A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

Consensus of multi-vehicle cooperative attack with stochastic multi-hop time-varying delay and actuator fault

A consensus-distributed fault-tolerant(CDFT)control law is proposed for a class of leader-following multi-vehicle cooperative attack(MVCA)systems in this paper.In particular,the switching communication topologies,stochastic multi-hop timevarying delays,and actuator faults are considered,which may lead to system performance degradation or on certain occasions even cause system instability.Firstly,the estimator of actuator faults for the following vehicle is designed to identify the actuator faults under a fixed topology.Then the CDFT control protocol and trajectory following error are derived by the relevant content of Lyapunov stability theory,the graph theory,and the matrix theory.The CDFT control protocol is proposed in the same manner,where a more realistic scenario is considered,in which the maximum trajectory following error and information on the switching topologies during the cooperative attack are available.Finally,numerical simulation are carried out to indicate that the proposed distributed fault-tolerant(DFT)control law is effective.

A Multi-Vehicle, Multi-Factory Assignment Problem: A Case of Coca-Cola Bottling Company at Ahinsan and Spintex-Ghana

Determining the type of vehicles to transport goods between multiple factories and numerous distributors with different demands is one of the major logistic decisions that have to be made by industry players to reduce the cost of operations. A Mixed-Integer Quadratic Programming (MIQP) model was used to optimally distribute goods to 105 distributors from two factories across Ghana. The formulated model and analysis show that the existence of multiple vehicles in a fleet purposely for long hauling of goods also renders an optimal minimum cost as compared to a single-vehicle fleet. This optimum minimum cost accounts for 0.2066 of the total cost incurred by the two factories. This resulted in a 25% reduction in transportation cost. Again, a single-vehicle fleet with loading capacity within the mean value of all individual demands gave a minimum cost next to the optimal minimum.

Collaborative multi-lane on-ramp merging strategy for connected and automated vehicles using dynamic conflict graph

The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles’merging and lane-changing behaviors,while ensuring safety and optimizing traffic flow.However,there are few studies that have addressed the merging problem of ramp vehicles and the cooperative lane-change problem of mainline vehicles within a unified framework and proposed corresponding optimization strategies.To tackle this issue,this study adopts a cyber-physical integration perspective and proposes a graph-based solution approach.First,the information of vehicle groups in the physical plane is mapped to the cyber plane,and a dynamic conflict graph is introduced in the cyber space to describe the conflict relationships among vehicle groups.Subsequently,graph decomposition and search strategies are employed to obtain the optimal solution,including the set of mainline vehicles changing lanes,passing sequences for each route,and corresponding trajectories.Finally,the proposed dynamic conflict graph-based algorithm is validated through simulations in continuous traffic with various densities,and its performance is compared with the default algorithm in SUMO.The results demonstrate the effectiveness of the proposed approach in improving vehicle safety and traffic efficiency,particularly in high traffic density scenarios,providing valuable insights for future research in multi-lane merging strategies.

Video-based urban expressway traffic measurement and performance monitoring

This paper presents an urban expressway video surveillance and monitoring system for traffic flow measurement and abnormal performance detection. The proposed flow detection module collects traffic flow statistics in real time by leveraging multi-vehicle tracking information. Based on these online statistics, road operating situations can be easily obtained. Using spatiotemporal trajectories, vehicle motion paths are encoded by hidden Markov models. With path division and parameter matching, abnormal performances containing extra low or high speed driving, illegal stopping and turning are detected in real scenes. The traffic surveillance approach is implemented and evaluated on a DM642 DSP-based embedded platform. Experimental results demonstrate that the proposed system is feasible for the detection of vehicle speed, vehicle counts and road efficiency, and it is effective for the monitoring of the aforementioned anomalies with low computational costs.

Distributed output feedback stationary consensus of multi-vehicle systems in unknown environments

In the traditional distributed consensus of multi-vehicle systems, vehicles agree on velocity and position using limited information exchange in their local neighborhoods. Recently, distributed leaderless stationary consensus has been proposed in which vehicles agree on a position and come to a stop. The proposed stationary consensus schemes are based on all vehicles＇ access to their own absolute velocity measurements, and they do not guarantee this collective behavior in the presence of disturbances that persistently excite vehicles＇ dynamics. On the other hand, traditional distributed disturbance rejection leaderless consensus algorithms may result in an uncontrolled increase in the speed of multi-vehicle system. In this paper, we propose a dynamic relative-output feedback leaderless stationary algorithm in which only a few vehicles have access to their absolute measurements. We systematically design the distributed algorithm by transforming this problem into a static feedback robust control design challenge for the low-order modified model of vehicles with fictitious modeling uncertainties. We further propose dynamic leader-follower stationary consensus algorithms for multi-vehicle systems with a static leader, and find closed-form solutions for the consensus gains based on design matrices and communication graph topology. Finally, we verify the feasibility of these ideas through simulation studies.

Application of artificial neural networks for operating speed prediction at horizontal curves: a case study in Egypt

Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand provided models for predicting operating speeds.However, less attention has been paid to multi-lane highwaysespecially in Egypt. In this research, field operatingspeed data of both cars and trucks on 78 curve sections offour multi-lane highways is collected. With the data, correlationbetween operating speed （V85） and alignment isanalyzed. The paper includes two separate relevant analyses.The first analysis uses the regression models toinvestigate the relationships between V85 as dependentvariable, and horizontal alignment and roadway factors asindependent variables. This analysis proposes two predictingmodels for cars and trucks. The second analysisuses the artificial neural networks （ANNs） to explore theprevious relationships. It is found that the ANN modelinggives the best prediction model. The most influential variableon V85 for cars is the radius of curve. Also, for V85 fortrucks, the most influential variable is the median width.Finally, the derived models have statistics within theacceptable regions and they are conceptually reasonable.

A Precise Information Extraction Algorithm for Lane Lines

Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.

Design of multi-vehicle ad hoc experiment system based on VANets

Vehicular ad hoc networks(VANets) experiment system is studied.Configuration,characteristics and research fields of VANets are introduced briefly.According to the characteristics of VANets,key modules that multi-vehicle ad hoc experiment system needs are designed,and each module's function and practice requirement is analyzed.The reliability of experiment system is tested primarily using three experiment scenes of multi-vehicle ad hoc network,car following and wireless positioning.The experiment result shows that multi-vehicle ad hoc experiment system has the capability of the correlated experiment of VANets.

Resource allocation schemes in multi-vehicle cooperation systems

With the rapid development of smart driving and communications technologies,an increasing number of vehicles are cooperating with each other to improve traffc effciency and travel safety.This paper conducts a comprehensive survey of multi-vehicle cooperation from the aspects of control and communication.Firstly,three typical multi-vehicle cooperation scenarios are summarized.Communication issues relating to multi-vehicle cooperation are then introduced,including communication types,requirements,and potential solutions.To address the control requirements,a general resource allocation solution for multi-vehicle cooperation is formulated;speci cally,two types of resource allocation scheme for intersection management are proposed.Finally,performance of the proposed schemes is evaluated and compared.

A practical multi-lane factor model of bridges based on multi-truck presence considering lane load disparities

Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over multiple lanes.However,these disparities are not considered in current specifications.To address this drawback,a multi-coefficient MLF model was developed based on an improved probabilistic statistical approach that considers the presence of multiple trucks.The proposed MLF model and approach were calibrated and demonstrated through an example site.The model sensitivity analysis demonstrated the significant influence of lane disparity of truck traffic volume and truck weight distribution on the MLF.Using the proposed approach,the experimental site study yielded MLFs comparable with those directly calculated using traffic load effects.The exclusion of overloaded trucks caused the proposed approach,existing design specifications,and conventional approach of ignoring lane load disparity to generate comparable MLFs,while the MLFs based on the proposed approach were the most comprehensive.The inclusion of overloaded trucks caused the conventional approach and design specifications to overestimate the MLFs significantly.Finally,the benefits of the research results to bridge practitioners were discussed.

Hybrid Clustering Algorithms with GRASP to Construct an Initial Solution for the MVPPDP

Mobile commerce(m-commerce)contributes to increasing the popularity of electronic commerce(e-commerce),allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time.As demand for e-commerce increases tremendously,the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction.One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem(MVPPDP),where a selected set of pickup and delivery customers need to be served within certain allowed trip time.In this paper,we proposed hybrid clustering algorithms with the greedy randomised adaptive search procedure(GRASP)to construct an initial solution for the MVPPDP.Our approaches first cluster the search space in order to reduce its dimensionality,then use GRASP to build routes for each cluster.We compared our results with state-of-the-art construction heuristics that have been used to construct initial solutions to this problem.Experimental results show that our proposed algorithms contribute to achieving excellent performance in terms of both quality of solutions and processing time.

A case study on multi-lane roundabouts under congestion:Comparing software capacity and delay estimates with field data

Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for intersections with potential growth i~ future traffic, there has been a lack of existing studies with field data that provide reference values in terms of capacity and delay measurements. With the intent of providing such reference values, a case study was conducted by using the East DowlinC Road Roundabouts in Anchorage, Alaska, which are currently operating with extensive queues during the evening peak hours. This research used multiple video camcorders t（ capture vehicle turning movements at the roundabouts as well as the progressior~ of vehicle queues at the roundabout entrance approaches. With these video records, the number of vehicles in the queues can be accurately counted in any single minute during the peak hours. This study shows that unbalanced entrance flow patterns （i.e., ~ne entrance has significant higher flow than others） can intensify the queue and delay fo., the overall roundabouts. Then various software packages including RODEL, SIDRA and VISSIM were used to estimate several performance measurements, such as capacity. queue length, and delay, compared with the collected field data. With the comparison, it is found that all the three software packages overestimate multi-lane roundabout ca pacity before calibration. With default parameters, SIDRA and VISSIM tend to underes timate delays and queue lengths for the multi-lane roundabouts under congestion, while RODEL results in higher delay and queue length estimations at most of the entrance approaches.

Deep Reinforcement Learning Based Decision‑Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments

Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed.First,the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk.Second,a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning.Finally,the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles.The proposed framework is validated in both low-density and high-density traffic scenarios.The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.

Ranging-aided relative navigation of multi-platforms

This paper proposes a relative attitude and distance estimation algorithm based on pairwise range measurements between vehicles as well as inertial measurement of each platform. The solution of Wahba＇s Problem is introduced to compute the relative attitude between multi-platforms with the sampled pairwise ranges, in which the relative distance estimation is derived and the estimation error distributions are analyzed. An extended Kalman filter is designed to fuse the estimated attitude and distance with the inertial measurement of each platform. The relative poses between platforms are determined without any external aided measurement. To show this novelty, a real testbed is constructed by our research lab. And the experiment results are positive.

Steady formation analysis onmulti-robot systems

In this paper,a controller is designed for a group of cooperative robots to achieve the stable displacements.Communication network among the robots is assumed unavailable and each robot can only measure the displacements relative to their neighbours.The controller will enable the robots to move to a predefined formation or a special formation under a suitable protocol which we will design.We will discuss the effect of different sizes of the multi-vehicle system and conduct simulations to verify the effectiveness of the proposed controller and the formation protocols.

OF MOTORBIKESAND MEMORIES

The beeping of horns and revving of engines meld into the background of our conversation. Hurtling through the multi-lane traffic, wind roaring at 30 kilometers per hour, with no barrier between my skin and the outside world, I did not anticipate that a motor-taxi driver in Ho Chi Minh City would become my primary teacher on the history and identity of the Chinese Vietnamese, or Hoa （华, ＂Chinese＂）.