Neighboring Lanes Research Articles

Behavioral investigation of stochastic lateral wandering patterns in mixed traffic flow

The vehicles’ lateral movement exhibits inherent stochastic behavior, and when considered collectively, the traffic flow demonstrates certain lateral wandering pattern which serves a fundamental input for pavement design and sustainability analysis. Naturalistic driving trajectories show that the vehicles on neighboring lanes tend to interact laterally, which leads to correlated wandering on a multi-lane road. Compared with human-driven vehicles (HDV), autonomous vehicles (AVs) have a more centered wandering pattern, which accelerates the pavement fatigue. As the market penetration rate of the AVs changes, the wandering pattern of the mixed AVs flow would evolve accordingly, which further influence pavement performances. The evolutionary wandering pattern of mixed AVs flow which considers the interactions between neighboring lanes’ traffic flow is not fully studied yet. This research fills this gap with a stochastic behavioral lateral movement model that bridges the microscopic behavior and lateral wandering pattern. Firstly, a lateral movement model is developed, which assumes that lateral dynamics are influenced by lane markings, road boundaries, intra-lane interaction, and noise. The model is expressed in a stochastic differential form. The solution of the model which gives the lateral wandering is expressed by the Euler-Maruyama scheme, Fokker-Planck equation and Markov chain. Then the model is employed to investigate the wandering patterns under different scenarios. The results show that the model's outputs are consistent with current research results and empirical observations. This research provides additional insights regarding lateral wandering under mixed autonomous vehicle flow, and offers a theoretical tool for future road pavement design.

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles (CAVs) inducing by a moving bottleneck on a two-lane highway. A heuristic rules-based algorithm (HRA) has been used to control the traffic flow upstream of the moving bottleneck. In the HRA, some CAVs in the control zone are mapped onto the neighboring lane as virtual ones. To improve the driving comfort, the command acceleration caused by virtual vehicle is restricted. Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met, the HRA significantly improves the traffic flow: the overtaking throughput as well as the outflow rate increases, the travel delay and the fuel consumption decrease, the comfort level could also be improved.

A deep learning approach for vehicle velocity prediction considering the influence factors of multiple lanes

<abstract> <p>Predicting the future velocity of vehicles is essential for the safety of autonomous driving and the Intelligent Transport System. This study investigates how the surrounding vehicles influence a driving vehicle. Based on the HighD dataset, a scenario that considers the current lane and the neighboring lanes is selected while the drivers' visual angles and visual gap angles along with other parameters in the dataset are characterized as features. To predict the velocity of a driving vehicle and calibrate the influence of surrounding vehicles, a Transformer-based model integrating the features of multiple vehicles is proposed, and different features are added to the layers while constructing the model. Moreover, the information from previous timestamps of the vehicle state is integrated to estimate the duration of the influences, since the influence of an incident is not instantaneous. In our experiments, we find that the duration of the influence on the driving state perfectly fits the driver's reaction time when maneuvers occur in the surrounding vehicles. In addition, we further quantify the importance of the influence on the vehicle velocity prediction based on the Random Forest and obtain some practical conclusions, for instance, the velocity of a vehicle is more influenced by the front vehicle in the left lane than that in the right lane, but is still mainly influenced by the front vehicle in the current lane.</p> </abstract>

The suppressed high density phase in three-lane asymmetric exclusion processes with narrow entrances

This paper studies totally asymmetric simple exclusion processes (TASEPs) on three lanes with narrow entrances. Three types of particles move on three parallel lanes, and changing lanes is forbidden. One particle cannot enter one lane if exit site of the neighboring lane is occupied. Simple mean field approach in which correlation of sites is ignored is firstly carried out in the analysis of the system. It is shown that high density is suppressed in the middle lane, and the other two lanes are always symmetric. In addition, the analysis indicates that there are only four phases in the system, and spontaneous symmetry breaking phenomenon does not exist. Extensive Monte Carlo simulations are carried out to confirm the analysis. However, the simple mean field analytical results of phases boundaries deviate that of simulations in the phase diagram. Motivated by this, cluster mean field approach is carried out, and correlations of vertical cluster sites are considered. It is found that the cluster mean field analysis performs much better than the simple mean field analysis.

A Two-Lane Cellular Automaton Model to Evaluate the Bus Lane with Intermittent Priority

Bus lanes with intermittent priority (BLIPs) are lanes where general traffic is required to give way to approaching buses. BLIPs can improve the reliability of bus services and help maximize the use of road resources. It can be seen as an innovative sharing mobility, such as carsharing, carpooling, and lane sharing. However, implementation of BLIPs has never been feasible until vehicle communications could accommodate the idea. Vehicle-to-vehicle (V2V) communications have broad application prospects in the deployment of BLIPs. This paper develops a two-lane cellular automaton (CA) model to simulate BLIPs and assesses the benefits of connected vehicles for bus operation. In the model, lane-changings are asymmetric with an improved mandatory BLIP lane-changing rule underlying. The effects of BLIPs are explored through numerical simulations, including BLIPs’ impacts on neighboring lanes, travel time saving, fuel consumption, and the punctuality rate of buses. Analysis of traffic flow characteristics of corridors using BLIPs reveals that there is a strong connection among the bus departure interval, clear distance, and road capacity.

A reliability-based approach to evaluate the lateral safety of truck platoon under extreme weather conditions

The truck platoon is one of the most promising connected and autonomous vehicle (CAV) technologies that can reduce fuel consumption and emission, enhance traffic safety, and increase roadway capacity. It is predicted to become mainstream in the next decade. Therefore, it is imperative to fully investigate the safety issues of the truck platoon before its large-scale deployment. However, studies on the lateral safety of the truck platoon under extreme weather, especially crosswinds are still lacking. To fill such a research gap, the current study contributes to the literature by proposing a reliability-based framework to evaluate the safety of the truck platoon regarding incursion into neighboring lanes due to extreme weather, especially crosswinds. The proposed approach involved three main steps: (1) the computational fluid dynamics (CFD) simulation of the aerodynamics of the truck platoon; (2) the truck platoon driving simulation under crosswind; and (3) the advanced response surface model development and the reliability analysis. Four main factors regarding lateral safety of the platoon were considered: wind speed, road friction coefficients, driving speed, and inter-vehicle spacing. The maximum lateral displacement (MLD) was chosen as a measure of lateral safety. The results showed that there was a significant difference between the aerodynamics of a single truck and that of the truck platoon vehicles and the inter-vehicle spacing between trucks within the truck platoon barely influenced the MLD. The MLD was largest for the leading truck as compared to those of the following trucks. The inter-vehicle spacing didn’t have a significant influence on MLD when the inter-vehicle spacing is shorter than 1.5 times of the truck length, while the other factors impacted the MLD significantly. In addition, the support vector regression with the radial basis function outperformed the other response surface functions. Based on reliability analysis, the risk level of the truck platoon was quantified using the safety index, and the impact of contributing factors towards the safety index of the truck platoon was also evaluated. This study confirmed that the proposed framework could be applied to evaluate the lateral safety of the truck platoon. The findings provide important practical implications for the decision-making of transportation management agencies and tailored countermeasures in the CAV) environment.

Investigating lane-changing moves of vehicles departing from signalized junction

ABSTRACT Discretionary Lane-Changing (DLC) possesses different characteristics under various traffic conditions. Therefore, in order to model DLC moves in the correct manner, they need to be approached separately for various conditions. This study has examined the DLC moves of vehicles, which depart from signalized junction. It is observed that a large number of vehicles move to a neighboring lane in order to travel under better traffic conditions, after the traffic lights turn green. There are many parameters, which motivate the vehicles to change lanes at this stage. This study has focused on discovering these parameters. Data was collected through field work for this purpose. Thereinafter, the impact value of each parameter has been discovered using four separate feature selection methods and has been sorted. Fuzzy Cognitive Maps have been developed with the obtained impact values. The models have been validated using the field data and have been compared with each other.

Intelligent Traffic Flow Assist: Optimized Highway Driving Using Conditional Behavior Prediction

Many conventional systems for driver assistance on highways determine vehicle motion based on static situation configurations only. Adaptive Cruise Control systems for example react to vehicles cutting-in from neighboring lanes only after these vehicles have entered the ego-lane. Some more recent systems include prediction of future behaviors, but often neglect mutual influences between these and the best behavior for the ego vehicle. This paper introduces ?intelligent Traffic Flow Assist?, an assistance system that analyzes the current traffic situation and predicts candidate future behaviors conditioned on different options for the ego behavior. It computes vehicle trajectories optimized for safety, comfort and utility for each of the possible future situations and selects the best option for vehicle control. We describe the system architecture and implementation on a prototype vehicle with close-toproduction sensors and report on tests performed in different situations on proving ground and public highways.

Self-Organization When Pedestrians Move in Opposite Directions. Multi-Lane Circular Track Model

When pedestrians walk along a corridor in both directions, a frequently observed phenomenon is the segregation of the whole group into lanes of individuals moving in the same direction. While this formation of lanes facilitates the flow and benefits the whole group, it is believed that results from the actions of the individuals acting on their behalf, without considering others. This phenomenon is an example of self-organization and has attracted the attention of a number of researchers in diverse fields. We introduce and analyze a simple model. We assume that individuals move around a multi-lane circular track. All of them move at the same speed. Half of them in one direction and the rest in the opposite direction. Each time two individuals collide, one of them moves to a neighboring lane. The individual changing lanes is selected randomly. We prove that the system self-organizes. Eventually, each lane is occupied with individuals moving in only one direction. Our analysis supports the belief that global self-organization is possible even if each member of the group acts without considering the rest.

Effect of Imitation Phenomenon on Two-lane Traffic Safety in Fog Weather.

A neighboring lane’s vehicles are potentially important influence factors of traffic safety. In fog weather, drivers will automatically imitate the behaviors demonstrated by other vehicles in the neighboring lane. To illustrate the effect of the imitation phenomenon on traffic safety, this paper develops an extended two-lane car-following model in fog weather. Numerical simulations are carried out to study the effect of imitation on multiple-vehicle collision induced by a sudden stop, as well as perturbation propagation when a small perturbation is added to the uniform traffic flow. The results indicate that the number of collisions depends on the influence coefficient of neighboring lane’s vehicles, sensitivity, headway and initial velocity. Furthermore, the number of crumpled vehicles decreases when the imitation phenomenon is taken into account. In addition, lower vehicular velocity in the neighboring lane can reduce the magnitude of acceleration and fluctuation of headway. The perturbation can be absorbed under certain given conditions regarding the imitation phenomenon. Therefore, traffic safety can be improved by considering the effect of the imitation phenomenon on two-lane traffic flow in fog weather. The findings in this study can provide a theoretical reference for the development of multi-lane intermittent release measures in fog weather.

Traffic Microsimulation for Flexible Utilization of Urban Roadways

The current method of organizing traffic flows in urban networks uses directional right-of-way links to move traffic between urban intersections. Conflict resolution between vehicles is almost exclusively exercised at the intersections, which turns them into bottlenecks of our urban traffic systems. Even an attempt to model a different organization of traffic hits a major barrier, because the traditional simulation models do not offer enough flexibility to model bidirectional traffic on individual links in the network. This paper presents flexible arterial utilization simulation modeling (FAUSIM), a novel microsimulation platform designed to address this deficiency of traditional tools. The outputs from this tool are validated, successfully, in comparison with a commonly utilized Vissim model. The paper then illustrates the ability of FAUSIM to model conventional and unconventional traffic control scenarios. A combined alternate-direction lane assignment and reservation-based intersection control (CADLARIC) scenario is where directional driving paths are altered between neighboring lanes to align vehicles for decreased conflict for left and right turns at intersections where a reservation-based algorithm is utilized to process conflicts. This is compared with a conventional fixed-time (FT) control. The results of the experiments, executed on a small three-intersection corridor, show that CADLARIC significantly outperforms conventional driving with the FT control in relation to traffic efficiency (delays and stops). While the FT control generates fewer (potential) conflicting events, the CADLARIC confidently handles conflicting situations inside and outside the intersections. Future research should further validate the FAUSIM platform and investigate several other unconventional traffic scenarios with connected and automated vehicles.

Time-Optimal Trajectory Planning along Parametric Polynomial Lane-Change Curves with Bounded Velocity and Acceleration: Simulations for a Unicycle Based on Numerical Integration

G2 lane-change path imposes symmetric conditions on the path geometric properties. This paper presents the comparative study of time-optimal velocities to minimize the time needed for traversal of three planar symmetric parametric polynomial lane-change paths followed by an autonomous vehicle, assuming that the neighboring lane is free. A simulated model based on unicycle that accounts for the acceleration and velocity bounds and is particularly simple for generating the time-optimal path parameterization of each lane-change path is adopted. We base the time-optimal trajectory simulations on numerical integration on a path basis under two different end conditions representing sufficient and restricted steering spaces with remarkable difference in allowable maximum curvature. The rest-to-rest lane-change maneuvering simulations highlight the effect of the most relevant path geometric properties on minimal travel time: a faster lane-change curve such as a quintic Bezier curve followed by a unicycle tends to be shorter in route length and lower in maximum curvature to have achievable highest speed at the maximum curvature points. The results have implications to path selection for parallel parking and allow the design of continuous acceleration profile via time scaling for smooth, faster motion along a given path. This could provide a reference for on-road lane-change trajectory planning along a given path other than parametric polynomials for significantly more complex, complete higher-dimensional highly nonlinear dynamic model of autonomous ground vehicle considering aerodynamic forces, tire and friction forces of tire-ground interaction, and terrain topology in real-world.

Safety evaluation of discontinuous lane design of intersection approach in China

ABSTRACTA special kind of road marking design denoted as discontinuous lane design is prevailing in most cities of China, in which a changeover portion between the upstream road segment and the approach of intersection is void of road markings. This article evaluates the road safety and traffic efficiency of the discontinuous lane design for the most typical case that three lanes upstream are followed by four approaching lanes to the intersection. A safety risk comparison was made between the discontinuous lane design and its counterpart continuous lane design by index of postencroachment time (PET). Then, using an ordered probit model, the PETs, representing the severity of traffic conflict, were linked to relevant variables including the traffic volume, the queue length of target lane on the approach, the queue length of the neighboring target lane, the upstream lane used by the vehicle, the vehicle speed before entering the discontinuous zone, and so on. It was found that the upstream lane has an important effect on traffic safety under discontinuous lane design. Continuous lane design is only recommended for intersection approaches with relatively stable right and left turning flows, and not appropriate for specific cases when the turning flows fluctuate significantly in daily periods.

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

The technology of autonomous vehicles is expected to revolutionize the operation of road transport systems. The penetration rate of autonomous vehicles will be low at the early stage of their deployment. It is a challenge to explore the effects of autonomous vehicles and their penetration on heterogeneous traffic flow dynamics. This paper aims to investigate this issue. An improved cellular automaton was employed as the modeling platform for our study. In particular, two sets of rules for lane changing were designed to address mild and aggressive lane changing behavior. With extensive simulation studies, we obtained some promising results. First, the introduction of autonomous vehicles to road traffic could considerably improve traffic flow, particularly the road capacity and free-flow speed. And the level of improvement increases with the penetration rate. Second, the lane-changing frequency between neighboring lanes evolves with traffic density along a fundamental-diagram-like curve. Third, the impacts of autonomous vehicles on the collective traffic flow characteristics are mainly related to their smart maneuvers in lane changing and car following, and it seems that the car-following impact is more pronounced.

Effects of Lane Width, Lane Position and Edge Shoulder Width on Driving Behavior in Underground Urban Expressways: A Driving Simulator Study.

This study tested the effects of lane width, lane position and edge shoulder width on driving behavior for a three-lane underground urban expressway. A driving simulator was used with 24 volunteer test subjects. Five lane widths (2.85, 3.00, 3.25, 3.50, and 3.75 m) and three shoulder widths (0.50, 0.75, and 1.00 m) were studied. Driving speed, lane deviation and subjective perception of driving behavior were collected as performance measures. The results show that lane and shoulder width have significant effects on driving speed. Average driving speed increases from 60.01 km/h in the narrowest lane to 88.05 km/h in the widest lane. While both narrower lanes and shoulders result in reduced speed and lateral lane deviation, the effect of lane width is greater than that of shoulder width. When the lane and shoulder are narrow, drivers in the left or right lane tend to shy away from the tunnel wall, even encroaching into the neighboring middle lane. As the lane or shoulder gets wider, drivers tend to stay in the middle of the lane. An interesting finding is that although few participants acknowledged that lane position had any great bearing on their driving behaviors, the observed driving speed is statistically higher in the left lane than in the other two lanes when the lane width is narrow (in 2.85, 3 and 3.25 m lanes). These findings provided support for amending the current design specifications of urban underground roads, such as the relationship between design speed and lane width, speed limit, and combination form of lanes.

An efficient lane model for complex traffic simulation

AbstractTraffic simulation heavily relies on lane model. This paper presents a novel method to model lanes based on the road axis under the Frenet frame. The road axis is generated from the geographic information system data after curve approximation, discretization, and compression. This lane model couples mileage information with three‐dimensional geometric information, so it offers an easy and fast position transformation from mileage to the Cartesian coordinate. It also keeps strictly consistent for mileage among neighboring lanes so that it facilitates lane‐change processing. Compared with existing methods that depict lanes as simple polylines or curves, the proposed lane model is more functional and more efficient, especially for complex traffic simulation with a large number of lane‐changes. Copyright © 2015 John Wiley & Sons, Ltd.

A Fuzzy Rules-Based Driver Assistance System

A vision-based driver assistance system using fuzzy rules to determine whether warnings are necessary is presented. This system is comprised of four cameras, one of which focuses on the driver for estimating the driver’s viewing angle, another focuses on the road ahead for the detection of road condition ahead, and the last two cameras are on both sides of the vehicle, facing backward, for the purpose of determining whether neighboring lanes are occupied by vehicles hidden in the blind-spots. The system uses fuzzy-rules for the analysis of interactions between the driver’s gaze, whether there are vehicles ahead, and in the neighboring lanes to determine whether the current driving condition should be of concern to the driver and issues one of three levels of warnings, from safe to dangerous.

Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

Objective. We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participantʼs braking intention prior to the behavioral response. Approach. We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not. Main results. Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane). Significance. We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.

Embedded Driver-Assistance System Using Multiple Sensors for Safe Overtaking Maneuver

Advanced driver-assistance systems have recently become one of the most active topics related to intelligent vehicles. Such assistance facilitates vehicular operation by allowing drivers increased control and an enhanced driving experience, and it even comes in the form of automated assistance for autonomous vehicle operation. This paper presents a driver-assistance system that uses a low-cost embedded digital signal processor, with the overall system installed in a commercial vehicle. Based on driving information supplied by multiple sensors, such as a real-time vision system, a vehicle-to-vehicle communication system, and in-vehicle sensors, the proposed system can facilitate decision making and the performing of driving tasks while executing overtaking maneuvers. This paper developed a data fusion stage based on a collision warning algorithm in which the overtaken vehicle and other vehicles in the neighboring lane are accounted for to avoid collisions. The system employs fuzzy control in the steering and speed automation to emulate the driving tasks performed by humans. The applicability of the proposed system was examined in a real-road environment, and a set of experimental results demonstrate the feasibility of using the involved coordination strategies while conducting various driving maneuvers.

Feedback control for car following model based on two-lane traffic flow

In the paper, two-lane traffic flow considering lane changing behaviors has been discussed based on the control theory, and the friction interference which is from the neighbor lane has been taken into account. By using the control method, the stability condition is derived. The feedback signals, which include vehicular information from both lanes, acting on the two-lane traffic system have been introduced into the Full Velocity Difference car-following model. In the end, simulations are conducted to examine the validity and reasonability of the control method. It is proven that lane changing behaviors can aggravate the traffic perturbation. The traffic flow congestion could be suppressed by using the control method and the simulation results are in good agreement with the theoretical analysis.