Safe Headway Research Articles

Cooperative Lane-Change Control Method for Freeways Considering Dynamic Intelligent Connected Dedicated Lanes

Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a waste of road resources. This paper proposes a cooperative lane-changing control method for multiple vehicles considering Dynamic Intelligent Connected (DIC) dedicated lanes. Initially, inspired by the study of dedicated bus lanes, the paper elucidates the traffic regulations for DIC dedicated lanes, and two decision-making approaches are presented based on the type of lane-change vehicle and the target lane: CAV autonomous cooperative lane change and HDV mandatory cooperative lane change. Subsequently, considering constraints such as acceleration, speed, and safe headway, cooperative lane-change control models are proposed with the goal of minimizing the weighted sum of vehicle acceleration and lane-change duration. The proposed model is solved by the TOPSIS multi-objective optimization algorithm. Finally, the effectiveness and advancement of the proposed cooperative lane-changing method are validated through simulation using the SUMO software (Version 1.19.0). Simulation results demonstrate that compared to traditional lane-changing models, the autonomous cooperative lane-changing model for CAVs significantly improves the success rate of lane changing, reduces lane-changing time, and causes less speed disturbance to surrounding vehicles. The mandatory cooperative lane-changing model for HDVs results in shorter travel times and higher lane-changing success rates, especially under high traffic demand. The methods presented in this paper can notably enhance the lane-changing success rate and traffic efficiency while ensuring lane-changing safety.

Nonlinear Analysis of an Extended Heterogeneous Lattice Hydrodynamic Model Considering on/off-Ramps

To study the impact of on/off-ramps on traffic stability in heterogeneous traffic flow, a novel lattice hydrodynamic model was presented. The new model’s stability condition was determined using the linear stability analysis method. The theoretical results reveal that traffic flow stability is influenced by the proportion of vehicles with different maximum speeds and safe headway, as well as the presence of on-ramps and off-ramps to a certain degree. Through the approximate perturbation method, the mKdV equation and the kink–antikink solution of the traffic density at the jam area are obtained. In order to verify the effectiveness and feasibility of the new model, numerical simulations were conducted to demonstrate on/off ramp effect and different proportions of vehicles which possess bigger maximum velocity or safe space headway affect the traffic stability. The numerical results indicate that in heterogeneous traffic flow scenarios, increasing the ratio of vehicles which possess bigger maximum velocity or bigger safe space headway can lead to a deterioration of traffic stability. The effect of on-ramp could cause traffic instability, while the effect of off-ramp is beneficial for easing traffic congestion.

The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

A Decentralized Controller for Platooning of Connected Cars Subject to Swerving Behavior of Motorized Two-Wheelers

Connected vehicles are expected to play a pivotal role in the future of road transportation. One of the major advantages of connected vehicles is their ability to perform cooperative movement, such as platooning, in order to ensure better safety and efficiency. In the near future, connected vehicles will have to co-exist and interoperate with other human-driven vehicles. This necessitates the study of cooperative movement of connected vehicles subject to the behavior of human-driven vehicles. We study a platooning scenario with two different vehicle types: (i) connected cars (CCs) and (ii) human-driven motorized two-wheelers (MTWs). The CCs are automated and can share information with each other, while a human-driven MTW does not have any level of automation. An MTW may either follow the CC ahead, i.e. participate in the platoon, or choose to swerve away. The objective of this paper is to design a decentralized controller for the CCs that can ensure a safe headway with respect to the preceding vehicle, even under the swerving behavior of motorcyclists. The interaction between the CCs and behavior of the MTWs are captured using a random graph. The Algebraic Riccati equation is then used to derive a linear consensus controller that achieves the desired platoon spacing. The proposed design is corroborated through numerical simulations.

Evaluating the Effectiveness of Countermeasures in Improving the Safety of Highway Merging Zones

The merging process from an acceleration lane to the through lane of a motorway is an important aspect at interchanges, both in terms of safety and operating conditions. Several studies have demonstrated that the merging zones of interchanges are the main sources of interference between through and entering vehicles. In this context, incorrectly assessed gaps and headways can lead to severe crashes and traffic delays at interchanges. This study investigates the effectiveness of different countermeasures to improve the safety of merging zones and the ability of through drivers to adopt safe headways to vehicles entering from the motorway on-ramp. Four different countermeasures were tested in a driving simulator study: i) a gap metering signalization, consisting of a Variable Message Sign (VMS) and new pavement markings; ii) a static symbol projected on the vehicle's windshield by means of a Head-Up Display (HUD) that informed the driver to maintain safe headways to the entering vehicle; iii) a dynamic symbol based on Augmented Reality (AR) technology, built into connected vehicle technology, that provided additional visual information to the driver about his current headway to the entering vehicle; iv) the AR system was also tested with an additional audio warning. The driving behavior of forty-four drivers under the four different configurations based on the tested countermeasures was then compared to the driving behavior recorded under a configuration without any countermeasures (baseline condition). The results revealed significant positive effects of all the countermeasures, especially of the AR systems that helped through drivers to adjust their headway to the entering vehicle and thus the most effective solution for improving the safety of the merging zone. This study confirmed the great potential of AR and connected vehicle technologies to improve general safety conditions on the road network, especially in risky situations and difficult maneuvers.

A Platoon-Based Hierarchical Merging Control for On-Ramp Vehicles Under Connected Environment

Connected autonomous vehicle technology is conductive to promoting the transition from traditional merging control (e.g., ramp metering) to automated merging control. This paper proposes a platoon-based hierarchical merging control algorithm for on-ramp vehicles to achieve automated merging control under connected traffic environment. The proposed algorithm optimizes merging maneuvers of on-ramp vehicles to smooth their merging trajectories without frequent decelerations or stops at the end of the ramp, and to minimize disruption to the mainline traffic in the merging zone. A tactical layer controller is designed to select pre-target merging gaps for on-ramp vehicles, in which the future motion (i.e., acceleration and deceleration) of mainline vehicles is considered through the grey prediction model. An operational layer controller is constructed based on model predictive control to adjust the speed of on-ramp vehicles in advance, and controls on-ramp vehicles to merge into the pre-target merging gaps under state constraints (i.e., safe headway, maximum speed and so on). Through numerical simulation, the effectiveness of the proposed algorithm is validated under different merging scenarios. It is shown that on-ramp vehicles smoothly merge into the mainline within the pre-target merging gap at the same speed as adjacent mainline vehicles. Compared with the baseline merging control algorithm, the proposed algorithm significantly reduces both fuel consumption and travel time of on-ramp vehicles, and improves passenger comfort.

Comprehensive Evaluation of Operational Efficiency of Intersections in Arterial Considering Pedestrians Yield Rule

Yield to pedestrians has become a new trend of civilized transportation in the metropolis. In order to evaluate the influence of yield behavior on the comprehensive operation efficiency of signalized intersections in arterial, the efficiency of the subject who gives way and the subject who is given up in the process of yielding to pedestrians was considered in this study, and the comprehensive operation evaluation of intersection in arterial was given. First, based on the rule of pedestrians yield, the concept of a safe headway gap of pedestrians was introduced in the process of conflict analysis of traffic flows at the intersection, and 3 situations were discussed, which are no yield, yield to 1 flow and 2 flows, to calculate the departure rate of traffic flow at the intersection. Furthermore, models of 3 evaluation indices were established, which are number of people passing per unit time, average delay per people, and average yield number per people at intersection. Moreover, the entropy weight method was taken to decide the weight of these 3 indices, and to calculate the comprehensive efficiency evaluation of intersection operation, with the standardized matrix. Finally, case study work was carried out to evaluate the comprehensive efficiency of 4 types of intersections in arterial considering pedestrians yield rule, which is the intersection between arterial and arterial (IAA), intersection between arterial and subarterial (IAS), intersection with one-time (IAA-1, IAS-1), and two-time (IAA-2, IAS-2) crossing of pedestrians. The relevant results show that the impact of an increase in the number of pedestrians on the combined efficiency of arterial intersections can vary dramatically in different scenarios. Therefore, in the implementation process of “yielding to pedestrians,” the flow fluctuation characteristics and channelization of each intersection should be taken into account, and the corresponding phase changes should be based on pedestrian and vehicle volumes to improve the efficiency of all parts, such as pedestrians and drivers.

Safe and sub-optimal CAV platoon longitudinal control protocol accounting for state constraints and uncertain vehicle dynamics

State constraints and uncertain vehicle dynamics severely affect control stability and performance of connected and autonomous vehicle (CAV). To this end, this study puts forward a safe and sub-optimal longitudinal control protocol for CAV platoon with uncertain vehicle dynamics and state constraints. For platoon leader, a second order disturbance observer with L2 stability is presented to estimate lumped uncertainty coupled in vehicle dynamics. By iteratively utilizing control barrier functions and control Lyapunov function, state constraints and speed trajectory tracking stability condition are encoded into control constraints. Based on disturbance observer and encoded constraints, an extended quadratic programming is established as trajectory control law for platoon leader. For platoon followers, backstepping method and disturbance observer accounting for forward communication network are synthesized as formation control law. Besides, conditions of individual vehicle stability and string stability for formation control law are analyzed. Simulation results show that the leader of platoon can automatically switch its drive mode between speed cruising and safe headway keeping, respectively. Furthermore, each follower in platoon can follow its predecessors coordinatively and precisely.

Multi-lane-merging strategy for connected automated vehicles on freeway ramps

Most studies assume the ramp to be a single lane regardless of the possibility of multiple lanes on a freeway ramp. This assumption limits the application of previous ramp metering strategies in the real world. Therefore, this paper proposes a strategy for a centralized controller to efficiently merge connected automated vehicles from a multiple-lane ramp. The proposed strategy aims to allow vehicles from different lanes to pass through the conflict point with the objective of minimum delay and fuel consumption. Numerical experiments are carried out to compare the proposed strategy with first-in-first-out (FIFO) and Vissim built-in strategies. Simulation results indicate that the proposed strategy can reduce more delay than FIFO and Vissim-based strategies. Furthermore, the proposed strategy is also found to be the most reliable in various scenarios with different traffic demand splits, safe headways, and numbers of lanes.

Influences of dynamic safe headway on car-following behavior

In car following states, the reaction of drivers is closely related to the space headway at given speed difference. Unlike the constant velocity difference reaction coefficient in traditional traffic model, this work designed a dynamic function, which can realize the switch between models according to the dynamic headway between vehicles, and developed a new car-following model to investigate the influences of dynamic safe headway (DSH) on car-following behavior. First, the following behavior of vehicle with DSH under urgent situation is simulated to demonstrate that DSH has the ability to prevent the occurrence of collision. Then, the prediction of delay time of car motion and the kinematic wave speed is reasonable and the evolution from free flow to congested flow is well reproduced. In addition, the result of excitation simulation shows that DSH can absorb the disturbance and suppress the propagation of traffic oscillations effectively, which is hard to achieve for traditional traffic model. Finally, the numerical experiment under period condition demonstrate that the new model is conducive to improving driving performance and further to smooth traffic flow. In addition, calibration and validation results show that the empirical data can be modeled with a quantitative agreement.

Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line

A combination of express and local trains (E/L mode) is generally used to operate a suburban rail service, it can meet the rapid and direct service needs of long-distance travelers as well the needs of short-distance travelers. Generally, a stop plan is the core of the E/L mode. A stop plan optimization model in E/L mode, which aims to minimize the total passenger travel time and the number of operating trains during the peak period with the safe headway and departure frequency constraints, is proposed in this study. Meanwhile, an algorithm based on a genetic algorithm is designed to solve the proposed model. A case study of the Jiangjin Line, a suburban railway in Chongqing, China, is carried out. The results show the efficiency and feasibility of the proposed method. The calculation results also show that the total passenger travel time under E/L mode with the overtaking condition is significantly reduced compared with the all-stops (AS) mode and E/L mode without overtaking condition. The superiority of the E/L mode can be enhanced by reducing the dwell time at stations and adopting the overtaking condition.

Safe Platooning Headways on Girder Bridges

Truck platooning—digitally linking two or more trucks to travel in a closely spaced convoy—is an emerging technology with the potential to save fuel and reduce labor. A framework is described to determine how much a platoon permit load might be increased above Federal Bridge Formula B legal limits, given strict control over the load characteristics and operational tactics. Soon, platoons are expected to advance not only with respect to traffic operations but also in their ability to weigh and report axle weight and spacing, functioning as mobile weigh-in-motion vehicles. Consequently, platoon live load statistics (bias and coefficient of variation) can differ from code assumptions, and are perhaps controllable, which poses a significant opportunity with respect to operational strategies. A parametric study is presented that examined safe headways between platooning trucks, considering different girder spacings, span lengths, numbers of spans, types of structure, truck configurations, numbers of trucks, and adjacent lane loading scenarios. The Strength I limit state was evaluated for steel and prestressed concrete I-girder bridges optimally designed using load and resistance factor design. Reliability indices, β, were calculated for each load case based on Monte Carlo simulation. Summary headway guidance was developed and is presented here to illustrate potential safe operational strategies for varying truck weights and platoon live load effect uncertainties.

Connected Vehicle-Cooperative Adaptive Cruise Control Algorithm to Divide and Reform Connected Vehicle Platoons at Signalized Intersections to Improve Traffic Throughput and Safety

The cooperative adaptive cruise control (CACC) algorithm is a simple and effective way to form small-headway platoons so that road capacity and traffic throughput can be improved. The CACC algorithm has been broadly discussed in relation to the highway driving environment where frequent stopping and merging are uncommon. This paper proposes that CACC can also benefit urban arterials, using the appropriate algorithm to predict platoon behavior with optimized trajectories to divide and reform platoons before and after signalized intersections, thus maintaining small, safe headways. Connected vehicle (CV) technology is the key to adapting and improving the CACC algorithm, as it enables the signal phasing plan to be sent to a target CACC platoon and allows vehicles to acquire real-time information from other vehicles in the platoon. In this research, a CV-CACC algorithm is proposed consisting of two functions: platoon division and platoon reforming. The new algorithm is also equipped with acceleration as a new control variable instead of speed, so that the platoon is able to accommodate sharp speed changes around intersections, something the baseline CACC is unable to accommodate. In this study, computer simulations have been conducted to test the reliability of the CV-CACC algorithm and compare its performance against the baseline CACC algorithm.

Investigating the impact of a novel active gap metering signalization strategy on driver behavior at highway merging sections

A safe headway to the lead vehicle is important to reduce conflicts with merging vehicles from highway on-ramps. Previous research has outlined the advantage of gap metering strategies to yield sufficient space to merging vehicles and improve highway capacity during peak hours. However, prevailing gap metering systems fail to indicate the minimum required gap and leave it to the drivers’ judgment to adjust their headway. This paper proposes a new Active Gap Metering (AGM) signalization that helps outer lane drivers to adjust their headway to the lead vehicle when approaching highway ramps with incoming vehicles. This AGM signalization represents a combination of pavement markings and an innovative Variable Message Sign (VMS). The AGM system was tested alone and in combination with additional variable speed limits (VSL) in distinct environments of the Doha Expressway in the State of Qatar using a driving simulator. The driving behavior of 64 drivers was analyzed using repeated-measures ANOVA. The results showed that the AGM effectively influenced the drivers’ behavior on the right stream lane. Drivers did gradually increase the distance to the lead vehicle, which resulted in optimal headways to merging on-ramp vehicles. Most importantly, the minimum time-to-collision (TTCmin) to the merging vehicle was increased by an additional 1–1.5 s as compared to no treatment. The proposed AGM signalization can, therefore, be considered by policymakers to influence drivers’ headways at critical merging sections.

Automated cooperative control of multilane freeway merging areas in connected and autonomous vehicle environments

This study provides an automated cooperative control method in multilane freeway merging areas within the connected and autonomous vehicle (CAV) technology. First, a lane-change model for a mainline vehicle considering the ramp traffic demand is proposed to determine which vehicles should change lanes from the outer lane to the inner lane of the upstream mainline in the merging area. Second, the time window of the on-ramp vehicles entering the mainline is defined based on the safe headway time and acceleration time. Then, a linear programming cooperative decision model is adopted for the merging vehicles in a given time window. A typical scene of a two-lane freeway merging area is analysed, and the results show that, compared with the non-cooperative control method, the proposed method can effectively decrease the total delay and number of stops and increase the outflow rate downstream of the merging bottleneck.

Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving

A model used for velocity control during car following is proposed based on reinforcement learning (RL). To optimize driving performance, a reward function is developed by referencing human driving data and combining driving features related to safety, efficiency, and comfort. With the developed reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. To avoid potential unsafe actions, the proposed RL model is incorporated with a collision avoidance strategy for safety checks. The safety check strategy is used during both model training and testing phases, which results in faster convergence and zero collisions. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset are used to train and test the proposed model. The performance of the proposed model is evaluated by the comparison with empirical NGSIM data and with adaptive cruise control (ACC) algorithm implemented through model predictive control (MPC). The experimental results show that the proposed model demonstrates the capability of safe, efficient, and comfortable velocity control and outperforms human drivers in that it 1) has larger TTC values than those of human drivers, 2) can maintain efficient and safe headways around 1.2s, and 3) can follow the lead vehicle comfortably with smooth acceleration (jerk value is only a third of that of human drivers). Compared with the MPC-based ACC algorithm, the proposed model has better performance in terms of safety, comfort, and especially running speed during testing (more than 200 times faster). The results indicate that the proposed approach could contribute to the development of better autonomous driving systems. Source code of this paper can be found at https://github.com/MeixinZhu/Velocity_control.

Analysis of an extended two-lane lattice hydrodynamic model considering mixed traffic flow and self-stabilization effect

PurposeThe purpose of this paper is to explore the impact of the mixed traffic flow, self-stabilization effect and the lane changing behavior on traffic flow stability.Design/methodology/approachAn extended two-lane lattice hydrodynamic model considering mixed traffic flow and self-stabilization effect is proposed in this paper. Through linear analysis, the stability conditions of the extended model are derived. Then, the nonlinear analysis of the model is carried out by using the perturbation theory, the modified Kortweg–de Vries equation of the density of the blocking area is derived and the kink–antikink solution about the density is obtained. Furthermore, the results of theoretical analysis are verified by numerical simulation.FindingsThe results of numerical simulation show that the increase of the proportion of vehicles with larger maximum speed or larger safe headway in the mix flow are not conducive to the stability of traffic flow, while the self-stabilization effect and lane changing behavior is positive to the alleviation of traffic congestion.Research limitations/implicationsThis paper does not take into account the factors such as curve and slope in the actual road environment, which will have more or less influence on the stability of traffic flow, so there is still a certain gap with the real traffic environment.Originality/valueThe existing two-lane lattice hydrodynamic models are rarely discussed in the case of mixed traffic flow. The improved model proposed in this paper can better reflect the actual traffic, which can also provide a theoretical reference for the actual traffic governance.

A new car-following model on complex road considering driver’s characteristics

In order to explore the influence of driver’s characteristics in complex traffic flow, experienced, inexperienced attribution and the perception headway of the driver are introduced. Concurrently, an extended car-following model is established. The linear stability of the extended model is derived based on the control theory method, and obtains the stability conditions. This work verifies the impact of driver characteristics on traffic flow stability based on the open boundary simulation environment. The research results show that inexperienced driver will reduce the stability of traffic flow on complex roads, while experienced driver will improve the stability of traffic flow. Compared with the driver’s negative perception headway error, the positive perception headway error can improve the stability of traffic flow. More specifically, an experienced driver is good at predicting the state of the preceding vehicle, while the driver’s positive perception headway error tends to narrow the safe headway, and achieve the stability of traffic flow.

Modelling fundamental diagrams according to different water film depths from the perspective of the dynamic hydraulic pressure

In this paper, we propose enhanced fundamental diagrams based on different water film depths by considering the effects of hydroplaning using a physical method. Various factors are calculated to describe the total safe distance headway of main vehicle components. These factors include the driver reaction times, reaction distances, vehicle braking times, and vehicle braking distances corresponding to different water film depths. An excellent match is found between the computed braking distance, the braking time calculated using the proposed numerical model, and the results published in other papers. These calculations are performed to estimate the distance headway and quantitatively analyse the relationships between the speed, density, and water film depth. By using three road-specific parameters estimated by our proposed model, namely, the free-flow speed, jam density, and capacity flow, a link transmission model is developed to analyse the dynamic impact of the water film depth.

Modelling vehicular behaviour using trajectory data under non-lane based heterogeneous traffic conditions

The present study aims to understand the interaction between different vehicle classes using various vehicle attributes and thereby obtain useful parameters for modelling traffic flow under non-lane based heterogeneous traffic conditions. To achieve this, a separate coordinate system has been developed to extract relevant data from vehicle trajectories. Statistical analysis results show that bi-modal and multi-modal distributions are accurate in representing vehicle lateral placement behaviour. These distributions help in improving the accuracy of microscopic simulation models in predicting vehicle lateral placement on carriageway. Vehicles off-centeredness behaviour with their leaders have significant impact on safe longitudinal headways which results in increasing vehicular density and capacity of roadway. Another interesting finding is that frictional clearance distance between vehicles influence their passing speed. Analysis revealed that the passing speeds of the fast moving vehicles such as cars are greatly affected by the presence of slow moving vehicles. However, slow moving vehicles does not reduce their speeds in the presence of fast moving vehicles. It is also found that gap sizes accepted by different vehicle classes are distributed according to Weibull, lognormal and 3 parameter log logistic distributions. Based on empirical observations, the study proposed a modified lateral separation distance factor and frictional resistance factor to model the non-lane heterogeneous traffic flow at macro level. It is anticipated that the outcomes of this study would help in developing a new methodology for modelling non-lane based heterogeneous traffic.