Virtual Lanes Research Articles

Second-based queue length estimation with fusing MMW and low penetration rate CAV trajectory data

This paper combines millimeter-wave radar (MMW) data with connected autonomous vehicle (CAV) trajectory data to estimate queue length on a second-by-second basis. Firstly, queued vehicles on multiple lanes with the same traffic movement are mapped to a virtual lane. Then, the presence of CAV or human-driven vehicle (HDV) for any given queueing index is determined. A Bayesian joint probability model is subsequently established for queue length expectation, considering the existence of the queued vehicle type as a condition. The average time headway and dissipation speed distribution are derived from departure timestamps, which allows for the calculation of the prior probability ratio of queue length. Lastly, the maximum likelihood estimation (EM) algorithm is employed for iteratively estimating the CAV penetration rate. Simulation results demonstrate the method offers a compelling trade-off between precision and second-based real-time performance, while field test results further confirm the wide applicability under various traffic conditions.

A Cooperative Planning and Control Method for Intelligent Connected Agricultural Vehicles Considering Virtual Lanes

A Cooperative Planning and Control Method for Intelligent Connected Agricultural Vehicles Considering Virtual Lanes

Closet In-Path Vehicle Detection and Recognition Algorithm Based on Camera and Millimeter-Wave Radar Fusion

<div>The closet in-path vehicle (CIPV) is recognized relying on the detection results for road lane lines in most current ACC system, which may not work well in the poor conditions, for example, unclear road lane lines, low light level, bad weather, and so on. To solve this problem, the article proposes a sensor fusion-based CIPV recognition algorithm independent of road lane lines. First, a robust Kalman filter based on the global coordinate system is designed to fuse the millimeter-wave radar and camera targets. The fusion algorithm can dynamically adjust the covariance matrix of sensor observations to avoid the influence of anomalous observations on the fusion results. Stable detection of targets by the fusion algorithm is the basis of the CIPV recognition algorithm. Then, the CIPV recognition algorithm generates virtual lane lines using the motion parameters of self-vehicle or the driving trajectory of vehicle target and develops a mode switch strategy for virtual lane lines generation based on the driving state of target. This strategy can flexibly switch to the applicable virtual lane lines generation method in different scenarios. Finally, field tests are conducted in typical scenarios to verify the performance of the CIPV recognition algorithm. The results show that the algorithm is able to recognize CIPV stably and accurately without relying on road lane lines.</div>

Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions

In developing countries with limited or no availability of traffic sensors, theoretical delay models are the most commonly used tool to estimate control delay at intersections. The traffic conditions in such countries are characterised by a large mix of vehicle types and limited or no lane discipline (Heterogeneous, Less Lane-Disciplined (HLLD) traffic conditions), resulting in significantly different traffic dynamics. This research develops a queueing theory-based theoretical delay model that explicitly incorporates HLLD traffic conditions’ characteristic features like lack of lane discipline, violation of the First-In-First-Out rule, and a large mix of vehicle types. A new saturation flow-based Passenger Car Equivalent (PCE) estimation methodology to address heterogeneity and a virtual lane estimation approach to address lack of lane-discipline are proposed. The developed model shows 64% lesser error in average control delay estimation compared to the in-practice delay estimation models under HLLD traffic conditions. The developed model is used for signal optimisation under HLLD traffic conditions and reductions of up to 24% in control delay in comparison to the in-practice signal timing approach are observed. The study also highlights the significance of knowing the variation of delay in addition to average delay and presents a simple approach to capture the variation in delay.

A Novel Fast Responding Driver Assistance Technique with Efficient Lane Detection and Collision Avoidance Using Dynamic Feature Extraction in Any Environment

Road accidents caused by a driver's irresponsibility while driving is becoming increasingly common. Furthermore, if lanes are lacking on the road, even well-trained drivers may find it difficult to keep the lane while driving in low-light conditions. As a result, it is critical to design trustworthy, precise, and efficient mechanisms in the vehicle system that aid the driver in the event of a road collision. Almost every country in the world is attempting to conquer this greatest difficulty. The topic study article focuses on the crossroads and presents a more authentic and efficient diver aid system strategy in terms of lane departure alarm even when lanes are missing by taking prior lane patterns into account. In addition, research is being conducted in order to provide a speedy collision warning with nearly no false alarms. This study develops a fast-response spatial domain approach for detecting lanes on highways, and if lane markers are missing, virtual lanes are constructed using a novel suggested algorithm. In addition, for vehicle collision avoidance, the system estimates the distance, velocity, and direction from the frontal vehicle to itself. The suggested approach is evaluated on real-time videos in all environmental circumstances such as poor or bright sunlight, rain, and twilight, as well as on different road geometries such as straight and curving at various vehicle speeds. In every situation, the system has achieved more than 98.7 percent accuracy. Even if there are no markings on road, the system provides more accurate and reliable experimental results. Finally, the results are compared to several existing algorithms based on accuracy, precision, recall, and F1 score, along with processing time.

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

The autonomous vehicles (AVs) need to share the driving environment with the human driving vehicles (HDVs) on expressway in the future. The non-humanlike lane changing (LC) behavior of AVs can mislead human drivers, which brings potential risks. Stronger humanlike ability requires a more complex algorithm. However, the requirement of the on-board vehicle computation resources limits the humanlike ability of LC algorithms. In this context, considering environmental risks, driver speed requirements and driver focus shifting process, this paper proposes a new type of LC algorithm based on artificial potential field (APF) aiming to improve the humanlike ability. The coupling relationship between the longitudinal and the lateral potential field forces is analyzed theoretically based on environmental risk APF. Based on the relationship, we proposed a conversion mechanism of the driver between the lateral and longitudinal potential field forces to mimic the driver’s speed requirement. Then a target lane selection strategy is designed to trade-off the driver’s speed requirement and safety between multiple lanes. Finally, to mimic the driver’s focus transfer process, the lateral forces of the ego vehicle are analyzed theoretically to further propose a moving virtual lane lines algorithm to build the lateral space varying potential field. The algorithm proposed in this paper is validated by comparing with other LC algorithms in the real traffic scenarios. The results indicate that the proposed algorithm has a better ability to mimic human driver’s LC decision-making and provides a smoother and safer humanlike trajectory.

Constraint-tree-driven modeling and distributed robust control for multi-vehicle cooperation at unsignalized intersections

This paper proposes a multi-vehicle cooperation method based on the virtual platoon concept for unsignalized intersections. The method consists of two levels, i.e. constraint-tree-driven modeling (CTM) strategy for the supervision level and distributed robust control (DRC) method for the execution level. After projecting the approaching vehicles to a virtual lane, the CTM strategy determines the conflict-free passing sequence and driving mode and forms multiple local virtual platoons (LVP). A constraint tree is then constructed, in which constraints with a unified equality formation are utilized to describe the geometry topology of all vehicles, regardless of leader or member. Then, a class of robust control is proposed to render each vehicle to follow its prescribed constraint. The control is designed based on third-order nonlinear vehicle dynamics. The uniform boundedness, uniform ultimate boundedness performance of the entire system and string stability of LVP have been proven by leveraging the Lyapunov stability theory. The simulation results confirm that the proposed method functions well under time-varying traffic volume. Furthermore, the proposed method significantly improves the fuel economy, driving comfort and safety under low traffic volume compared with baseline methods.

Roaming the Virtual Lanes: Motorcyclists' Lane Filtering- Splitting Manoeuvres and Exposure to Road Crashes


 
 
 
 There is an urgent need to address motorcycle safety issues, especially in countries with high motorcycle ridership. This paper provides an overview of the research related to motorcycle movements in mixed traffic and their associated crash risk. It also highlights the factors that influence the risk of a crash from the perspective of motorcyclists and other motorists. The potential countermeasures to address the risk factors were also explored and suggestions were presented to improve the current status quo.
 
 
 

Estimation of the effect of motorcycles on the capacity of signalized intersections

O objetivo principal deste artigo é estimar e avaliar o efeito das motocicletas em intersecções semaforizadas. Para isso, foram coletadas variáveis que representam a presença de motocicletas na frente do primeiro veículo na fila, entre os veículos longitudinalmente e entre veículos lateralmente (na faixa virtual). Os resultados mostram que o efeito médio de uma motocicleta posicionada à frente ou entre veículos no tempo perdido inicial está entre 0.3 e 0.7s. No entanto, não houve evidência de que motocicletas na faixa virtual afeta a descarga da fila de veículos. Em relação aos headways de saturação, os resultados indicam que uma motocicleta na frente de um veículo aumenta seu headway de descarga em 0.7 s, um aumento de 30%. Um aumento de 10% na proporção de motocicletas diminui o fluxo de saturação em cerca de 60 veh/h. O fator de equivalência de carros de passeio para motocicletas obtido neste estudo foi de 0.21, considerando apenas as motocicletas posicionadas longitudinalmente entre veículos.

DVL-Lossy: Isolating Congesting Flows to Optimize Packet Dropping in Lossy Data-Center Networks

The performance of lossy data-center networks (DCNs) may degrade due to packet dropping (and possible retransmission) under congestion. In this article, we propose and evaluate a solution to deal with congestion in lossy DCNs, based on the same approach as the dynamic virtual lanes technique, previously proposed for lossless DCNs. This approach consists of isolating congesting flows in special queues, so that they do not share queues with noncongesting ones. This reduces the probability of standard queues becoming congested, thus reducing the dropping (and retransmission) of noncongesting packets and improving network performance. The experiment results confirm that these benefits are achieved by adding just a single special queue per switch port.

BEHAVIOUR CONTROL WITH AUGMENTED REALITY SYSTEMS FOR SHARED SPACES

Abstract. Augmented Reality (AR) in a traffic context has mainly been used in navigation with path augmentation, focused around safely guiding the user with prior knowledge of the route and the destination. Other works are reported to warn drivers by visualizing other traffic participants or dangers, which are yet currently out of sight. However they do not cover aspects of mediating control by recommending users with actions, even when such efforts are expected to foster collaboration in a multiagent environment. To the best of our knowledge, AR has not yet been applied to visualize virtual control information, e.g. virtual lanes or signposts, notably in the context of shared spaces. Such an environment should support spatial understanding of proximate participants with adaptive augmented controls to recommend actions to each user. However when such systems work in context where a conflict of interest would arise, a rule based control logic centered on priority should be accounted for. Traditionally, these rules are defined by traffic management. This paper presents a Behaviour Control with AR (BCAR) Systems based framework for control of user behaviour in a shared space via augmentation and proposes how a control logic can be part of it. The framework which incorporates navigation focuses on mapping users from real to the virtual world .This framework also enables simulations and visualization of multiagent interactions and proposing controls for user actions leveraging the environment complexity reduction achieved through the real to virtual transfer. A prototype implementation of the proposed framework with ARCore and unity3D has been evaluated for pedestrian behaviour control to understand its feasibility.

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing. It is very promising to introduce reinforcement learning in the unsignalized intersection control. However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition. Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem. Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem. Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL. Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment. At last, an intersection simulation platform is developed to evaluate the proposed method. According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.

Multi‐mode human–machine cooperative control for lane departure prevention based on steering assistance and differential braking

Both steering assistance and differential braking have an irreplaceable advantage for man-machine cooperative control of lane departure prevention. To realise the human-machine cooperative control of lane departure prevention with minimal human-machine conflict, a multi-mode human-machine cooperative control algorithm based on steering assistance and differential braking is proposed. Taking the longitudinal velocity and road curvature as input and considering the driver torque, the fuzzy controller used to determine the virtual lane boundary is designed. The control system is divided into the driver control mode, the steering assistance cooperative control mode and the differential braking cooperative control mode by the driver state and the virtual lane boundary width. Next, the model predictive controller is developed to determine the front steering angle in the steering assistance cooperative mode and the additional yaw moment in the differential braking cooperative mode, respectively. Finally, the active disturbance rejection controller is used to determine the assistance torque, and single rear wheel brake control strategy is developed to calculate the wheel braking force. The effectiveness of the proposed multi-mode human-machine cooperative control algorithm is evaluated with numerical simulation and experiments on a human-in-the-loop platform.

Path2SL: Leveraging InfiniBand Resources to Reduce Head-of-Line Blocking in Fat Trees

The number of endnodes in high-performance computing and datacenter systems is constantly increasing. Hence, it is crucial to minimize the impact of network congestion to guarantee a suitable network performance. InfiniBand is a prominent interconnect technology that allows implementing efficient topologies and routing algorithms, as well as queuing schemes that reduce the head-of-line (HoL) blocking effect derived from congestion situations. Here, we explain and evaluate thoroughly a queuing scheme called Path2SL that optimizes the use of the InfiniBand Virtual Lanes to reduce the HoL blocking in fat-tree network topologies.

Towards an integrated longitudinal and lateral movement data-driven model for mixed traffic

Abstract In recent years, there has been an increasing interest in modeling driving behavior in developing countries, where conditions, such as non–lane discipline and heterogeneity in vehicle types, prevail. Traffic flow in such conditions is very complex in nature and safety issues arise. Traffic simulation models have been formulated for lane–based conditions. The existing models do not consider the wider range of situations that drivers in mixed traffic may face compared to drivers in homogeneous lane–based traffic, such as multiple–leader following, and passing and lateral shifts. In this research, we define the concept of virtual lanes for modeling mixed traffic conditions. A methodology based on temporary virtual lanes and data–driven approaches is developed to simulate driving behavior in developing countries. The role of vehicle type in model efficiency is also explored. The proposed methodology is validated on mixed traffic trajectory data from India.

Two-Time-Scale Hybrid Traffic Models for Pedestrian Crowds

This paper introduces new models to describe pedestrian crowd dynamics in a typical unidirectional environment, such as corridors, pathways, and railway platforms. Pedestrian movements are represented in a two-dimensional space that is further divided into narrow virtual lanes. Consequently, pedestrians either move in a lane following each other or change lanes, when it is desirable. Within this framework, the motions of pedestrians are modeled as a two-dimensional and two-time-scale hybrid system. A pedestrian’s movement along the crowd direction is labeled as the $x$ direction and modeled by a real-valued process, a solution of a differential equation in continuous time, the lane change is labeled as the $y$ direction. In contrast to the $x$ direction dynamics, the movements in the $y$ direction only happen at some time epoch. Although the movements are still on the same time horizon as the $x$ direction movements, with a slight abuse of notation and for simplicity and convenience, we use discrete time as the time indicator, and model the movements by a recursive equation taking values in a finite set. Under common assumptions of crowd movements, we prove that the crowd movements in the $x$ direction will converge to a uniform distance distribution and the convergence rate is exponential. Furthermore, by using a velocity-distance function to represent the common crowd and traffic congestion scenarios, we show that all pedestrians will asymptotically move with a uniform group speed. In the $y$ direction, when pedestrians naturally wish to change to faster lanes, we show that the numbers in each virtual lanes converge to a balanced distribution and hence achieves asymptotic consensus as shown typically in a crowd behavior. Stability and convergence analysis is carried out rigorously by using properties of circular matrices, stability of networked systems, and stochastic approximations. Simulation studies are used to demonstrate the main properties of our modeling approach and establish its usefulness in representing pedestrian dynamics.

Traffic Signal Timing Optimization for Heterogeneous Traffic Conditions Using Modified Webster’s Delay Model

Indian traffic is typically composed of different vehicle classes like motorized two-wheelers, three-wheelers, four-wheelers, and non-motorized modes, which vary widely in their static and dynamic characteristics. Such diverse group of vehicle classes sharing of the same right-of-way (ROW) results in a type of traffic behaviour called “gap filling” and “virtual lane”. At signalized intersections, this behaviour is manifested by the Probably-First-In-Probably-First-Out (PFIPFO) queuing behaviour, which has been defined and described in this paper. A literature review of the topic reveals that previous delay models have focused on homogeneous traffic conditions and cannot be used for the heterogeneous traffic conditions in countries like India without significant modifications. Basic Webster’s delay model is among the most widely used around the world, including India, for designing and evaluation of signalized intersections. In this model, the signalized intersection is treated as an M/D/1 queuing system with constant (or deterministic) service times and random arrival times. While this is a reasonable model for lane-disciplined, car-dominated traffic, its suitability for the heterogeneous traffic in India is dubious, as the service times are also rendered random in this case by the differences in vehicle characteristics. Hence an M/M/1 or M/M/N queuing model is more appropriate in such scenario. Considering these points, it is therefore necessary to modify Webster’s basic delay model to make it suitable for heterogeneous, weakly lane-disciplined, saturated traffic conditions in India. In the present study, a new model for random delay component using multichannel queuing theory and PFIPFO queuing behaviour is derived for Indian traffic conditions. The proposed model is validated using the results from a micro-simulation and finally it is used to optimize the traffic signal timing for three intersections in Bangalore city.

Implementation of autonomous driving of a ground vehicle for narrow high-curvature roads using surround view images

In this paper, a new approach for the implementation of autonomous driving of a ground vehicle on narrow and high-curvature roads using a surround view system is proposed. The approach called the independent frame consecutive searching technique is an algorithm which is developed to detect the left and right lane markers of a lane stably and to generate virtual lane marker lines especially when the roads are narrow and have sudden corners and windings with some faded lane markers. Then the steering angle of an autonomous vehicle is calculated so that the vehicle drives along the desired path after deriving the smoothed virtual centerline. In the real-time experiment, the suggested approach achieved robust detection of lane markers using surround view images and drove a test vehicle autonomously with the generated desired paths on such narrow and high-curvature roads with any lane departures. Therefore, this study shows that the method can be applied to the development of autonomous vehicles.

Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections

Connected vehicles will change the modes of future transportation management and organization, especially at intersections. In this paper, we propose a distributed conflict-free cooperation method for multiple connected vehicles at unsignalized intersections. We firstly project the approaching vehicles from different traffic movements into a virtual lane and introduce a conflict-free geometry topology considering the conflict relationship of involved vehicles, thus constructing a virtual platoon. Then we present the modeling of communication topology to describe two modes of information transmission between vehicles. Finally, a distributed controller is designed to stabilize the virtual platoon for conflict-free cooperation at intersections. Numerical simulations validate the effectiveness of this method.

Scalable Deadlock-Free Deterministic Minimal-Path Routing Engine for InfiniBand-Based Dragonfly Networks

Dragonfly topologies are gathering great interest nowadays as one of the most promising interconnect options for High-Performance Computing (HPC) systems. However, Dragonflies contain physical cycles that may lead to traffic deadlocks unless the routing algorithm prevents them properly. In general, existing deadlock-free routing algorithms, either deterministic or adaptive, proposed for Dragonflies, use Virtual Channels (VCs) to prevent cyclic dependencies. However, these topology-aware algorithms are difficult to implement, or even unfeasible, in systems based on the InfiniBand (IB) architecture, which is nowadays the most widely used network technology in HPC systems. This is due to some limitations in the IB specification, specifically regarding the way Virtual Lanes (VLs), which are considered as similar to VCs, can be assigned to traffic flows. Indeed, none of the routing engines currently available in the official releases of the IB control software has been specifically proposed for Dragonflies. In this paper, we present a new deterministic, minimal-path routing for Dragonfly that prevents deadlocks using VLs according to the IB specification, so that it can be straightforwardly implemented in IB-based networks. We have called this proposal D3R (Deterministic Deadlock-free Dragonfly Routing). Specifically, D3R maps each route to a single, specific VL depending on the destination group, and according to a specific order, so that cyclic dependencies (so deadlocks) are prevented. D3R is scalable as it requires only 2 VLs to prevent deadlocks regardless of network size, i.e., fewer VLs than the required by the deadlock-free routing engines available in IB that are suitable for Dragonflies. Alternatively, D3R achieves higher throughput if an additional VL is used to reduce internal contention in the Dragonfly groups. We have implemented D3R as a new routing engine in OpenSM, the control software including the subnet manager in IB. We have evaluated D3R by means of simulation and by experiments performed in a real IB-based cluster, the results showing that, in general, D3R outperforms other routing engines.