Desired Time Headway Research Articles

Research on Ecological Driving Following Strategy Based on Deep Reinforcement Learning

Traditional car-following models usually prioritize minimizing inter-vehicle distance error when tracking the preceding vehicle, often neglecting crucial factors like driving economy and passenger ride comfort. To address this limitation, this paper integrates the concept of eco-driving and formulates a multi-objective function that encompasses economy, comfort, and safety. A novel eco-driving car-following strategy based on the deep deterministic policy gradient (DDPG) is proposed, employing the vehicle’s state, including data from the preceding vehicle and the ego vehicle, as the state space, and the desired time headway from the intelligent driver model (IDM) as the action space. The DDPG agent is trained to dynamically adjust the following vehicle’s speed in real-time, striking a balance between driving economy, comfort, and safety. The results reveal that the proposed DDPG-based IDM model significantly enhances comfort, safety, and economy when compared to the fixed-time headway IDM model, achieving an economy improvement of 2.66% along with enhanced comfort. Moreover, the proposed approach maintains a relatively stable following distance under medium-speed conditions, ensuring driving safety. Additionally, the comprehensive performance of the proposed method is analyzed under three typical scenarios, confirming its generalization capability. The DDPG-enhanced IDM car-following model aligns with eco-driving principles, offering novel insights for advancing IDM-based car-following models.

Robust String Stability and Safety of CTH Predictor-Feedback CACC

We establish robustness of string stability to delay uncertainty as well as positivity of spacing and speed states, for homogeneous vehicular platoons under predictor-feedback Cooperative Adaptive Cruise Control (CACC). Each individual vehicle’s dynamics are described by a second-order linear system with delayed desired acceleration, under acceleration information transmitted to the ego vehicle from a single, preceding vehicle. The nominal design (in the delay-free case) is a constant time-headway (CTH) policy and no restriction on the delay size, in relation with the desired time headway, is imposed. The proofs rely on combination of an input-output approach (on the frequency domain) and on deriving estimates on explicit, closed-loop solutions; under specific, sufficient conditions that are derived on initial conditions and parameters of the baseline, CTH controller. We illustrate in simulation and numerical examples, the guarantees of robust stability and string stability as well as of collisions avoidance, of CTH predictor-feedback CACC design. We also present extensions of our design and analysis approach to heterogeneous, third-order dynamic models of vehicles.

Consistency Analysis of Drivers’ Car-Following Behaviors

Car-following models are calibrated to account for various driver behaviors such as speed and space headway. Because drivers do not all drive the same way, they are typically classified based on their level, or profile, of aggressiveness. This approach to model calibration assumes that a single set of car-following parameters applies to an individual driver consistently, that is, during the entire driving period a study considers. The purpose of this research is to challenge that assumption by analyzing the heterogeneity and inconsistency of driver behaviors in different traffic conditions. A total of 3,262 urban expressway car-following periods from 51 drivers were extracted from the Shanghai Naturalistic Driving Study database. The intelligent driver model (IDM) was selected as this study’s car-following model for its favorable performance in highly dynamic situations; its parameters, that is, desired speed, desired time headway, maximum acceleration, comfortable deceleration, acceleration exponent, and minimum spacing, were calibrated. To reflect the effect of traffic conditions, the car-following periods were categorized into three regimes from low-speed to high-speed. Two-way analysis of variance was used to evaluate heterogeneity and inconsistency in the car-following parameters. The findings of this study show that (1) the main significant variable that distinguished driver profile was desired time headway, while the comfortable acceleration was commonly affected by both driver profile and speed regime; (2) variability of individual driver parameter estimates greatly depended on the traffic regime (low-, medium-, and high-speed), showing that driver behavior was highly influenced by traffic conditions; and (3) speed information was demonstrated to be a useful tool by which to divide car-following periods for modeling, as the car-following parameters exhibited distinct differences among the regimes in the varied traffic environment.

Car-Following Behavior of Human-Driven Vehicles in Mixed-Flow Traffic: A Driving Simulator Study

Connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) will inevitably coexist on roads in the future, creating mixed-flow traffic. The heterogeneous car-following (CF) behavior of HDVs can degrade the control performance of CAVs and introduce inefficiencies in CAV operations. To address these challenges, it is necessary to comprehend HDV CF behavior in mixed-flow traffic. This driving simulator-based study investigates HDV CF behavior in mixed-flow traffic under three different CAV control settings (string-stable, string-unstable, and HDV-like). The effects of traffic congestion level and demographic characteristics on CF behavior are also considered. Statistical analysis and CF model calibration, based on trajectory data collected from 72 participants in driving simulator experiments, are performed to examine the impacts of these factors on string stability, traffic efficiency, and safety. Then, online parameter estimation is conducted to illustrate the time-varying desired time headway, and sensitivity to spacing and speed variations (i.e., CF behavior evolution). Additionally, analysis of post-experiment interview results and eye-tracking data show that the string-stable CAV control setting is preferred by most HDV drivers, but can trigger driver distraction. The results also provide insights for CF behavior prediction and the optimal mixed platoon formation to enhance CAV benefits for traffic flow.

Cooperative control of a platoon of connected autonomous vehicles and unconnected human‐driven vehicles

AbstractBy using kinematic state information obtained through vehicle‐to‐vehicle communications, connected autonomous vehicles (CAVs) can drive cooperatively to alleviate shockwave propagation associated with traffic disturbances. However, during the transition to full autonomy, CAVs and human‐driven vehicles (HDVs) will coexist on the road, creating mixed‐flow traffic. The inherent heterogeneity and randomness in human driving behavior can generate additional disturbances in the traffic flow. Further, HDVs without communication functionality (unconnected HDVs) can cause the control performance of CAVs to degrade by negatively impacting platoon formation. To proactively mitigate the negative impacts of HDVs in mixed‐flow traffic, this study proposes a cooperative control strategy with three components for platoons consisting of CAVs and unconnected HDVs: (i) a number estimator for estimating the number of HDVs between two CAVs, (ii) a kinematic state predictor for predicting the kinematic states of HDVs, and (iii) a multi‐anticipative car‐following controller (i.e., control strategy using kinematic state information of multiple preceding vehicles) for CAVs to maintain string stability and desired time headway. To initialize the proposed strategy, the number estimator is developed using a deep neural network (DNN). Then, a DNN‐based kinematic state predictor predicts the kinematic states of HDVs for CAVs to enable multi‐anticipative car‐following control. The multi‐anticipative car‐following controller is implemented using an extended intelligent driver model‐guided deep deterministic policy gradient algorithm, which ensures safety, string stability, and traffic efficiency. The effectiveness of the proposed control strategy is validated through numerical experiments using NGSIM data. Results indicate that the proposed strategy can produce accurate estimations of the number and the kinematic states of HDVs between CAVs. Further, it can achieve string stability while maintaining smaller time headways, compared to car‐following models used for training guidance under different market penetration rates of CAVs, which significantly improves traffic smoothness and mobility.

Freeway Congestion Management With Reinforcement Learning Headway Control of Connected and Autonomous Vehicles

Adaptive cruise control (ACC) systems are increasingly offered in new vehicles in the market today, and they form a core building block for future full autonomous driving. ACC systems allow vehicles to maintain a desired headway to a leading vehicle automatically. Recent research demonstrates that (1) shorter headways lead to higher throughput, and (2) the effective use of ACC can improve traffic flow by adapting the desired time headway in response to changing traffic conditions. In this paper we show that, although shorter headways result in higher capacity, flow breakdown still occurs if traffic densities at bottlenecks are allowed to exceed the critical density. Therefore, dynamic traffic control near bottlenecks is still necessary to avoid bottleneck activation and capacity loss. We propose an adaptive reinforcement learning (RL) headway controller that uses ACC headways to optimize traffic flow and minimize delay. Based on state measurements, the controller dynamically assigns an optimal headway value for each freeway section within a control cycle. In a freeway simulation example, we first demonstrate that different nondynamic headway assignment strategies failed to avoid congestion and traffic breakdown. We then present a dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts the desired headway according to the changing traffic conditions on both the freeway and the ramp to effectively maximize traffic flow and minimize system delay. We quantitatively demonstrate that our DRL dynamic headway control strategy improved traffic and reduced system delay by up to 57% compared with the examined nondynamic headways.

Stability of connected and automated vehicles platoon considering communications failures

As a form of a future traffic system, a connected and automated vehicle (CAV) platoon is a typical nonlinear physical system. CAVs can communicate with each other and exchange information. However, communication failures can change the platoon system status. To characterize this change, a dynamic topology-based car-following model and its generalized form are proposed in this work. Then, a stability analysis method is explored. Finally, taking the dynamic cooperative intelligent driver model (DC-IDM) for example, a series of numerical simulations is conducted to analyze the platoon stability in different communication topology scenarios. The results show that the communication failures reduce the stability, but information from vehicles that are farther ahead and the use of a larger desired time headway can improve stability. Moreover, the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

Incorporating Driver Relaxation into Factory Adaptive Cruise Control to Reduce Lane-Change Disruptions

Current adaptive cruise control (ACC) systems adopt a fixed desired time headway, which often leads to abrupt speed changes in response to a close new leader after a cut-in or a lane change. These are disruptive maneuvers. In contrast, human drivers are willing to accept spacings much smaller than equilibrium values at the onset of these maneuvers and then gradually increase the spacing until they again reach equilibrium. This process, which typically lasts around 20 s or 30 s, is known as driver relaxation, which improves comfort and capacity. Therefore, this paper aims to incorporate relaxation into ACC systems. Based on the open-source factory-level ACC platform Openpilot by Comma.ai, the paper proposes a feasible relaxation model compatible with recent market ACC systems. The model is tested using simulation and road tests using a 2019 Honda Civic with its stock ACC hardware. The study further investigates the potential benefits of relaxation ACC on traffic operations. Comparative simulations suggest that incorporating relaxation into ACC can help: (i) reduce the magnitude of speed perturbations in both cut-in vehicles and followers; (ii) stabilize the lane-changing (LC) traffic by reducing the speed variance and preventing the lateral propagation of congestion; and (iii) increase the average flow speed and capacity after a bottleneck occurs.

Research on Car-Following Model considering Driving Style

In this paper, a car-following model considering various driving styles is constructed to fulfill the personalized needs of different users of autonomous vehicles. First, according to a set of selection rules, car-following events are selected from the Next Generation Simulation (NGSIM) dataset, and then through an unsupervised machine learning method, the extracted data are divided into two styles, i.e., conservative and aggressive. Statistical analysis is then conducted to analyze the differences in vehicle speed, acceleration, desired time headway, and so on between both driving styles. Based on the analysis, a car-following model based on model predictive control is designed. Experimental results from testing data show that the proposed car-following models demonstrate different driving styles in terms of safety, comfort, and effectiveness. The conservative driving model is safer and more comfortable than the radical driving model, but the driving efficiency is low.

Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles

With the development of technology, platoons of intelligent and connected vehicles (ICVs) will join regular vehicles on roads, and the characteristics of the traffic flow will change accordingly. In this paper, the traffic flow configurations and the spatial distributions of various types of vehicles are analyzed, when the mixed traffic flow is in equilibrium. To model the mixed traffic flow with platoons of ICVs, the intelligent driver model and the PATH laboratory-calibrated cooperative adaptive cruise control model are introduced for capturing the car-following behavior in this study. Then, analytical methods for the stability and the fundamental diagram models of mixed traffic flow are developed. The results of the sensitivity analysis demonstrated that ICVs can improve the stability of the mixed traffic flow under a critical speed. However, if this critical speed is exceeded, an increase in the number of ICVs may degrade the stability of the mixed traffic flow. Moreover, this critical speed decreases as the maximum platoon size increases. In addition to stability, the results suggest that ICVs can effectively improve traffic capacity. Moreover, the study results demonstrate that a decrease in the desired time headway and an increase in the maximum platoon size are advantageous for the efficiency of the mixed flow.

Response Time and Time Headway of an Adaptive Cruise Control. An Empirical Characterization and Potential Impacts on Road Capacity

Road vehicles are characterized by increasing levels of automation and it is vital to understand the future impact on transport efficiency. Adaptive Cruise Control (ACC) is one of the first and most common automated functionalities available in privately owned vehicles. The effect of ACC on traffic flow has been widely studied by making assumptions on its operating strategy and on some of its important parameters such as the response time and the desired time headway. In the literature, these parameters are usually set to low values, based on the vehicle controller’s theoretical ability to respond within a very short time frame. Response time is known to be an important parameter in defining the capacity of the road and therefore, assuming a very short response time, studies usually conclude that systems like the ACC will contribute increasing the road capacity significantly. The present study aims at measuring the actual response time of an ACC-enabled vehicle in car-following conditions. A new methodology for the estimation of the controller’s response time and the desired time-gap was developed to this objective. Results show that the response time of the particular ACC controller was in the range 0.8s-1.2s, which is similar to what is commonly assumed for human drivers. In this light, the results of the present study question the common assumption that ACC or other automation technologies necessarily improve traffic flow and increase road capacity.

Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Traffic impact assessment is a key step in the process of work zone planning and scheduling for transportation agencies. Microscopic traffic simulation models enable transportation agencies to conduct detailed analyses of work zone mobility performance measures during the planning and scheduling process. However, traffic simulation results are valid only when the simulation model is calibrated to replicate driver behavior that is observed in the field. Few studies have provided guidance on the calibration of traffic simulation models at work zones and have offered driver behavior parameters that reproduce capacity values that are observed in the field. This paper contributes to existing knowledge of work zone simulation by providing separate driver behavior model parameters for heavy vehicles and passenger vehicles. The driver behavior parameters replicate the flow and speed at the work zone taper and at roadway segments upstream of the work zone. A particle swarm optimization framework is proposed to improve the efficiency of the calibration process. The desired time headway was found to be 2.31 seconds for heavy vehicles and 1.53 seconds for passenger cars. The longitudinal following threshold was found to be 17.64 meters for heavy vehicles and 11.70 meters for passenger cars. The proposed parameters were tested against field data that had not previously been used in the calibration of driver behavior models. The average absolute relative error for flow rate at the taper was 10% and the mean absolute error was 54 veh/h/ln. The GEH statistic for the validation dataset was 1.48.

Autonomous driving in framework of three-phase traffic theory

We discuss a strategy for autonomous driving in the framework of three-phase traffic theory. The main reason for autonomous driving in the framework of the three-phase theory is that an autonomous driving vehicle should learn from driver behaviors in real traffic found in empirical data and explained in the three-phase theory. In contrast with the classical approach to autonomous driving in which at each given speed the vehicle tries to reach a fixed desired time headway, in the autonomous driving strategy based on the three-phase traffic theory there is no fixed time headway to the preceding vehicle.

Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways

Although plenty of studies have been conducted recently about the impacts of cooperative adaptive cruise control (CACC) system on traffic efficiency, there are few researches analyzing the safety effects of this advanced driving-assistant system. Thus, the primary objective of this study is to evaluate the impacts of the CACC system on reducing rear-end collision risks on freeways. The CACC model is firstly developed, which is based on the Intelligent Driver Model (IDM). Then, two surrogated safety measures, derived from the time-to-collision (TTC), denoting time exposed time-to-collision (TET) and time integrated time-to-collision (TIT), are introduced for quantifying the collision risks. And the safety effects are analyzed both theoretically and experimentally, by the linear stability analysis and simulations.The theoretical and simulation results conformably indicate that the CACC system brings dramatic benefits for reducing rear-end collision risks (TET and TIT are reduced more than 90%, respectively), when the desired time headway and time delay are set properly. The sensitivity analysis indicates there are few differences among different values of the threshold of TTC and the length of a CACC platoon. The results also show that the safety improvements weaken with the decrease of the penetration rates of CACC on the market and the increase of time delay between platoons. We also evaluate the traffic efficiency of the CACC system with different desired time headway.

A hybrid traffic flow model with considering the influence of adaptive cruise control vehicles and on-ramps

Recently, autonomous vehicles and the relevant studies have attracted much attention. Adaptive cruise control (ACC), which is a kind of cruise control system for vehicles, automatically adjusts the vehicle speed to maintain a safe distance from vehicles ahead. Since the vehicle with ACC (called ACC vehicles) is semi-autonomous, the performance of ACC vehicle must be quite different from that of manual vehicle. The characteristics of traffic flow with ACC vehicles should be carefully investigated, especially when the traffic system is a bit complicated, such as on-ramp system. The primary objective of this paper is to propose a traffic flow model to simulate the traffic flow with considering the influence of ACC vehicles and on-ramps. Based on the model proposed by Yuan in 2009 [Yuan Y M 2009 Ph. D. Dissertation (Hefei: University of Science and Technology of China)], a hybrid traffic flow model with considering the influence of ACC vehicles and on-ramps is developed. Considering the differences between ACC and manual vehicles, a car-following sub-model based on constant time headway principle is developed for ACC vehicles, while an MCD cellular automata sub-model is proposed for manual vehicles. Besides, a new parameter, , is introduced to show different psychologies of drivers when changing lane from on-ramp to main road. The lane-changing model for vehicles on-ramp is developed as well. At the end, numerical simulation is demonstrated to study the influence of ACC vehicles on traffic flow at on-ramp, and to reveal the influence of parameters on the proposed hybrid model (i.e., the length of merge area, the desired time headway of ACC vehicle and ) on model performance. The results of this paper are as follows. 1) When the ACC vehicles exist in a traffic system, the performance of traffic flow in a on-ramp area is improved: the influence of merged vehicles on main road is reduced, and the average speed and volume are increased. 2) The increase of ACC vehicles can help to alleviate traffic congestion in both congestion duration and scope aspects. 3) The newly proposed hybrid model is sensitive to the length of merge area lw, the desired time headway of ACC vehicle Hd and lane-changing psychology parameter : the decrease of Hd and the increase of can both improve the average speed and volume of traffic flow. In addition, when the volume of on-ramp is small, the speed and volume of main road can be improved by enlarging lw. When the volume of on-ramp is large, a small lw will be better for traffic flow.

Replication of Work Zone Capacity Values in a Simulation Model

Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the default parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. The two most significant car-following parameters and one lane-changing parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.

Adaptive Throttle Control for Automatic Vehicle Following

Electronic throttle control design is an important part of Automated Vehicle Control System (AVCS). In this paper we design an adaptive throttle control scheme for vehicle following under constant time headway spacing policy. The scheme maintains a steady state inter-vehicle spacing decided by a desired time headway set by the driver. The vehicle is assumed to be equipped with sensors which can detect relative distance, relative speed and absolute vehicle speed. The design is based on a simplified linear model but is simulated with the full order nonlinear model and tested on an actual vehicle. The simulated and tested results are included in this paper.