Automated Vehicle Environment Research Articles

RCP‐RF: A comprehensive road‐car‐pedestrian risk management framework based on driving risk potential field

AbstractRecent years have witnessed the proliferation of traffic accidents, which led wide researches on automated vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time‐based frameworks cannot handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, a comprehensive driving risk management framework named RCP‐RF is novelly proposed based on potential field theory under connected and automated vehicles environment, where the pedestrian risk metric is combined into a unified road‐vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state‐of‐the‐art methods on real‐world dataset NGSIM and real AV platform.

Improving Traffic Operation of Bottleneck in a Connected and Automated Vehicles Environment: An Integrated Lane-Level Control Method

Improving Traffic Operation of Bottleneck in a Connected and Automated Vehicles Environment: An Integrated Lane-Level Control Method

Analysis of superblocks during the transition phase from traditional vehicles to fully automated vehicle environment: A case study of Barcelona City

The planning of vehicle-dependent cities across the world with emerging technologies like automated vehicles must be amended to make them pedestrian-friendly. Therefore, there is a need to reorganise the urban streets to create more spaces for pedestrians, even with the presence of automated vehicles (AVs). This research proposes the concept of a superblock model in the presence of automated vehicles to meet this challenge. This research aims to assess the impacts of AVs on pedestrian-friendly streets using a superblock approach. In the superblock model, cut-through traffic demand is restricted, and traffic generated or attracted to a superblock can have access, allowing for more room for public spaces. This study systematically discusses the superblock model implemented in a network with a grid structure using a genetic algorithm. It addresses the dynamic user equilibrium assignment for demand and topology segmentation. Three scenarios are considered due to the longer period of transition from traditional vehicles to fully AVs. The study evaluates the potential and constraints of implementing the superblock model with different penetration rates of AVs in the system by defining variable traffic demand and the variable number of superblocks. Furthermore, a cost-benefit analysis of the proposed methodology is conducted. The proposed model is implemented on a real-time network of Barcelona city, and the findings of this study are summarised by the macroscopic fundamental diagram, which depicts the improvement in the performance of the network with the inclusion of AVs.

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.

TD3LVSL: A lane-level variable speed limit approach based on twin delayed deep deterministic policy gradient in a connected automated vehicle environment

Variable speed limit (VSL) control plays a vital role in the emerging connected automated vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by capacity drop and accidents. However, the effect of the VSL on improving traffic efficiency is still controversial. It is necessary to study how to explore the potential benefits of the VSL by balancing its influence on reducing crash risk and enhancing traffic efficiency. To fill the technological gap above, this paper proposes a reinforcement learning-based lane-level VSL (LVSL) control approach to conduct refined traffic control on the mainlines. Firstly, an actor-critic framework is developed to generate and evaluate the discrete speed limits of each lane in continuous action space. To optimize traffic control performance, a hybrid reward function is developed by synchronously considering traffic safety and traffic efficiency of the bottleneck area. Then, to solve the overestimation bias problem of the actor-critic methods caused by function approximation error, a twin delayed deep deterministic policy gradient (TD3) method is introduced to train the framework of the LVSL method. Finally, a real-world recurrent bottleneck of the State Route 91 highway in California is simulated with consideration of the connected automated vehicles to examine the performance of the TD3-based LVSL (TD3LVSL) controller. The simulation results reveal that the proposed method is capable of reducing crash risk and improving traffic efficiency synchronously. Compared with the LVSL controller based on the deep deterministic policy gradient approach, the TD3LVSL controller shows better performance in terms of traffic safety and efficiency. These findings indicate that the proposed controller could contribute to future traffic control in a CAVH environment.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

AbstractIn recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

Managing the Morning Commute Problem with Tradable Credit Schemes under a Fully Autonomous Vehicle Environment

This study investigates the morning commute problem in a single corridor with a bottleneck under a fully automated vehicle environment. By extending Vickrey’s bottleneck model, we formulate the joint decisions on departure time and parking choices of morning commuters who make trade-offs among travel cost, the cost associated with parking, and the cost of tradable credits. To alleviate traffic congestion and improve social welfare, we propose a time-varying tradable credit scheme and integrate it with the morning commute problem. We explore the travel patterns and the optimal design of tradable credit schemes for the morning commute problem with homogeneous and heterogeneous commuters, respectively. For the homogeneous case, we derive the conditions on the tradable credit scheme to ensure the existence of equilibrium. The system-wide travel cost decreases with parking density after tradable credits are incorporated. Additionally, the efficiency of the tradable credits scheme can be improved by increasing the rate of credit charge rate. For the heterogeneous case, we propose an initial distribution strategy and combine it with the optimal tradable credit in order to guarantee social equity. The commuters with a low value of time (VOT) should be allocated more credits and the commuters with high VOT should be charged more credits. For both cases at system optimum, we find that the equilibrium price of tradable credits increases with parking density and decreases with the total amount of tradable credits.

Real-Time Adaptive Traffic Signal Control in a Connected and Automated Vehicle Environment: Optimisation of Signal Planning with Reinforcement Learning under Vehicle Speed Guidance.

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions.

Human-Lead-Platooning Cooperative Adaptive Cruise Control

In this study, a Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for connected and automated vehicles to “include” human drivers in platooning process. The goal is to form a platoon between automated vehicles and human drivers so that turbulence caused by human drivers could be smoothed out by automated vehicles. Unlike the conventional CACC where only longitudinal control is automated, the proposed HLP-CACC regulates both longitudinally and laterally. In other words, the followers in an HLP-CACC platoon are fully autonomous. The controller is formulated utilizing model predictive control (MPC) solved by Chang-Hu’s method. The technology has the following advantages: 1) take advantage of human drivers’ perception to enable conditional full autonomy; 2) accommodate actuator delay in system dynamics to improve actuator control accuracy; 3) automates both longitudinally and laterally; and 4) ensures string stability in partially connected and automated vehicles environment. Both simulation tests and field tests were conducted to verify the effectiveness of the proposed algorithm. Four scenarios, including straight cruising, lane changing, U-turn and circling were tested. Sensitivity analysis was conducted for speed, turning radius, communication delay and oscillation acceleration. The results confirm that the proposed CACC controller is ready for field implementation. The computation time of the proposed optimal control is approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4~\sim ~8$ </tex-math></inline-formula> milliseconds when running on an NVIDIA Drive PX 2 computer. Under the control of the proposed HLP-CACC, maximum longitudinal error and lateral error are both within 40 cm.

Vehicle Trajectory Reconstruction at Signalized Intersections Under Connected and Automated Vehicle Environment

Vehicle trajectories can provide a clear picture of traffic flow, which facilitates traffic state estimation and signal control optimization at intersections. Connected and Automated Vehicles (CAVs) can not only report their own trajectories, but also continuously collect surrounding vehicles’ trajectories using onboard sensors, which creates an opportunity to reconstruct fully-sampled vehicle trajectories. However, this data source brings challenges such as low penetration rate of CAVs and complex detection environment at intersections. To address these problems, this study proposes a novel framework under micro-perspective, in which trajectory estimation and fusion algorithms are integrated. The spatiotemporal correlations of detected trajectories are analyzed and classified into four regions, and four corresponding trajectory estimation algorithms based on extended car-following model are established to estimate the undetected part of each trajectory. Furthermore, a trajectory fusion algorithm based on Particle Filter is developed to fuse the estimated trajectories separately derived from upstream and downstream with minimized errors. The proposed method was comprehensively evaluated at field and simulated signalized intersections. The results show that compared with Variational Theory method, queue location error, time error and cumulative distance error of the proposed method were 76.3%, 44.4% and 54.5% lower, respectively, and the proposed trajectory fusion algorithm improved the accuracy and smoothness with the above three indices decreased by 17.3%, 47.7% and 6.2%, respectively. It was also found that the proposed method can adapt to different traffic conditions and penetration rates of CAVs.

Integrating traffic signal optimization with vehicle microscopic control to reduce energy consumption in a connected and automated vehicles environment

Transportation systems face a variety of problems, especially in the aspects of traffic efficiency and energy consumption. The emerging of connected and automated vehicles (CAVs) technologies offers promising solutions to tackle these challenges. Integrating traffic signal control with vehicle trajectory optimization has significant potential to improve transportation sustainability and traffic efficiency. This paper presents an integrated traffic control framework for traffic signal optimization and vehicle microscopic control of CAVs at an isolated signalized intersection to reduce fuel consumption and improve transportation sustainability. Firstly, considering the higher controllability of CAVs, the Minimizing Overall Braking Induced by Lane Changes Model and the Intelligent Driver Model are redesigned to control the microscopic longitudinal behavior and lateral behavior of vehicles. Secondly, an optimal control model is established to optimize the trajectories of vehicles considering fuel consumption and driving comfort. The analytical, closed-form solutions are derived through Pontryagin's Maximum Principle. Thirdly, to alleviate vehicle delay, the Controlled Optimization of Phase model is applied to determine the optimal intersection signal timing plan, including signal cycle length and duration of green and red phases. Finally, a receding horizon framework is built to integrate these control strategies and conduct the optimization. Experimental results suggest that the proposed integrated traffic control framework can improve both traffic efficiency and energy efficiency. The reduced traffic delay, energy consumption, and pollutant emission can be as much as 33.51–44.25%, 18.44–22.14%, and 13.36–55.20%, respectively depending on the demand scenario.

A Value Proposition of Cooperative Bus-Holding Transit Signal Priority Strategy in Connected and Automated Vehicles Environment

In this study, we assess a cooperative bus-holding transit signal priority strategy (C-BHTSP) in a connected and automated vehicles (CAV) environment. The research objectives of this study are to quantify the benefits of C-BHTSP to transit, its impact on opposing non-transit traffic, and the CAV market penetration rate required to neutralize that impact. A simulation model was developed in VISSIM using detailed, high-quality, up-to-date data for transit, non-transit traffic, and signal control parameters. Our assessment shows that the proposed strategy results in about a 60% reduction in transit delay compared to the base scenario. The results also show that a CAV MPR of 32% was found sufficient to overcome the negative impact of C-BHTSP on the opposing non-transit traffic while maintaining transit benefits. Higher MPRs produce substantial improvements in the overall travel time, delay, fuel consumption, and greenhouse gas emissions. The framework and findings of this study provide a blueprint for the decision-making process of implementing transit priority under varying CAV MPRs.

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. These results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

Collaborative control of traffic signal and variable guiding lane for isolated intersection under connected and automated vehicle environment

AbstractExisting signalized intersection control methods allocate intersection resources to each phase from the time dimension based on traffic conflict. Solving the traffic control issues caused by the unbalanced traffic demands at intersections is always a challenge. Recently developed connected and automated vehicle (CAV) technologies render it possible to collect detailed real‐time traffic information from vehicles for the optimal control and management of time‐space resources at signalized intersections. In this paper, we propose a mixed integer quadratic programming to jointly optimize signal timings and variable guide lane settings for a typical four‐arm intersection in the CAV environment. From the time dimension, restrictions of the conventional concept of the signal cycle are overcome. Phase sequences, green start, and duration are freely assigned for each CAV platoon based on the movement‐based signal timing. From the space dimension, lane resources can be reasonably allocated in consideration of the dynamic traffic demand distribution. In this study, a demand‐response control is realized for the signalized intersection through time‐space resources cooperated optimization. Furthermore, vehicle trajectory control is integrated into the collaborative control framework to reduce or eliminate wasted green time. At last, we propose the dynamic control process using the collaborative control framework. Numerical simulation results show that the collaborative control method outperforms over fixed‐time and signal optimization control modes in terms of travel time with both under‐saturated and over‐saturated conditions.

Optimal roundabout control under fully connected and automated vehicle environment

Roundabouts promise potential safety and efficiency benefits in the homogenous environment of conventional vehicles. However, their performance can deteriorate if high and/or imbalanced traffic exists. The advent of connected and automated vehicles (CAVs) has provided great opportunities to make transportation infrastructures more efficient. The purpose of this study is to develop a model to optimally control CAVs at roundabouts under a fully CAV environment. A two-stage optimization model is proposed to optimize vehicle trajectories. At the first stage, a mixed-integer linear programming model is formulated that optimizes vehicle arrival time at the roundabout. Then the optimal arrival time is fed to the second stage model to optimize vehicle trajectories, which is a non-linear programming model. CAVs are guaranteed to pass the roundabout safely without stops. Simulation tests are carried out with different demand levels and turning ratios. To evaluate the performance of the proposed model, delay and throughput are compared with a conventional strategy in which the roundabout is controlled by the yielding rules. Sensitivity analysis is conducted to investigate the impact of control zone length and roundabout diameters. The results show the advantages of the proposed model in terms of delay and throughput in both high and imbalanced traffic.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment

Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios.

A Unifying Graph-Coloring Approach for Intersection Control in a Connected and Automated Vehicle Environment

A Unifying Graph-Coloring Approach for Intersection Control in a Connected and Automated Vehicle Environment

Errata for “Cooperative Eco-Driving at Signalized Intersections in a Partially Connected and Automated Vehicle Environment”

Errata for “Cooperative Eco-Driving at Signalized Intersections in a Partially Connected and Automated Vehicle Environment”