Definition of a merging assistant strategy using intelligent vehicles

In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, we summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies.     

Vehicle platoon formation using interpolating control: A laboratory experimental analysis

This work addresses the formation phase of automatic platooning. The objective is to optimally control the throttle of vehicles, with a given arbitrary initial condition, such that desired ground speed and inter-vehicular spacings are reached. The steering of the vehicles is also controlled, because the vehicles should track a desired path while forming the platoon. In order to address the platoon formation problem, a cooperative strategy is formed by constructing a discrete state space model which represents the dynamics of a set of n vehicles. Once this model is set, a control method known as Interpolating Control, which aims at regulating to the origin an uncertain and/or time-varying linear discrete-time system with state and control constraints, is utilized. The performance of this control method is evaluated and compared with other approaches such as Model Predictive Control (MPC).Simulations are conducted which suggest that the Interpolating Control approach can be seen as an alternative to optimization-based control schemes such as Model Predictive Control, especially for problems for which finding the optimal solution requires calculations, where the Interpolating Control approach can provide a straightforward sub-optimal solution.In the experimental part of this work, the control algorithms for the platoon formation and path tracking problems are combined, and tested in a laboratory environment, using three mobile robots equipped with wireless routers. Validation of the proposed models and control algorithms is achieved by successful experiments.     

Efficient intersection control for minimally guided vehicles: A self-organised and decentralised approach

An important question for the practical applicability of the highly efficient traffic intersection control is about the minimal level of intelligence the vehicles need to have so as to move beyond the traffic light control. We propose an efficient intersection traffic control scheme without the traffic lights, that only requires a majority of vehicles on the road to be equipped with a simple driver assistance system. The algorithm of our scheme is completely decentralised, and takes into full account the non-linear interaction between the vehicles at high density. For vehicles approaching the intersection in different directions, our algorithm imposes simple interactions between vehicles around the intersection, by defining specific conditions on the real-time basis, for which the involved vehicles are required to briefly adjust their dynamics. This leads to a self-organised traffic flow that is safe, robust, and efficient. We also take into account of the driver comfort level and study its effect on the control efficiency. The scheme has low technological barrier, minimal impact on the conventional driving behaviour, and can coexist with the traffic light control. It also has the advantages of being easily scalable, and fully compatible with both the conventional road systems as well as the futuristic scenario in which driverless vehicles dominate the road. The mathematical formulation of our scheme permits large scale realistic numerical simulations of busy intersections, allowing a more complete evaluation of the control performance, instead of just the collision avoidance at the intersection.     

Evaluation of a reactive dynamic route guidance strategy

The aim of the paper is to evaluate the performance of a new strategy which is able to control dynamic route guidance (DRG) systems, mainly in urban road networks. The purpose of this strategy is to achieve dynamic user equilibrium in the network, even in abnormal network conditions, for example when there is an unexpected increase in traffic volume. It is based on feedback concept and it reacts to the traffic conditions observed in real time by adopting a decentralized structure.A series of experiments was performed, by means of a traffic micro-simulator, in a section of an urban road network. In the situations examined, the results seem to be quite positive. The analyses of the link level show that all of the various travel alternatives to reach the destination become more advantageous for users if DRG devices become more widespread among vehicles. In some cases we observe that the strategy succeeds in maintaining the possible alternatives in equilibrium conditions, by distributing users among the feasible turns. At no point in our investigations do we observe an unstable behaviour of the system, even when the number of vehicles fitted with a DRG device increases.     

Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic

Connected Vehicles (CV) equipped with a Speed Advisory System (SAS) can obtain and utilize upcoming traffic signal information to manage their speed in advance, lower fuel consumption, and improve ride comfort by reducing idling at red lights. In this paper, a SAS for pre-timed traffic signals is proposed and the fuel minimal driving strategy is obtained as an analytical solution to a fuel consumption minimization problem. We show that the minimal fuel driving strategy may go against intuition of some people; in that it alternates between periods of maximum acceleration, engine shut down, and sometimes constant speed, known in optimal control as bang-singular-bang control. After presenting this analytical solution to the fuel minimization problem, we employ a sub-optimal solution such that drivability is not sacrificed and show fuel economy still improves significantly. Moreover this paper evaluates the influence of vehicles with SAS on the entire arterial traffic in micro-simulations. The results show that SAS-equipped vehicles not only improve their own fuel economy, but also benefit other conventional vehicles and the fleet fuel consumption decreases with the increment of percentage of SAS-equipped vehicles. We show that this improvement in fuel economy is achieved with a little compromise in average traffic flow and travel time.     

Millimeter-wave communicating-radars for enhanced vehicle-to-vehicle communications

To be effective, safety relevant applications based on wireless communications between vehicles need a minimum rate of vehicles equipped with communication devices. Although this minimum rate of vehicles could be relatively low, it is still difficult to obtain starting from a nonequipped vehicles situation. However, Long and short range radars are becoming very popular these days for cruise control, obstacle detection, parking assistance and pre-crash sensing. These radars are active sensors that produce significant radiofrequency power in wide allocated frequency bands. They also integrate a sensitive receiver. To accelerate the vehicle-to-vehicle communications penetration rate, this paper evaluates the possibility of enhancing vehicle-to-vehicle communications by using communicating-radars working at millimeter-wave. Current allocated frequencies for both vehicle-to-vehicle communication and radars are presented. Short-range and long-range radar radiofrequency parameters are analyzed to verify that existing automotive radar radio standards are consistent with communication. At grazing angles above the road, the characteristics of the communicating-radar propagation channel are theoretically and experimentally studied and compared to a more conventional 5.9 GHz channel. An analysis of Ultra Wide Band radio communication providing simultaneous access to vehicles in the same communication area is presented. Lastly, relevant architectures for communicating-radars are discussed.     

Infrastructure based approach for roadway departure risk assessment

This paper presents a warning device to prevent the roadway departure of light vehicles while cornering. The proposed risk assessment methodology is based on recent works from the authors (Rey et al., 2011b,a; Rey, 2010). Given the random variability arising from the driver, the vehicle and the infrastructure at the entrance to the curve, a probabilistic strategy is adopted to assess the roadway departure risk. The infrastructure-based methodology enables the real curve characteristics to be considered and an alarm triggering decision to be made. Two safety criteria are tested and the potential safety benefits of the proposed warning device are evaluated. Contrary to existing roadway departure warning systems, the proposed approach does not require extra equipment for vehicles; it only requires that the measuring and warning devices be part of the road infrastructure, which is a great advantage in terms of economic cost.     

A control theoretic formulation of green driving strategies based on inter-vehicle communications

Various green driving strategies have been proposed to smooth traffic flow and lower pollutant emissions and fuel consumption in stop-and-go traffic. In this paper, we present a control theoretic formulation of distributed, cooperative green driving strategies based on inter-vehicle communications (IVCs). The control variable is the advisory speed limit, which is designed to smooth a following vehicle’s speed profile without changing its average speed. We theoretically analyze the performance of a constant independent and three simple cooperative green driving strategies and present three rules for effective and robust strategies. We then develop a distributed cooperative green driving strategy, in which the advisory speed limit is first independently calculated by each individual vehicle and then averaged among green driving vehicles through IVC. By simulations with Newell’s car-following model and the Comprehensive Modal Emissions Model (CMEM), we demonstrate that such a strategy is effective and robust independently as well as cooperatively for different market penetration rates of IVC-equipped vehicles and communication delays. In particular, even when 5% of the vehicles implement the green driving strategy and the IVC communication delay is 60 s, the fuel consumption can be reduced by up to 15%. Finally we discuss some future extensions.     

Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control

This contribution furthers the control framework for driver assistance systems in Part I to cooperative systems, where equipped vehicles can exchange relevant information via vehicle-to-vehicle communication to improve the awareness of the ambient situation (cooperative sensing) and to manoeuvre together under a common goal (cooperative control). To operationalize the cooperative sensing strategy, the framework is applied to the development of a multi-anticipative controller, where an equipped vehicle uses information from its direct predecessor to predict the behaviour of its pre-predecessor. To operationalize the cooperative control strategy, we design cooperative controllers for sequential equipped vehicles in a platoon, where they collaborate to optimise a joint objective. The cooperative control strategy is not restricted to cooperation between equipped vehicles. When followed by a human-driven vehicle, equipped vehicles can still exhibit cooperative behaviour by predicting the behaviour of the human-driven follower, even if the prediction is not perfect.The performance of the proposed controllers are assessed by simulating a platoon of 11 vehicles with reference to the non-cooperative controller proposed in Part I. Evaluations show that the multi-anticipative controller generates smoother behaviour in accelerating phase. By a careful choice of the running cost specification, cooperative controllers lead to smoother decelerating behaviour and more responsive and agile accelerating behaviour compared to the non-cooperative controller. The dynamic characteristics of the proposed controllers provide new insights into the potential impact of cooperative systems on traffic flow operations, particularly at the congestion head and tail.     

Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system

As electric vehicles (EVs) have gained an increasing market penetration rate, the traffic on urban roads will tend to be a mix of traditional gasoline vehicles (GVs) and EVs. These two types of vehicles have different energy consumption characteristics, especially the high energy efficiency and energy recuperation system of EVs. When GVs and EVs form a platoon that is recognized as an energy-friendly traffic pattern, it is critical to holistically consider the energy consumption characteristics of all vehicles to maximize the energy efficiency benefit of platooning. To tackle this issue, this paper develops an optimal control model as a foundation to provide eco-driving suggestions to the mixed-traffic platoon. The proposed model leverages the promising connected vehicle technology assuming that the speed advisory system can obtain the information on the characteristics of all platoon vehicles. To enhance the model applicability, the study proposes two eco-driving advisory strategies based on the developed optimal control model. One strategy provides the lead vehicle an acceleration profile, while the other provides a set of targeted cruising speeds. The acceleration-based eco-driving advisory strategy is suitable for platoons with an automated leader, and the speed-based advisory strategy is more friendly for platoons with a human-operated leader. Results of numerical experiments demonstrate the significance when the eco-driving advisory system holistically considers energy consumption characteristics of platoon vehicles.     

Isolated intersection control for various levels of vehicle technology: Conventional,connected, and automated vehicles

Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection.Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.     

Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads

This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper for every HEV, where the higher level and the lower level controller share information with each other and solve two different problems that aim at improving its fuel efficiency. The higher level controller of each HEV is considered to utilize traffic light information, through vehicle to infrastructure (V2I) communication, and state information of the vehicles in its near neighborhood, via vehicle to vehicle (V2V) communication. Apart from that, the higher level controller of each HEV uses the recuperation information from the lower level controller and provides it the optimal velocity profile by solving its problem in a model predictive control framework. Each lower level controller uses adaptive equivalent consumption minimization strategy (ECMS) for following their velocity profiles, obtained from the higher level controller, in a fuel efficient manner. In this paper, the vehicles are modeled in Autonomie software and the simulation results are provided in the paper that shows the effectiveness of the proposed control architecture.     

Vehicular pollution control in Delhi

Delhi is one of the most polluted cities in the world caused by spectacular vehicular growth in the past 2–3 decade. To restore the air quality and refurbish its image, a number of command and control policy instruments have been implemented in Delhi. The paper attempts to investigate whether the enactment of policy instruments and the efforts have led to commensurate fall in air pollution in Delhi. The analysis shows that the imposition has not resulted in concomitant improvement in ambient air quality. One of the reasons is reliance on new vehicles, with little emphasis on in-service vehicles. Even with new vehicles, the focus is on emission limits not on the limit on ambient air quality. With between 370 and 600 new vehicles being registered every day, any expectation of improvement in air quality is far-fetched. The paper concludes that the containment of vehicular pollution requires an integrated approach, with combined use of transport policies and air pollution control instruments.     

Analytical model for information flow propagation wave under an information relay control strategy in a congested vehicle-to-vehicle communication environment

Vehicular traffic congestion in a vehicle-to-vehicle (V2V) communication environment can lead to congestion effects for information flow propagation. Such congestion effects can impact whether a specific information packet of interest can reach a desired location, and if so, in a timely manner to influence the traffic system performance. Motivated by the usefulness and timeliness of information propagation, this paper aims to characterize the information flow propagation wave (IFPW) for an information packet in a congested V2V communication environment under an information relay control strategy. This strategy seeks to exclude information that is dated in the communication buffer under a first-in, first-out queue discipline, from being relayed if the information flow regime is congested. It trades off the need to enable the dissemination of every information packet as far as possible, against the congestion effects that accrue because of the presence of multiple information packets. A macroscopic two-layer model is proposed to characterize the IFPW. The upper layer is formulated as integro-differential equations to characterize the information dissemination in space and time under this control strategy. The lower layer adopts the Lighthill-Whitham-Richards model to capture the traffic flow dynamics. Based on the upper layer model, a necessary condition is derived which quantifies the expected time length that needs to be reserved for broadcasting the information packet of interest so as to ensure the formation of an IFPW under a given density of V2V-equipped vehicles. When the necessary condition is satisfied under homogeneous conditions, it is shown that the information packet can be propagated at an asymptotic speed whose value can be derived analytically. Besides, under the proposed control strategy, only a proportion of vehicles (labeled asymptotic density of informed vehicles) can receive the specific information packet, which can be estimated by solving a nonlinear equation. The asymptotic IFPW speed, the asymptotic density of informed vehicles, and the necessary condition for the IFPW, help in evaluating the timeliness of information propagation and the influence of traffic dynamics on information propagation. In addition, the proposed model can be used to numerically estimate the IFPW speed for heterogeneous conditions, which can aid in the design of traffic management strategies built upon the timely propagation of information through V2V communication.     

基于ADVISOR的并联混动汽车控制策略再开发研究

控制策略开发作为混合动力汽车的核心技术之一,成为了国内外研究的热点。合理的控制策略和方法,对于驱动车辆在不同工况下行驶具有重要意义。本文通过使用ADVISOR软件对并联式混动汽车的控制策略进行再开发,提高工作模式切换的合理性,改善电动机对蓄电池组的充放电性能,提高车辆的续航能力,对于混动汽车的发展具有重要的意义。     

Mobility and environment improvement of signalized networks through Vehicle-to-Infrastructure (V2I) communications

Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.     

A conceptualization of vehicle platoons and platoon operations

Vehicle platooning, a coordinated movement strategy, has been proposed to address a range of current transport challenges such as traffic congestion, road safety, energy consumption and pollution. But in order to form platoons in an ad-hoc manner the vehicles have to ‘speak the same language’, which is in current practice limited to vehicles of particular manufacturers. There is no standard language yet. Also in research, while the current literature focuses on platoon control strategies, intra-platoon communication, or platooning impacts on traffic, the conceptualization of platooning objects and their operations remained unattended. This paper aims to fill this fundamental gap by developing a formal model of platooning concepts. The paper proposes an ontological model of platooning objects and properties and abstract basic building blocks of platoon operations that can then be aggregated to complex platooning behavior. The presented ontological model provides the logical reasoning to support vital decision-making during platoon lifecycles. The ontological model is implemented and demonstrated.     

Real-time merging traffic control for throughput maximization at motorway work zones

Work zones on motorways necessitate the drop of one or more lanes which may lead to significant reduction of traffic flow capacity and efficiency, traffic flow disruptions, congestion creation, and increased accident risk. Real-time traffic control by use of green–red traffic signals at the motorway mainstream is proposed in order to achieve safer merging of vehicles entering the work zone and, at the same time, maximize throughput and reduce travel delays. A significant issue that had been neglected in previous research is the investigation of the impact of distance between the merge area and the traffic lights so as to achieve, in combination with the employed real-time traffic control strategy, the most efficient merging of vehicles. The control strategy applied for real-time signal operation is based on an ALINEA-like proportional–integral (PI-type) feedback regulator. In order to achieve maximum performance of the control strategy, some calibration of the regulator’s parameters may be necessary. The calibration is first conducted manually, via a typical trial-and-error procedure. In an additional investigation, the recently proposed learning/adaptive fine-tuning (AFT) algorithm is employed in order to automatically fine-tune the regulator parameters. Experiments conducted with a microscopic simulator for a hypothetical work zone infrastructure, demonstrate the potential high benefits of the control scheme.     

Characterising Green Light Optimal Speed Advisory trajectories for platoon-based optimisation

Conceptually, a Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles, allowing them to pass through an intersection during the green interval. In previous papers, a single speed is computed for each vehicle in a range between acceptable minimum and maximum values (for example between standstill and the speed limit). This speed is assumed to be constant until the beginning of the green interval, and sent as advice to the vehicle. The goal is to optimise for a particular objective, whether it be minimisation of emissions (for environmental reasons), fuel usage or delay. This paper generalises the advice given to a vehicle, by optimising for delay over the entire trajectory instead of suggesting an individual speed, regardless of initial conditions – time until green, distance to intersection and initial speed. This may require multiple acceleration manoeuvres, so the advice is sent as a suggested acceleration at each time step. Such advice also takes into account a suitable safety constraint, ensuring that vehicles are always able to stop before the intersection during a red interval, thus safeguarding against last-minute signal control schedule changes. While the algorithms developed primarily minimise delay, they also help to reduce fuel usage and emissions by conserving kinetic energy. Since vehicles travel in platoons, the effectiveness of a GLOSA system is heavily reliant on correctly identifying the leading vehicle that is the first to be given trajectory advice for each cycle. Vehicles naturally form a platoon behind this leading vehicle. A time loop technique is proposed which allows accurate identification of the leader even when there are complex interactions between preceding vehicles. The developed algorithms are ideal for connected autonomous vehicle environments, because computer control allows vehicles’ trajectories to be managed with greater accuracy and ease. However, the advice algorithms can also be used in conjunction with manual control provided Vehicle-to-Infrastructure (V2I) communication is available.     

Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance.The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string.Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance of the adaptive driving strategy under V2I cooperation is tested in simulation. Results show that even the estimation of control parameters of non-connected automated vehicles are imperfect and there is mismatch between the model used in analytical derivation and that in simulation, the proposed adaptive driving strategy suppresses disturbances in a wide range of situations.