and image sharpness. The crowdsourced CAV data are collected through extensive field experiments and PreScan simulations. The classical line detection algorithm of VPS-Net and the target detection algorithm of YOLO-v3 are applied to detect on-street parking availability. A failure probability model based on the XGBoost algorithm is then developed to establish the relationship between data trustworthiness and different impact factors. The results show that the proposed model has an average accuracy of 78.29% and can effectively assess the degrees of external influences on the trustworthiness of the crowdsourced data. This paper provides a new tool to identify the data quality and improve the sensing accuracy for a crowdsourcing-based parking availability information system.

Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation.

Cybersecurity regulatory challenges for connected and automated vehicles – State-of-the-art and future directions

The technological advancements of Connected and Automated Vehicles (CAVs) are outpacing the current regulatory regime, potentially resulting in a disconnect between legislators, technology, and CAV stakeholders. Although many studies explore the regulatory requirements of operations of CAVs, studies on regulatory challenges specific to the cybersecurity of CAVs are also emerging and receiving lots of attention among researchers and practitioners. However, studies providing an up-to-date synthesis and analysis on CAVs regulatory requirements specific to cyber-risk reduction or mitigation are almost non-existent in the literature. This study aims to overcome this limitation by presenting a comprehensive overview of the role of key Intelligent Transportation Systems (ITS) stakeholders in CAV's cybersecurity. These stakeholders include road operators, service providers, automakers, consumers, repairers, and the general public.The outcome of this review is an in-depth synthesis of CAV-based ITS stakeholders by visualising their scope in developing a Cybersecurity Regulatory Framework (CRF). The study demonstrated the compliance requirements for ITS communication service providers, regulatory standards for CAVs automakers, policy readiness for CAVs customers and the general public who interact with CAVs, and the role of the CAVs Network Operator Centre in regulating CAVs data flow. Moreover, the study illuminates several critical pathways necessary in future for synthesizing and forecasting the legal landscape of CAV-based transportation systems to integrate the regulatory framework for CAV stakeholders. The paper's findings and conclusions would assist policymakers in developing a comprehensive CRF.

Road Asset Management and the Vehicles of the Future: An Overview, Opportunities, and Challenges

Connected and automated vehicles (CAVs) have the potential to significantly impact transportation systems in terms of mobility, the environment, safety, and the economy. These vehicles rely on a range of sensors and cameras to detect road signs and lane markings, as well as to scan their surroundings, and they are connected to other vehicles and infrastructures. Previous research has highlighted the need for transport asset management processes to manage the intrinsic aspects of CAVs more effectively, with a view to improving performance, resource utilization, and budget planning. However, little literature was found with a deep discussion of how CAVs will impact asset management. In this article we offer an initial discussion on the impacts of CAVs on road asset management. To do so, we first provide a short introduction to CAVs, followed by an overview of road asset management. We then comprehensively discuss many asset management aspects that are affected by CAVs. Finally, future research opportunities, challenges, and important subjects are outlined.

Infrastructure sensor-based cooperative perception for early stage connected and automated vehicle deployment

Infrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results.

Evaluation of fuel consumption and emissions benefits of connected and automated vehicles in mixed traffic flow

Introduction: The presence of connected and automated vehicles (CAV) in mixed traffic flows with different market penetration rates (MPRs) in urban road scenarios has a significant effect on fuel consumption and exhaust emissions.Methods: Therefore, in this study, real-world road networks and traffic data are simulated using SUMO based on actual data from a survey. The fuel consumption and emission benefits of CAVs in mixed traffic flows are well-evaluated, and the energy-saving performance of CAVs under low-speed vehicle interference is tested. In addition, this study explores both the energy consumption and emissions of purely electric vehicles.Results: The results show that with 100% CAV penetration, fuel vehicles have a maximum reduction in fuel consumption of 18% and a maximum increase in average speed of 31.6%, while the energy consumption of electric vehicles increases due to communication, detection, and collaboration between CAVs.Discussion: However, the results clearly demonstrate that the carbon emissions of electric vehicles are significantly lower than fuel vehicles. In addition, the increase in low-speed vehicles will result in an increase in energy consumption and emissions. Therefore, increasing the percentage of electric vehicles on the roads and transitioning from manual to autonomous driving systems is crucial to curbing carbon emissions.

Evaluation of BLE beacon technology for time critical I2V communication to support CAV deployment on urban roadways

Road traffic accidents are on the rise. Most accidents are due to factors such as driver error and failure to obey traffic signs. Connected and automated vehicles (CAVs) are expected to reduce accidents and improve road safety by navigating safer and faster than traditional cars. Vehicle-to-everything (V2X) communication, which includes infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication, can support the deployment of CAVs and enhance road safety for both human-only-driven and automated vehicles. Bluetooth Low Energy (BLE) beacons are a potential technology for I2V communication due to their low cost, compact size, low power consumption, mass compatibility with modern devices, and relatively large range. This study evaluated the potential of using BLE beacons as roadside units (RSUs) attached to traffic signs to deliver time-critical information to vehicles in urban road settings. To this end, the study utilized a CAV development platform to determine whether the vehicle was able to receive the beacon message far enough to take appropriate action while driving at the road's speed limit, taking into consideration road geometry and accounting for both dry and wet road conditions. In addition, the study tested several BLE beacon configurations to determine optimal configurations that achieved the required distance at all signs. The results of this study demonstrated that BLE beacons have the potential to be used in time sensitive I2V communications on urban roads. In addition, the optimal beacon configurations for the signs to be safely detected by CAVs were identified.

Mass‐based omni‐directional risk indicator (MORI) for multi‐participant traffic

AbstractThis study focuses on the potential of connected and automated vehicles (CAVs) to enhance road traffic safety through the provision of rich physical motion state information. Real‐time risk indicators are crucial for improving driving safety and must be tailored to the specific characteristics of the CAV environment. To this end, this paper introduces the concept of “risk pair” to decompose the joint actions of multiple traffic participants into pairwise interactions. An omnidirectional risk indicator (ORI) is developed to describe the effect of “risk pairs”, and the superposition of ORI based on quality is proposed as the mass‐based ORI (MORI). A comparison between MORI and existing risk indicators shows that MORI has good performance in quantifying one‐dimensional scenarios. In two‐dimensional scenarios involving multiple participating entities, MORI provides two quantitative results: vector summation and scalar summation. The trajectory data from the next generation simulation database is used to validate the MORI model. The results show that both vector summation and scalar summation of MORI demonstrate strong risk quantification capabilities with different directions. Although some limitations of MORI still exist, this approach will provide a scientifically effective means of risk quantification for two‐dimensional complex scenarios.

Coordinated traffic control of urban networks with dynamic entrance holding for mixed CAV traffic

Traffic congestion has been regarded as one of the most challenging problems in the urban transportation systems. The problem becomes trickier at traffic bottlenecks where the heavy traffic flow spills over and over-saturation appears, and the coordination of traffic control strategies in a network scale is required. With the information availability and control precision enabled by connected and automated vehicle (CAV) technologies, this study proposes a network-level traffic coordination method with dynamic entrance holding in a mixed CAV traffic, and establishes a two-layer framework to develop traffic control strategies to handle the over-saturation problem. In the upper layer, the coordination of signal control among intersections is optimized to minimize travel cost within the network. The upper bound of travel delays under the control strategy is theoretically derived and the critical traffic volume is specified for designing the entrance holding strategy. To accelerate the solving process, a two-stage solution method along with a series of valid inequations are developed, and the feasibility and optimality of the solution are investigated. In the lower layer, a dynamic entrance holding strategy is put forward to regulate the entering traffic at network perimeters to avoid over-saturation and improve traffic orderliness. To enhance the holding effectiveness, a traffic prediction method is created to capture the future traffic evolution based on the real-time information, and combined with the flow reservation mechanism to proactively adjust the holding strategy in real time. Simulation experiments are conducted to evaluate the performance of the proposed methodologies, and compare with other control strategies. The results show that the coordinated method could substantially improve traffic mobility within the network, especially in high traffic demands.

Determination of the optimal connected and automated vehicles platoon size based on the merging success rate

ABSTRACT The platoon size is a critical parameter in connected and automated vehicle (CAV) platoon configuration. However, the optimal platoon size configuration for the mixed human-driven vehicles (HVs) and CAVs traffic has not been well-studied, especially in merging areas. This paper aims to determine the optimal platoon size in the merge area through numerical analysis. Specifically, the existing merge probability calculation model is improved considering the impact of mainline random platoon size on on-ramp vehicles. In the improved model, the CAV platoon is treated as a vehicle with different lengths, and the headway between the platoon and single vehicle and the headway between the platoons are related to the maximum platoon size and CAV penetration rate. Three key parameters, namely the CAV penetration rate, acceleration lane length, and mainline traffic volume, were combined at various values. Based on this input, the relationship between the success rate and the platoon size was analyzed. The numerical analysis results indicate that: (1) when the CAV penetration rate is 10% ~30%, the merge success rate increases and stabilizes as the platoon size increases, and the optimal platoon size is 4 ~ 6. (2) When the CAV penetration rate is 40% ~70%, the trend of merge success rate varies with increasing platoon size under different mainline traffic volumes and acceleration lane lengths. Under the situation with lower traffic and longer acceleration length, the merge success rate tends to decline easier with larger platoon sizes, with 3 ~ 8 being the optimal platoon size. (3) When the CAV penetration rate is 80% ~90%, as the platoon size increases, the merging success rate tends to increase to the highest point and decrease. The optimal platoon size is 3 ~ 5. Finally, the validity of the theoretical model is confirmed through simulation experiments, and its limitations are discussed.

Reservation-Based Cooperative Ecodriving Model for Mixed Autonomous and Manual Vehicles at Intersections

Oversaturation has become a serious issue for urban intersections worldwide due to the rapid increase in population and traffic demands. The emergence of connected and automated vehicle (CAV) technologies demonstrates the potential to improve oversaturated arterial traffic. Integrating vehicular control and intersection controller optimization into a single process based on CAV technologies can optimize the performance of mixed traffic flow scenarios with various levels of CAV market penetration. This paper proposes an efficient reservation-based cooperative ecodriving model (RCEM) for an isolated intersection under partial and complete CAV market penetration, which can simultaneously optimize the CAV trajectories and intersection controller. CAVs are utilized to precluster manual vehicles into a platoon to improve vehicle passage efficiency. Then, a heuristic-based algorithm is developed to effectively obtain an optimal solution. The proposed RCEM scheme is compared with fixed signal control and actuated signal control in a Simulation of Urban MObility (SUMO)-based platform. Experimental results prove that the RCEM scheme outperforms the fixed signal control and actuated signal control in terms of stop delay, fuel consumption, and emissions under the condition of low levels of CAV penetration. Sensitivity analysis indicates that the system performance further improves as the CAV penetration rate increases, and the stop delay is almost eliminated when the CAV market penetration reaches 100%. Furthermore, the vehicle delay fluctuation under left-turning rates ranging from 5%-75% is 4.4 sec, which is far better than the vehicle delay fluctuation of the fixed signal control (176 sec) and actuated control (65.6 sec).

Impacts of Connected and Automated Vehicles on Travel Demand and Emissions in California

The emerging connected and automated vehicle (CAV) technology is likely to bring significant changes to the transportation world. This study helps understand how the anticipated emergence of CAVs will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. We design a set of future system configurations under the California Statewide Travel Demand Model framework to simulate scenarios for the deployment of passenger CAVs in California by 2050. These scenarios consider the ownership and operational type (e.g., private and shared) of CAVs, as well as additional policies such as pricing and the use of zero-emission vehicles (ZEVs) to curb potential impacts. The scenarios are: 0. Baseline (no automation); 1. Private CAV; 2. Private CAV + Pricing; 3. Private CAV + ZEV; 4. Shared CAV; 5. Shared CAV + Pricing; 6. Shared CAV + ZEV. Impacts of the introduction of CAVs across the transportation system are quantitatively estimated. The results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled (VMT) and emissions will likely increase, because of the relative convenience of CAVs. The results also show that VMT could be substantially affected by a modification in auto travel costs. This means that the implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help reduce tailpipe pollutant emissions in future scenarios.

Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

Cooperative control of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic, where human-driven vehicles with unknown dynamics coexist, data-driven predictive control techniques allow for CAV safe and optimal control with measurable traffic data. However, the centralized control setting in most existing strategies limits their scalability for large-scale mixed traffic flow. To address this problem, this paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) formulation and its distributed implementation algorithm. In cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple subsystems with one single CAV, which collects local trajectory data for subsystem behavior predictions based on the Willems’ fundamental lemma. Meanwhile, the cross-subsystem interaction is formulated as a coupling constraint. Then, we employ the Alternating Direction Method of Multipliers (ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation. Our simulations on different traffic scales verify the real-time wave-dampening potential of distributed DeeP-LCC, which can reduce fuel consumption by over 31.84% in a large-scale traffic system of 100 vehicles with only 5%–20% CAVs.

Principles for External Human–Machine Interfaces

Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) have been shown to have major benefits regarding the trust and acceptance of CAVs in multiple studies. However, a harmonization of eHMI signals seems to be necessary since the developed signals are extremely varied and sometimes even contradict each other. Therefore, the present paper proposes guidelines for designing eHMI signals, taking into account important factors such as how and in which situations a CAV needs to communicate with ORU. The authors propose 17 heuristics, the so-called eHMI-principles, as requirements for the safe and efficient use of eHMIs in a systematic and application-oriented manner.

Simulation Analysis of Capacity Evaluation of Bus Stops under Connected and Automated Vehicles Environment

The application of connected and automated vehicles (CAVs) technology has changed the operation characteristics of vehicles. Investigating the traffic capacity of bus stops under a CAVs environment can allocate traffic flow more reasonably, which is effective in alleviating traffic congestion. Therefore, this paper proposes a method that can be used to evaluate the traffic capacity of bus stops under a CAVs environment. First, two evaluation indexes, failure duration time (FD) and forced lane-changing rate (FLR) are proposed. Second, the simulation scheme with ten scenarios is determined, and simulation experiments are conducted. Then, the relationships between FD, FLR, and traffic flow under different penetration rates of CAVs are analyzed. Finally, the relationship models between FD, FLR, and traffic capacity are fitted to verify their validity for traffic capacity analysis. Additionally, a predictive model is proposed for estimating capacity under a CAVs environment using indicators from HV traffic flow. Results indicate that: (i) FD and FLR both positively correlate with capacity, and perform well in capacity evaluation of bus stops; (ii) FD and FLR can be utilized to predict the capacity under a CAVs environment; (iii) the higher the penetration rate of CAVs, the smaller the impact of the bus failure phenomenon and forced lane change on traffic flow.

Infrastructure‐related challenges in implementing connected and automated vehicles on urban roads: Insights from experts and stakeholders

AbstractThe introduction of connected and automated vehicles (CAVs) has the potential to bring numerous advantages to urban mobility. However, many challenges for road infrastructure need to be overcome before those benefits can be achieved. This study addressed multiple dimensions of the implications of CAV deployment for road infrastructure through a comprehensive survey with 168 experts from different sectors and regions around the world. The issues are grouped into five categories: (1) key challenges of accommodating CAVs in existing urban transport networks; (2) infrastructure improvement required for shared CAV models; (3) maintenance aspects of infrastructure for CAVs; (4) implementation time of infrastructure support for CAVs; and (5) financing infrastructure upgrades to facilitate CAVs on the roads. The outcomes of the research show that there is still no consensus among the stakeholders on what should be considered to maximise CAV benefits for society as a whole. This indicates the necessity for cooperation between stakeholders to achieve the safe and efficient operation of CAVs. Overall, this study provides in‐depth insights for decision‐makers and transport planners to form policies, regulations, and guidelines regarding the future implementation of CAVs for roads before their commercialisation phase.

Incentives to Encourage the Adoption of Connected and Automated Vehicles: Lessons Learned from Hybrid-Electric Vehicle Incentive Programs

Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types.

An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals

With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the car-following model for CAVs, safety is usually the basic constraint. Safety-oriented car-following models seek to specify a safe following distance that can guarantee safety if the preceding vehicle were to brake hard suddenly. The discrete signals of CAVs bring a series of phenomena, including discrete decision-making, phase difference, and discretely distributed communication delay. The influences of these phenomena on the car-following safety of CAVs are rarely considered in the literature. This paper proposes an efficient safety-oriented car-following model for CAVs considering the impact of discrete signals. The safety constraints during both normal driving and a sudden hard brake are incorporated into one integrated model to eliminate possible collisions during the whole driving process. The mechanical delay information of the preceding vehicle is used to improve car-following efficiency. Four modules are designed to enhance driving comfort and string stability in case of heavy packet losses. Simulations of a platoon with diversified vehicle types demonstrate the safety, efficiency, and string stability of the proposed model. Tests with different packet loss rates imply that the model could guarantee safety and driving comfort in even poor communication environments.

Driver Digital Twin for Online Prediction of Personalized Lane-Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM.

Cooperative Vehicles versus Non-Cooperative Traffic Light: Safe and Efficient Passing

Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself is not cooperative. Information about indented trajectories such as decisions on how and when to accelerate, decelerate and stop, is communicated among the vehicles involved. We provide an optimization-based procedure for efficient and safe passing of traffic lights (or other temporary road blockage) using vehicle-to-vehicle communication (V2V). We locally optimize objectives that promote efficiency such as less deceleration and larger minimum velocity, while maintaining safety in terms of no collisions. The procedure is computationally efficient as it mainly involves a gradient decent algorithm for one single parameter.