Automated Vehicle Environment Research Articles

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”

Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle’s acceleration and steering angle. The statistical analysis of the model’s parameter reveals that acceleration and steering angle will directly affect the distribution of the driving risk potential field and that this strong correlation should not be ignored if one is interested in the vehicle’s microscopic motion behavior. We further develop a driving risk potential field-based car-following model (DRPFM) to remedy the failure of acceleration consideration under the conventional environment, whose parameters are calibrated by filtered I-80 NGSIM data with frequent traf?c oscillations. Simulation results indicate that our proposed DRPFM model is proved to be a good description of car-following behavior and outperforms two classical car-following models (Optimal Velocity Model and Intelligent Driver Model) in frequent oscillation phases due to our consideration of potential acceleration data acquisition in real-time under the CAVs environment. In addition, this DRPFM model is applied to deduce the safety conditions for vehicle lane-changing. The analysis results prove that this model can reasonably explain the influencing factors between driver types and lane-changing safety conditions in practice.

Using Artificial Potential Field Theory for a Cooperative Control Model in a Connected and Automated Vehicles Environment

Artificial potential field (APF) theory has been extensively applied in traffic path planning as an efficient method to avoid collision. However, studies in collision avoidance based on APF theory only considered the movement of single vehicle. In this paper, a vehicle cooperative control model for avoiding collision in the connected and autonomous vehicles (CAVs) environment is presented, using APF theory. The proposed model not merely guarantees the travel safety of vehicles in avoiding collision, but also promotes driving comfort and improves traffic efficiency. To verify the cooperative control model, simulations of four scenarios are designed and compared with the human driving environment. Five indicators are selected to evaluate the results, that is, time–space diagram, time mean speed (TMS), the rate of large deceleration time (large deceleration is that deceleration larger than –2 m/s2), the inverse time-to-collision ([Formula: see text]), and lane-changing times. According to the simulation results, the cooperative control model could alleviate the capacity drop and increase the TMS to improve traffic efficiency, reduce the rate of the large deceleration time to promote driving comfort, and decrease [Formula: see text] to promote safety in small and large input flow rates. The results reveal the proposed model is significantly superior to the human driving environment whether in free or congested situations, except for the lane-change times, which are slightly larger.

Development of Integrated Traffic Control of Dynamic Merge and Lane Change at Freeway Work Zones in a Connected and Automated Vehicle Environment

Development of Integrated Traffic Control of Dynamic Merge and Lane Change at Freeway Work Zones in a Connected and Automated Vehicle Environment

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques

Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks or provide assistance when it is needed the most. This study proposed trajectory-level lane change detection models based on features from vehicle kinematics, machine vision, roadway characteristics, and driver demographics under different weather conditions. To develop the models, the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) datasets were utilized. Initially, descriptive statistics were utilized to investigate the lane change behavior, which revealed significant differences among different weather conditions for most of the parameters. Six data fusion categories were introduced for the first time, considering different data availability. In order to select relevant features in each category, Boruta, a wrapper-based algorithm was employed. The lane change detection models were trained, validated, and comparatively evaluated using four Machine Learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtrem Gradient Boosting (XGBoost). The results revealed that the highest overall detection accuracy was found to be 95.9 % using the XGBoost model when all the features were included in the model. Moreover, the highest overall detection accuracy of 81.9 % using the RF model was achieved considering only vehicle kinematics-based features, indicating that the proposed model could be utilized when other data are not available. Furthermore, the analysis of the impact of weather conditions on lane change detection suggested that incorporating weather could improve the accuracy of lane change detection. In addition, the analysis of early lane change detection indicated that the proposed algorithm could predict the lane changes within 5 s before the vehicles cross the lane line. The developed detection models could be used to monitor and control driver behavior in a Cooperative Automated Vehicle environment.

Reducing gasoline consumption in mixed connected automated vehicles environment: A joint optimization framework for traffic signals and vehicle trajectory

The application of connected automated vehicles can significantly reduce traffic congestion, gasoline consumption, and transportation emissions. Most of the existing research on the connected automated vehicles focuses on the improvement of the performance of the transportation system, the impact on gasoline consumption and transportation emissions is not concerned. This paper aims to study the impact on gasoline consumption and transportation emissions of mixed traffic flow with connected automated vehicles at an isolated intersection. Therefore, a joint optimization framework for traffic signals and vehicle trajectory of mixed connected automated vehicles and human-driven vehicles at an isolated intersection is proposed to reduce gasoline consumption and transportation emissions. The joint optimization framework is a two-level optimization model. Firstly, a proposed dynamic programming at the upper level is applied to optimize traffic signal timing based on predicted vehicle arrival, which can be calculated by the lower level. Secondly, a model predictive control method is proposed at the lower level to optimize vehicle trajectories considering gasoline consumption. Finally, the joint optimization framework is verified in a simulated connected automated vehicle environment and a case study conducted based on an isolated intersection. Numerical results indicate that the joint optimization method can reduce both gasoline consumption and transportation emissions. The reduced vehicle delay, gasoline consumption, and CO2 emissions can be as much as 57.21%, 22.36%, and 18.61%, respectively, for the 100% penetration rate of connected automated vehicles.

Cooperative Eco-Driving at Signalized Intersections in a Partially Connected and Automated Vehicle Environment

The emergence of connected and automated vehicle (CAV) technology has the potential to bring a number of benefits to our existing transportation systems. Specifically, when CAVs travel along an arterial corridor with signalized intersections, they can not only be driven automatically using pre-designed control models but can also communicate with other CAVs and the roadside infrastructure. In this paper, we describe a cooperative eco-driving (CED) system targeted for signalized corridors, focusing on how the penetration rate of CAVs affects the energy efficiency of the traffic network. In particular, we propose a role transition protocol for CAVs to switch between a leader and following vehicles in a string. Longitudinal control models are developed for conventional vehicles in the network and for different CAVs based on their roles and distances to intersections. A microscopic traffic simulation evaluation has been conducted using PTV VISSIM with realistic traffic data collected for the City of Riverside, CA, USA. The effects on traffic mobility are evaluated, and the environmental benefits are analyzed by the U.S. Environmental Protection Agency’s MOtor Vehicle Emission Simulator (MOVES) model. The simulation results indicate that the energy consumption and pollutant emissions of the proposed system decrease, as the penetration rate of CAVs increases. Specifically, more than 7% reduction on energy consumption and up to 59% reduction on pollutant emission can be achieved when all vehicles in the proposed system are CAVs.

Impact of conflict resolution parameters on combined alternate-directions lane assignment and reservation-based intersection control

We recently proposed a concept, called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC), for organizing directionally unrestricted traffic flows in automated vehicle environment. The conflicts between through movements are handled by a reservation-based algorithm while the turning conflicts at the intersections are avoided altogether. This paper extends this research by analyzing the impacts that CADLARIC’s parameters, used to control the conflict-resolution processes, have on the efficiency and surrogate safety indicators. The investigated parameters include: (i) buffer time in cell’s reservation schedule; (ii) allowed speed to cross the reserved cell; (iii) distance from intersection from which a vehicle can make reservation, and (iv) duration of the lane-change process. For most of the investigated parameters, the numerical results show that less efficient operations lead not just to an increase in delay time and number of stops but also increase number of conflicting situations, because of vehicular queues formed within the intersection zone.

Dynamic combination control of phase movement at signal intersection under the automated vehicle environment

With the continuous perfection of the technology of automated vehicles (AV), data exchange can be conveniently carried out between different vehicles and infrastructures, which makes it easier to collect different types of traffic parameters. Therefore, under AV environment, the vehicle status can be determined to obtain the periodic arrival rate of movements and a more efficient control strategy can be designed. The combination styles of phase movement (PM), an important factor of the signal control, will also become more complicated for intersection signal control. The current methods about the PM combination styles only considered two kinds of movement combination styles, and cannot get the extensive phase combination (PC) schemes in AV environment. This paper documents a new PM combination method by fractionalized movement compatibility relations, and uses discrete mathematics to calculate overall PC schemes. Then, a PM dynamic combination control method is proposed to optimize cyclically signal control. The analysis results of numerical tests showed that the average vehicle of the proposed method is reduced by 6.9 % and 14.5 % for 20 signal cycles, respectively, and the total throughput can be increased by 4.3% and 7.8%, respectively, compared with the dynamic timing control mode and the fixed control mode. Results show that the proposed method could significantly improve intersection control effectiveness.

Car-following Model Based on Safety Potential Field Theory Under Connected and Automated Vehicle Environment

Car-following Model Based on Safety Potential Field Theory Under Connected and Automated Vehicle Environment

An Extensive Investigation of an Eco-Approach Controller under a Partially Connected and Automated Vehicle Environment

This study evaluated the performance of an eco-approach control system at signalized intersections under a partially connected and automated vehicle (CAV) environment. This system has the first eco-approach controller able to function with the existence of surrounding human-driven traffic. A previous evaluation only confirmed its benefits. The purpose of this study was to conduct a further extensive test on the controller to identify room for improvement. Two different networks were tested, including an isolated signalized intersection and a corridor with two signalized intersections. The measures of effectiveness (MOEs) adopted were throughput and fuel consumption. All the before-and-after MOEs were compared using t-tests. The results indicate that the controller generally improved the fuel efficiency without harm to the mobility, and its environmental performance was affected by the minimum CAV speed, green ratio, congestion level, and marker penetration rate of CAVs. A detailed investigation revealed that no significant environmental benefit was generated under high congestion levels when the minimum speed of CAVs was more than 20 mph, and the shockwaves caused by the eco-approach control may result in a gating effect that reduces the throughput at the upstream intersection of the corridor under high congestion levels.

Combined Alternate-Direction Lane Assignment and Reservation-Based Intersection Control

This paper presents a concept, herein called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC), for organizing directionally unrestricted traffic flows in automated vehicle environment. In the proposed concept, vehicles can use lanes traditionally reserved for the opposite direction of travel. This concept allows left and right turning vehicles to align themselves in an appropriate lane before reaching the downstream intersection, so that they can smoothly go through the intersection without having any conflicts with vehicles from the other movements. Conflicts between through movements are handled by a reservation-based algorithm. To simulate the proposed concept, i.e. allow flexibility of such driving maneuvers that cannot be accomplished in the other existing simulation tools, a new microsimulation platform is developed. The proposed CADLARIC control scheme is evaluated through a comparison with a conventional fixed-time signal control and Fully Reservation-Based Intersection Control (FRIC). The results show that CADLARIC: (i) significantly outperforms other control methods in terms of the traffic performance (i.e. delays and stops); and (ii) generates a reduction of conflicting situations at the network level when compared to FRIC.

Cooperative Lane Changing Strategies to Improve Traffic Operation and Safety Nearby Freeway Off-Ramps in a Connected and Automated Vehicles Environment

The study proposes a cooperative lane changing strategy to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). The cooperative strategy was implemented by the coordination of behaviors between the diverging vehicle and its cooperative vehicle on the target lane. The Minimizing Overall Braking Induced by Lane Changes Model (MOBIL) and Intelligent Driver Model (IDM) were modified to develop a simulation platform for a CAV environment. The optimal cooperative lane changing zones were firstly calculated by a heuristic algorithm, and then were applied in the simulation platform to implement the cooperative strategy. Various metrics were considered to evaluate the proposed strategy, including: total travel time, surrogate safety measures and traffic waves in the system. The experimental results showed that the length of the optimal cooperative zones obtained in our strategy were smaller than the fixed zone required in modified MOBIL strategy. Moreover, the results indicated that the cooperative strategy with the optimal zones, could improve traffic operation, traffic safety and traffic oscillation as compared to the modified MOBIL strategy with the fixed zone. The cooperative strategy can be potentially implemented nearby highway off-ramps by vehicle-based control, with the applications of the aforementioned cooperative zones.

Single-Lane Roundabout Manager under Fully Automated Vehicle Environment

In the advent of new automation and communication technologies, the introduction of connected and autonomous vehicles (CAVs) can make efficient use of the space on our road networks. Since roundabouts suffer from lower efficiencies when traffic flows are unbalanced and unfamiliar drivers are present, CAVs have the potential to improve traffic operations, reducing lost time and inefficiency in gap-usage. The aim of this research is to take advantage of those recently available technologies to design a traffic management system for roundabouts. Such a system can provide CAVs with optimal trajectories to negotiate the roundabout, maximizing throughput and minimizing control delay. The rule-based Roundabout Manager algorithm developed considers the roundabout as a whole system, assessing the priority of incoming vehicles on a first-come-first-served scheduling to solve conflicts. The management system takes into consideration the geometry of the roundabout, including the flared entries and exits and the location of conflict sections based on merging trajectories of vehicles in negotiating the roundabout. To illustrate the operation of the algorithm, a total of 15 undersaturated demand scenarios with 10 replications per scenario were simulated. From the results, it can be concluded that the system can reduce control delay for medium demand scenarios, maximizing the throughput for all demand scenarios. The Roundabout Manager can work in real time providing optimized trajectories in less than 0.02 s at any traffic flow rate simulated.

Spatiotemporal intersection control in a connected and automated vehicle environment

Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO2 emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.

Auction-based tolling systems in a connected and automated vehicles environment: Public opinion and implications for toll revenue and capacity utilization

Autonomous and connected vehicles are expected to enable new tolling mechanisms, such as auction-based tolls, for allocating the limited roadway capacity. This research examines the public perception of futuristic auction-based tolling systems, with a focus on the public acceptance of such systems over current tolling practices on highways (e.g., dynamic and fixed tolling methodologies). Through a stated-preference survey, responses from 159 road-users residing in Virginia are elicited to understand route choice behavior under a descending price auction implemented on a hypothetical two-route network. Analysis of the survey data shows that there is no outright rejection of the presented auction-based tolling among those who are familiar with the current tolling methods. While males strongly support the new method, no clear pattern emerges among other demographic variables such as income and education level, and age. While high income respondents and regular commuters are more likely to pay higher tolls, no statistical significance between different genders, age groups, household sizes, and education levels is found. Based on the modeling results and the hypothetical road network, it is found that descending price tolling method yields higher average toll rates, and generates at least 70% more revenue when travel time saving is 30min, and improves capacity utilization of the toll road significantly compared to fixed tolls.

Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment

This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin’s Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO2 emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV’s market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.