Connected Automated Vehicles Research Articles

Lane-Changing Strategy Based on a Novel Sliding Mode Control Approach for Connected Automated Vehicles

Safe and efficient autonomous lane changing is a key step of connected automated vehicles (CAVs), which can greatly reduce the traffic accident rate and relieve the traffic pressure. Aiming at the requirements of the smoothness and efficiency of the lane-changing trajectory of CAVs, it is necessary to design the lane changing controller to integrate the sensing, decision-making, and control tasks in the driving process. Firstly, based on the vehicle dynamics model, this paper proposes a vehicle lane-changing control strategy based on NNTSMC method (neural network enhanced non-singular fast terminal sliding mode control). The designed lane-changing controller can well realize the designed path tracking, and both lateral position and yaw angle can well track the expected value. This method enables the vehicle to control the front wheel steering angle intelligently, and the lateral acceleration during steering changes in the small scope, which ensures the steering stability of the vehicle. In this study, an improved adaptive RBF neural network with bounded mapping is designed to estimate the upper bound of the total disturbance of the system, which effectively reduces the chattering phenomenon of the control force. The Lyapunov function constructed in this study proves that the designed controller can ensure the stability of the controlled system. Finally, a comparative experiment is performed by the MATLAB/Simulink-CarSim co-simulation. Compared with SMC and TSMC (non-singular fast terminal sliding mode control), the proposed method has a performance improvement of at least 58.0% and 34.1%, respectively. The effectiveness and superiority of the proposed control method were confirmed by the experiments on the co-simulation platform.

Connected automated vehicle trajectory optimization along signalized arterial: A decentralized approach under mixed traffic environment

Trajectory optimization, as a key connected automated vehicles (CAVs) operation task, has the potential to mitigate traffic congestion, lower energy consumption, and increase the efficiency of traffic operation. This study proposes a decentralized approach to optimization CAV trajectories in both longitudinal and lateral dimensions along a signalized arterial under the mixed traffic environment, where human vehicles (HVs) and CAVs co-exist. More specifically, a 2-stage model is developed to optimize CAV trajectories based on traffic signal plans of downstream intersections and trajectory information of surrounding vehicles. The stage-1 is formulated to provide a rough estimate of the minimal travel time required for a single CAV traveling along this arterial with minimum stops. The stage-2 model is then designed to optimize the longitudinal and lateral behavior of CAVs with the objective of minimizing delay and lane-changing costs. This model is solved by a dynamic programming algorithm to satisfy the real-time optimization needs. A rolling horizon approach is adapted to dynamically implement the proposed model in light of changing traffic conditions. Numerical experiments have been conducted on a real-world arterial to evaluate the model performances. By comparing the optimized trajectories to the no optimization benchmark, the proposed model can reduce average stop delays of CAVs. Moreover, it can also reduce the stop delays of HVs and mixed traffic.

Guest editorial: Decision making and control for connected and automated vehicles

Guest editorial: Decision making and control for connected and automated vehicles

Camera-Sensing-Assisted Fast mmWave Beam Tracking for Connected Automated Vehicles

The millimeter-wave (mmWave) technology can support the wide-band communication requirements for the environment raw perception information sharing to guarantee the safety of connected automated vehicles (CAVs). But, how to achieve the fast and robust mmWave beam tracking is a challenging problem in a high mobility CAVs scenario. Therefore, this article proposes a camera-sensing-assisted joint offline and online beam tracking (CS-JBT) algorithm in the mmWave frequency band. First, the mobility model is designed to predict the next moment of CAV and reduce the beam searching space overhead. In the offline learning phase of the proposed CS-JBT algorithm, the optimal beam pair under the current mobility state is obtained to guarantee the timeliness of the beam tracking process. Furthermore, to solve the mobility state deviation problem caused by the camera sensing error, the online learning phase is designed to achieve the optimal beam tracking performance efficiently for the practical scenario. Simulation and hardware testbed results verify that the proposed CS-JBT algorithm can minimize the beam tracking latency from 500 to 20 ms in contrast to the existing VBC-PF algorithm, while achieving a stable throughput over 2.5 Gb/s in the 28-GHz mmWave frequency band.

Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

AbstractVehicular trajectory data collected by connected automated vehicles (CAVs) is minimal due to the low penetration rates (PRs) of CAVs, and fail to capture the characteristics of traffic flow. This study proposes a fully sampled trajectory reconstruction method for mixed traffic flow with regular vehicles (RVs), connected vehicles (CVs), and CAVs based on car‐following behaviour. Firstly, considering the minimum safety distance constraints between vehicles, an optimization model for minimizing the impact on the acceleration of the known vehicles is developed to obtain the number of inserted RVs. Secondly, the speed of the inserted RVs is estimated based on the traffic flow model. Then, an optimization model is proposed to determine the position of each inserted RV. Finally, numerical simulation is designed to investigate the influence of traffic density and PRs of CAVs and CVs. Results show that the proposed method can better reconstruct the vehicle trajectory on the freeway under the different traffic densities in a congested state. The MAPE of the number and position of inserted RVs is less than 10.7% and 0.37%, respectively. In addition, the proposed method performs well even if the PRs of CAVs and CVs are extremely low.

Development of a cyber-physical-system perspective based simulation platform for optimizing connected automated vehicles dedicated lanes

Recently, many studies have shown that during the transition period of the coexistence of autonomous vehicles (AVs)/connected automated vehicles (CAVs) and human-driven vehicles (HDVs), a dedicated lane (DL) for AVs/CAVs can improve traffic throughput and lower collision risk, which has become a hot topic in the field of intelligent transportation systems (ITS). This study presents an integrated simulation platform for DL optimization from cyber-physical-system (CPS) perspective. The developed simulation platform can be used to qualitatively and quantitatively evaluate the design parameters, operation modes, contingency plan, etc. of DL in a “What You See Is What You Get (Wysiwyg)” style. First, we introduce the architecture and features of the proposed platform, which comprises of SUMO, OMNeT++, and Unity3D. We implemented all experimental scripts for DL optimization in SUMO, including the configuration of the DLs, car-following models, lane-changing models, vehicle departure models, origin–destination demands, and 2D visualization of traffic flow simulation. We used OMNeT++ for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Unity3D is a 3D visualization platform for simulations synchronized with SUMO. Connected with the driving simulator and paired with a virtual reality (VR) helmet, Unity3D can offer a high-fidelity immersive environment for all types of users. We developed a specific communication protocol to achieve data sharing and synchronization of all components of the proposed simulation platform. Second, this paper depicts the functions of the proposed simulation platform in detail, including the modeling of road networks, vehicles, traffic flow, optimal design, and evaluation indicators of DL. Finally, by using the developed simulation platform, we demonstrated and analyzed three cases: (1) without DL vs with DL, (2) inner lane vs outer lane, and (3) without V2I vs with V2I. The experimental results confirm the feasibility of the developed simulation platform, and yield some interesting findings, such as (1) market penetration rates (MPRs) are an important factor in deciding whether to deploy DLs, (2) the number of lane changes when the vehicle enters/leaves the designated lane is the key criterion for determining the placement of the DLs, and (3) the cooperative vehicle-infrastructure system can offer AVs/CAVs with better over-the-horizon perception capabilities, helping to improve the smoothness of traffic flow in the case of abnormal events. The developed simulation platform is expected to offer a new tool for optimal design and evaluation of DLs for AVs/CAvs.

Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative merging control approach that ensures collision-free and traffic-efficient merging through the interaction of a maneuver planner and an operational trajectory controller. The planner predicts future vehicular trajectories, including acceleration trajectories and time instants when lane changes start, in a long horizon up to 50 seconds with a linear prediction model. It establishes the optimal dynamic vehicle sequence in each lane by minimizing predicted traffic disturbances that can propagate upstream and lead to traffic breakdown. During the process, mainline vehicles may change lane to facilitate the on-ramp merging, albeit with a higher ego cost. The operational controller follows the established instructions from the planner and regulates vehicular trajectories with model predictive control in a shorter horizon of 6 seconds. The performance of the designed hierarchical cooperative merging control approach was compared to a cooperative merging method utilizing widely used first-in-first-out rule to establish merging sequences and the same operational controller to generate vehicular trajectories. Systematic comparison shows that the proposed approach consistently results in less disturbances during merging under 528 different scenarios with different traffic states, initial vehicular states, and desired time gap settings. On average, a decrease of 39.18% in disturbances was observed.

Observer-Based Double Closed-Loop Control for Mixed Vehicle Groups: A Macro and Micro Perspective

The paper aims to ensure that traffic parameters are simultaneously available between sparse sensors, and to improve the traffic efficiency of expressways and achieve the consistent driving state of mixed vehicle groups. Firstly, we propose a dynamic mixed segmental linear (DMSL) traffic model with external disturbances including automated vehicles (AVs) and connected automated vehicles (CAVs) as the composition of mixed vehicle groups on the expressway. Secondly, an observer-based double closed-loop control strategy is investigated by jointly controlling the on-ramp flow in the outer-loop control system and the driving state of mixed vehicle groups on the main section of the expressway in the inner-loop control system. Thirdly, the convergence and stability of the inner-loop control system and the outer-loop control system are analyzed by using Lyapunov stability theory, and the control gain and observation gain of the double closed-loop are obtained via adopting linear matrix inequality (LMI) method. Finally, numerical simulation experiments are executed to confirm the feasibility and accuracy of the proposed control algorithm. The results illustrate that the proposed control method is effective while the percentage of automated vehicles (AVs) is gradually increasing.

Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems

• Propose interpretable dimensionality reduction techniques in trajectory prediction. • Piecewise Taylor series approximation and piecewise Fourier series approximation. • Reduce device cost and computing energy consumption by simplifying computation. • Verify the techniques’ effectiveness by comparing them with benchmarks. • Prove the techniques’ robustness against data noises. To facilitate low-cost connected automated vehicle (CAV) system development, this study proposes two interpretable dimensionality reduction techniques in vehicle trajectory prediction, i.e., the piecewise Taylor series approximation (PTA) and the piecewise Fourier series approximation (PFA), to lower computation complexity, reduce device investment, and decrease computation energy consumption. Two benchmarks are developed, the long short-term memory (LSTM)-based model without dimensionality reduction and the LSTM-based model with encoder-decoder (a widely used dimensionality reduction technique). Results show that the four predictions have similar accuracy, and the training time (proportional to computation energy consumption) of models with dimensionality reduction techniques is greatly reduced. The reduction is even more significant when PTA/PFA is used. Sensitivity analysis advises PFA/PTA parameter selections to reduce computation complexity without significant loss of prediction accuracy. Further, the robustness of the LSTM PTA/PFA is proven by the investigation of data noises.

RBFNN-Based Adaptive Event-Triggered Control for Heterogeneous Vehicle Platoon Consensus

Vehicle platoon, as an important application of connected automated vehicles (CAVs), is a hot topic in the intelligent transportation systems (ITSs). Most existing results on platoon control used the exact feedback linearization method to transform nonlinear vehicle dynamics into linear ones, which required complete prior knowledge of parameters. As the measurement errors and the environment disturbance exist, the accurate parameters are difficult to obtain in real world. This paper considers a nonlinear uncertain vehicle platoon under a network topology (NT) modeled by a spanning tree in a digraph. Radial basis function neural networks (RBFNNs) are used to approximate the uncertain function and an adaptive distributed controller is designed by backstepping control method. To reduce the update rate of the controllers, an event-triggered control scheme is proposed with two different kinds of triggering conditions. It is proven that vehicle platoon practical consensus can be achieved under the Zeno-free adaptive event-triggered control scheme. Moreover, for bidirectional (BD) NT and two predecessor-following (TPF) NT, vehicle platoon reaches the string stability. Finally, a vehicle platoon under three different kinds of NTs is simulated to demonstrate the effectiveness of the theoretical results.

Automated city shuttles: Mapping the key challenges in cybersecurity, privacy and standards to future developments

The Automated City Shuttles (ACSs) aim to shape the future public transportation and provide more efficient and accessible mobility in smart cities. With the absence of a driver, such mini-busses process the sensors’ inputs and exchanged data with other vehicles and intelligent transport systems to achieve a real time assimilation of its surroundings. Consequently, the technologies supporting the driverless functionalities ushered new cybersecurity risks and data privacy breaches. Unfortunately, several studies mostly focus on individual Connected-Automated Vehicles (CAV), though intrinsic underpinnings of the ACS’s threat vectors remain unexplored. In the present paper, we considerably extend that investigation by proposing a comprehensive state of the art with farsighted analyses addressing security threats and data privacy concerns from both technical and legal perspectives to thwart potential attacks. Moreover, as existing approaches have not provided yet a clear road map about ACS’s security standards, the present work sheds light on recent and up to date standards and standardisation bodies dealing with cybersecurity and privacy issues in the automated driving ecosystem. This paper presents an analysis debating the trade-off between maximising the ACS benefits and minimising the associated security vulnerabilities and attacks through an overview of technical and legal mitigation strategies.

Platoon Trajectory Completion in a Mixed Traffic Environment Under Sparse Observation

Obtaining sufficient trajectory data of human-driven vehicles (HDVs) is critical for effective control of connected automated vehicles (CAVs) in mixed traffic of HDVs and CAVs. However, due to limited sensing and communication capabilities, only a fraction of HDVs’ trajectories are often observed. This paper proposes a completion method to recovery all HDVs’ trajectories in a mixed platoon based on partial observations. The trajectory completion problem is formulated as an optimization problem, aiming to minimize the error between observed and completed trajectories with car-following constraints defined by Newell’s simplified car-following model. The method also allows various model parameters of different drivers, which is known as the inter-driver heterogeneity, to reduce the completion error. Validation using empirical trajectory data shows that the proposed method greatly lowers the completion error than other typical trajectory completion methods under sparse observation (6s/sample).

Distributed Data-Driven Predictive Control for Hybrid Connected Vehicle Platoons With Guaranteed Robustness and String Stability

As a critical component of the Internet of Things, connected automated vehicles (CAVs) are progressively gaining attention for their benefits in terms of increased safety and reduced traffic congestion. In this article, a novel distributed data-driven model-predictive control (DDMPC) approach including feedforward for disturbance is proposed for cruise control of a hybrid platoon with a combination of human-operated and autonomous vehicles. By employing a predictor constructed from input/output data, predictive controllers are obtained without depending on the characteristic information of the system. A robustness analysis is performed with a combination of the input-to-state stability (ISS) theory with the sampled-data systems theory, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> -norm string stability is ensured by strict mathematical proof. In addition, we also discuss the asymptotic stability when the controller switches. CarSim simulation and bench experiment results verify that the DDMPC for connected vehicles can be robust to velocity disturbances and achieve satisfactory performance in ensuring string stability.

An Eco-Driving Strategy for Partially Connected Automated Vehicles at a Signalized Intersection

We consider a signalized intersection under a partially connected automated vehicle (CAV) environment. There is a control center for the control zone that needs to predict the trajectories of human driving vehicles (HDVs) and control the trajectories of CAVs. By adopting model predictive control, we propose a real-time eco-driving strategy for the control center. In the proposed strategy, the Gipps’ car-following model is selected to update the acceleration of HDV and an optimal control problem (OCP) is proposed to optimize the trajectory of each CAV based on real-time travel information. The control objective is to minimize the total fuel consumption of each CAV during the current control period. Pontryagin’s minimum principle is employed to derive necessary optimality conditions under different scenarios. With the necessary optimality conditions, a numerical method is developed to solve the proposed OCP. Finally, numerical examples are provided to illustrate the performance of the proposed eco-driving strategy.

Distributed cooperative trajectory and lane changing optimization of connected automated vehicles: Freeway segments with lane drop

This study presents a methodology for optimal control of connected automated vehicles (CAVs) in freeway segments with a lane drop. Lane drops can create bottlenecks with a considerable number of mandatory and discretionary lane-changing maneuvers when traffic volume is high, which can eventually lead to stop-and-go conditions. Proper motion planning aligned with optimal lane changing upstream of a lane drop can increase capacity and reduce the number of stops and the risk of collision. This paper introduces a vehicle-level mixed-integer program to control longitudinal and lateral movement of CAVs, provide a smooth flow of traffic, and avoid congestion in freeway segments with lane drops. To ensure the feasibility of vehicle-level decisions and promote system-level optimality, a cooperative distributed algorithm is established, where CAVs coordinate their decisions to find the optimal longitudinal and lateral maneuvers that avoid collisions among all vehicles. The proposed coordination scheme lets CAVs find their optimal trajectories based on predictive information from surrounding vehicles (i.e., future locations and speeds) and coordinate their lane-changing decisions to avoid collisions. The results show that optimal lane changing of CAVs smoothens the traffic flow and increases freeway capacity in congested traffic conditions. Compared with all-knowing CAVs simulated in Vissim, the proposed methodology reduced the average travel time by up to 86.4%. It increased the number of completed trips by up to 134.3% based on various traffic demands and lane drop layout combinations.

Platooning Trajectory Optimization for Connected Automated Vehicles in Coordinated Arterials

This paper proposes heuristic methods to optimize the trajectories of connected automated vehicles (CAVs) along an arterial assuming fully automated traffic condition. CAV trajectories are adjusted to form platoons at the saturation headway, guaranteeing the arrival of vehicles at the downstream intersection during the green interval. On the arrival of CAVs, an algorithm enables a smooth transition to the system target speed. The CAVs’ trajectories are then adjusted by an algorithm according to the vehicles’ positions (leader/follower). Simulation parameters that consider human driving are selected to avoid overestimating the benefits of the proposed strategy. A simulation algorithm is developed to evaluate the performance of the proposed heuristic. The proposed method is compared with a pre-timed coordinated signal control scheme. Seven demand scenarios corresponding to undersaturated conditions are evaluated. The proposed method reduced travel time by 7% to 16% and delay by 23% to 43%. Its computational efficiency makes the proposed method suitable for real-world tests.

Characteristics Analysis and Equilibrium Optimization of Mixed Traffic Flow considering Connected Automated and Human-Driven Vehicles

Considering the impact of informatization condition, vehicles on the road network are divided into connected automated vehicles (CAVs) and human-driven vehicles (HDVs), which follow the principle of system optimization and stochastic user equilibrium, respectively. Taking the road network reserve capacity maximization model under the condition of road capacity constraint as the upper-level programming and the traffic assignment model under heterogeneous flow environment as the lower level programming, then a bilevel programming model is constructed. Among them, the nonuniform demand growth multiplier is adopted for each OD pair to reflect the inconsistency of traffic demand structure growth, and the calculation of link capacity is related to the market penetration of CAVs. The incremental method, method of successive averages, and simulated annealing algorithm are used to solve the model, and the effects of different market penetration on road network capacity, travel time, and saturation are analyzed through a numerical example. The relevant data under different weights are normalized and the optimal deployment scheme of CAVs and HDVs in different periods is obtained by comprehensive evaluation. Meanwhile, the mixed equilibrium flow state is explored under the premise of given market penetration to verify the feasibility of the model and algorithm.

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Helping Automated Vehicles With Left-Turn Maneuvers: A Game Theory-Based Decision Framework for Conflicting Maneuvers at Intersections

The deployment of connected, automated vehicles (CAVs) provides the opportunity to enhance the safety and efficiency of transportation systems. However, despite the rapid development of this technology, human-driven vehicles are predicted to predominate the vehicle fleet, compelling CAVs to be able to operate in a mixed traffic environment. The key to achieving a reliable and safe human-CAV collaboration in such environments is to characterize the interactions between the actors and incorporate the underlying decision-making mechanism of human drivers into CAVs’ motion planning algorithms. Towards this goal and extending a previously developed game theoretical model, the present study proposes a decision-making dynamic to achieve more realistic models of human behavior when making conflicting maneuvers at intersections. A novel field test is conducted to extract the required modeling data directly from CAVs’ perception system, facilitating the incorporation of the model into CAV navigation algorithms. Model validation and sensitivity analysis provided invaluable insights into the nature of human decisions and indicated that the proposed structure is robust to environmental uncertainties and can well capture the real-world behavior of human drivers in unprotected left-turn maneuvers. The derived knowledge can be directly used in CAV motion planning algorithms to provide the vehicle with more accurate predictions of human actions when operating in mixed traffic environments.

Impact of Connected Automated Buses in a Mixed Fleet Scenario With Connected Automated Cars

Considering the significant increase in transportation (R. Abe, 2019) demand in the last few decades and its consequences on society and the environment, the development of a sustainable transport system is vital. The relevance of public transport networks is expanding as present-day environmental, financial and resource pressures bear upon cities, providing the impetus for new sustainable and economical modes of mobility. Promoting public transport and introducing connectivity and automation are crucial for effectuating a sustainable transport system. Buses can improve the network performance, but curbside bus stops (CBS) elevate the side friction which cause substantial delays to other vehicles, urging imprudent forced lane changes and affecting safety. The introduction of connectivity and automation in buses may improve performance along with safety, and this study attempts to investigate the repercussions of connected automated buses (CABs) in a mixed fleet with connected automated vehicles (CAVs), through a microsimulation modelling exercise. Results indicate that CABs can reduce network-level travel time by 7.9%, 6.4%, 2.1%, 1.3%, 1.8%, 1.2% and 2.27% for 0%, 10%, 30%, 50%, 70%, 90% and 100% CAV penetration, when compared to the network with Human-driven buses (HBs) with the same CAV penetration. Furthermore, CABs also improve the performance of link containing bus stops by reducing standstill times (timespan when buses have zero velocity on the bus stop link). Results exhibit that CABs can decrease standstill times by 12.5%, 13.3%, 12.6%, 15%, 12.9%, 16.7% and 19.9% for 0%, 10%, 30%, 50%, 70%, 90% and 100% CAV penetration, when compared to the network with HBs with identical CAV penetration. Additionally, the number of lane changes in these links were reduced by 28.2% at 100% CAV penetration, indicating synchronous driving and reduced forced lane changes. The crash rate indicates that critical events involving automated vehicle entities and Manual Vehicles (MV) can increase as the MVs cannot adapt promptly to automated driving behaviour. This is supported by the non- parametric distribution of post-encroachment time (PET), time- to-collision (TTC) and relative speed (DeltaS), indicating that the conflict/collision typology’s severity can increase at intermediate CAV penetrations, but plunge significantly at 100% penetration when compared to the current scenario. Lastly, the limitation of tools in flagging conflicts involving CAVs is discussed, and future scrutiny is proposed.