Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

To improve the ride comfort and safety of a traditional adaptive cruise control(ACC)system when the preceding vehicle changes lanes,it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle.First,the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine,and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset.Second,according to the lane-changing intention and collision threat of the preceding vehicle,the target vehicle selection algorithm is studied under three different conditions:safe lane-changing,dangerous lane-changing,and lane-changing cancellation.Finally,the effectiveness of the proposed algorithm is verified in a co-simulation platform.The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver.In the case of a dangerous lane change,the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system;thus,it can effectively avoid collisions and improve the safety of the subject vehicle.

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

A new traffic model on compulsive lane-changing caused by off-ramp

In the field of traffic flow studies, compulsive lane-changing refers to lane-changing （LC） behaviors due to traffic rules or bad road conditions, while free LC happens when drivers change lanes to drive on a faster or less crowded lane. LC studies based on differential equation models accurately reveal LC influence on traffic environment. This paper presents a second-order partial differential equation （PDE） model that simulates both compulsive LC behavior and free LC behavior, with lane-changing source terms in the continuity equation and a lane-changing viscosity term in the momentum equation. A specific form of this model focusing on a typical compulsive LC behavior, the ＇off-ramp problem＇, is derived. Numerical simulations are given in several cases, which are consistent with real traffic phenomenon.

Modelling of vehicle interaction behavior during discretionary lane-changing preparation process on freeway

In order to increase the accuracy of microscopic traffic flow simulation,two acceleration models are presented to simulate car-following behaviors of the lane-changing vehicle and following putative vehicle during the discretionary lanechanging preparation( DLCP) process, respectively. The proposed acceleration models can reflect vehicle interaction characteristics. Samples used for describing the starting point and the ending point of DLCP are extracted from a real NGSIM vehicle trajectory data set. The acceleration model for a lanechanging vehicle is supposed to be a linear acceleration model.The acceleration model for the following putative vehicle is constructed by referring to the optimal velocity model,in which optimal velocity is defined as a linear function of the velocity of putative leading vehicle. Similar calibration,a hypothesis test and parameter sensitivity analysis were conducted on the acceleration model of the lane-changing vehicle and following putative vehicle,respectively. The validation results of the two proposed models suggest that the training and testing errors are acceptable compared with similar works on calibrations for car following models. The parameter sensitivity analysis shows that the subtle observed error does not lead to severe variations of car-following behaviors of the lane-changing vehicle and following putative vehicle.

A new traffic model with a lane-changing viscosity term

In this paper, a new continuum traffic flow model is proposed, with a lane-changing source term in the continuity equation and a lane-changing viscosity term in the acceleration equation. Based on previous literature, the source term addresses the impact of speed difference and density difference between adjacent lanes, which provides better precision for free lane-changing simulation; the viscosity term turns lane-changing behavior to a ＂force＂ that may influence speed distribution. Using a flux-splitting scheme for the model discretization, two cases are investigated numerically. The case under a homogeneous initial condition shows that the numerical results by our model agree well with the analytical ones; the case with a small initial disturbance shows that our model can simulate the evolution of perturbation, including propagation,dissipation, cluster effect and stop-and-go phenomenon.

Effects of Potential Lane-Changing Probability on Uniform Flow

In this paper, we use the car-following model with the anticipation effect of the potential lane-changing probability （Acta Mech. Sin. 24 （2008） 399） to investigate the effects of the potential lane-changing probability on uniform flow. The analytical and numerical results show that the potential lane-changing probability can enhance the speed and flow of uniform flow and that their increments are related to the density.

Development Barriers and Cut-in Points of Next Generation Services

The paper discusses the characteristics of next generation services, and analyzes its development barriers and cut-in points.Broadband services that NGN offers have to transit from an unknown state to an popular state to become a scale economy.Personal information and communication services and multimedia entertainment services will be the cut-in points.

The characteristics of driver lane-changing behaviour in congested road environments

Lane-changing behaviour is one of the complex|driving behaviours.The lane-changing behaviour of drivers may exacerbate congestion,however driver behavioural characteristics are difficult to accurately acquire and quantify,and thus tend to be simplified or ignored in existing lane-changing models.In this paper,the Bik-means clustering algorithm is used to analyse the urban road congestion state discrimination method.Then,simulated driving tests were conducted for different traffic congestion conditions.Through the force feedback system and infrared camera,the data of driver lane-changing behaviours at different traffic congestion levels are obtained separately,and the definitions of the start and end points of a vehicle changing lanes are determined.Furthermore,statistical analysis and discussion of key feature parameters including driver lane-changing behaviour data and visual data under different levels of traffic congestion were conducted.It is found that the average lane-change intention times in each congestion state are 2 s,4 s,6 s and 7 s,while the turn-signal duration and the number of rear-view mirror observations have similar patterns of change to the data on lane-changing intention duration.Moreover,drivers’pupil diameters become smaller during the lane-changing intention phase,and then relatively enlarge during lane-changing;the range of pupil variation is roughly 3.5 mm to 4 mm.The frequency of observing the vehicle in front of the target lane increased as the level of congestion increased,and the frequency of observation in the driver’s mirrors while changing lanes approximately doubled compared to driving straight ahead,and this ratio increased as the level of congestion increased.

Lane changing analysis for two-lane traffic flow

In this paper, the two-lane traffic are studied by using the lane-changing rules in the car-following models. The simulation show that the frequent lane changing occurs when the lateral distance in car following activities is considered and it gives rise to oscillating waves. In contrast, if the lateral distance is not considered （or considered occasionally）, the lane changing appears infrequently and soliton waves occurs. This implies that the stabilization mechanism no longer functions when the lane changing is permitted. Since the oscillating and soliton waves correspond to the unstable and metastable flow regimes, respectively, our study verifies that a phase transition may occur as a result of the lane changing.

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance.Aiming at this for hybrid electric vehicles(HEV)during the car-following(CF)and lane-change(LC)process while accelerating,a hierarchical control strategy for vehicle stability control is proposed.This new approach is different from the conventional hierarchical control.On the basis of model predictive control(MPC)theory,a two-layer MPC controller is designed at the top level of the control structure.The upper layer is a linear time-varying MPC(LTV-MPC),while the lower layer is a hybrid MPC(HMPC).For the LTV-MPC controller,a control-oriented linear discrete model for HEV is established,which integrates the dynamic model with three degrees of freedom(DOF)and the car-following model.The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic(MLD)model of the HEV powertrain.As for the bottom level,a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established.In addition,the system stability is proven.A deep fusion of vehicle dynamics control and energy management is achieved.Compared with LC-ACC control and conventional ACC control,the simulation and the hardware-in-the-loop(HIL)test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions.Additionally,the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.

Research on TDM approach of sharing BRT-lane supported by vehicle-infrastructure integration

In order to increase the availability of the part-time idle bus rapid transit lane(BRT-lane),a time division multiplexing(TDM) method to share BRT-lane with the vehicles besides BRT buses is proposed based on vehicle-road collaboration. The TDM control strategy is established under the circumstance of vehicle-infrastructure integration(VII). The algorithm is given to forecast the segmented BRT travel time. According to the real time traffic information,a comprehensive model is given to estimate the vehicles' lane-changing time from/to the BRTlane to/from its neighbor lane and determine the timing sequence for vehicles collaboration. Finally,the experiment demonstrates that the predicted value of the travel time and lane-changing time is much close to the true value. The control strategy of the vehicles collaboration could promise the non-BRT vehicles to share BRT-lane without disturbing BRT's priority.

Effects of feature selection on lane-change maneuver recognition:an analysis of naturalistic driving data

Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature candidates.Blindly using feature could take up large storage and excessive computation time,while insufficient feature selection would cause poor performance.Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition.This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approach–In total,1,375 LC cases are analyzed.To comprehensively select features,the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid.Then the effect size(Cohen’s d)and p-value of every feature are computed to assess their contribution for each scenario.Findings–It has been found that the common lateral features,e.g.yaw rate,lateral acceleration and time-to-lane crossing,are not strong features for recognition of LC maneuver as empirical knowledge.Finally,cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic.Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/value–In this paper,the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data.The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

Lane-change path planning and control method for self-driving articulated trucks

Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles.A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads.With different steering and braking maneuvers,minimum safe distances were modeled and calculated.Considering safety and ergonomics,the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change.Furthermore,a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability.Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.Findings–The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks.The proposed trajectory model could provide safety lane-change path planning,and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.Originality/value–This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles.There are two main contributions:thefirst is a more quantifiable trajectory model for self-driving articulated vehicles,which provides the opportunity to adapt vehicle and scene changes.The second involves designing a feedback linearization controller,combined with a multi-objective decision-making mode,to improve the comprehensive performance of intelligent vehicles.This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

Traffic dynamics of an on-ramp system with a cellular automaton model

This paper uses the cellular automaton model to study the dynamics of traffic flow around an on-ramp with an acceleration lane. It adopts a parameter, which can reflect different lane-changing behaviour, to represent the diversity of driving behaviour. The refined cellular automaton model is used to describe the lower acceleration rate of a vehicle. The phase diagram and the capacity of the on-ramp system are investigated. The simulation results show that in the single cell model, the capacity of the on-ramp system will stay at the highest flow of a one lane system when the driver is moderate and careful; it will be reduced when the driver is aggressive. In the refined cellular automaton model, the capacity is always reduced even when the driver is careful. It proposes that the capacity drop of the on-ramp system is caused by aggressive lane-changing behaviour and lower acceleration rate.

Lane-changing trajectory prediction based on multi-task learning

As a complex driving behaviour,lane-changing(LC)behaviour has a great influence on traffic flow.Improper lane-changing behaviour often leads to traffic accidents.Numerous studies are currently being conducted to predict lane-change trajectories to minimize dangers.However,most of their models focus on how to optimize input variables without considering the interaction between output variables.This study proposes an LC trajectory prediction model based on a multi-task deep learning framework to improve driving safety.Concretely,in this work,the coupling effect of lateral and longitudinal movement is considered in the L.C process.Trajectory changes in two directions will be modelled separately,and the information interaction is completed under the multi-task learing framework.In addition,the trajectory fragents are clustered by the driving features,and trajectory type recognition is added to the trajectory prediction framework as an auxiliary task.Finally,the prediction process of lateral and longitudinal trajectory and LC style is completed by long short-term memory(LSTM).The model training and testing are conducted with the data collected by the driving simulator,and the proposed method expresses better performance in LC trjectory prediction compared with several traditional models.The results of this study can enhance the trajectory prediction accuracy of advanced driving assistance systems(ADASs)and reduce the traffic accidents caused by lane changes.

High Efficiency Double–Fed Induction Generator Applied to Wind Power Generator Technical Analyses

High efficiency Double-Fed Induction Generator applies new power electronic technology, and utilizes vector control to fix the magnetic direction of the stator to the vertical axis. Adjusting the input current of rotor via an inverter can separately control the cross axis and vertical axis current of real power and reactive power of a generator. Traditionally, rotating speed affects frequency and the output is unstable. This study concentrates on high efficiency Double-Fed Induction Generators and Traditional Generators from mathematic model to derive and control the characteristics simulation and comparison than get an output of high efficiency Double-Fed Industrial Generators. This study utilizes the simulation software MATLAB/Simulink to simulate the response characteristics of vector control of a Double-Fed Industrial Generator. The operating and control functions are better than those of a traditional generator.

Theoretical Study of Wind Turbine Model with a New Concept on Swept Area

Commercially available wind-turbines are optimized to operate at certain wind velocity, known as rated wind velocity. For other values of wind velocity, it has different output which is lower than the rated output of the wind plant. Wind mill can be designed to provide maximum power output at different wind velocities through modification of swept area to match with the wind speed available at the moment. This can result in higher power output at all the velocities except that at rated wind speed because of limitation of generator. This results in increased utilization of generation capacity of wind mill compared to its commercially designed counterpart. A theoretical simulation has been done to prove a new concept about swept area of wind turbine blade which results in a significant increase in the power output through the year. Simulation results of power extracted through normal wind blade design and new concept are studied and compared. The findings of the study are presented in graphical and tabular form. Study establishes that there can be a significant gain in the power output with the new concept.

Intelligent Vehicle Driving Controller Design and Implementation Evaluation

An intelligent vehicle control system is designed and embedded in a Digital Signal Processing （DSP） platform （eZdspTM F2812）. A golf cart is used as an installation platform for the overall system, including steering wheel Alternating Current （AC） serve motor, brake actuator, throttle driving circuit and sensors. Digital image processing technology is also used to enable the autonomous driving system to achieve multi-mode lane-keeping, lane-change and obstacle-avoidance. The overall system is tested and evaluated on a university campus.

Mandatory lane-changing decision making and planning in emergency situations

By considering mandatory lane-changing as a collision avoidance measure,this paper presented the corresponding lane-change decision making and trajectory planning algorithm under an emergency scenario.Different from the traditional algorithm in which lane-change decision making and trajectory planning are separated,they are here coupled in a proposed algorithm and the related parameters are dynamically adjusted in the whole process.In addition to lane-change collision avoidance feasibility analysis,lanechange time instance and duration time are obtained by solving the constrained convex quadratic optimization programme.By taking lane-change time instance and duration time as inputs,the algorithm then proceeded to propose a kinematic model-based highorder polynomial lane change trajectory.By giving the simulation result compassion with the related algorithm,it is proved that the proposed algorithm has a good robustness and high efficiency.

Mandatory lane-changing modelling based on a game theoretic approach in traditional and connected environments

The paper proposes a model of mandatory lane-changing behaviour based on a non-cooperative game in a traditional environment and analyses its applicability in a connected environment.In order to solve the problem of traffic safety and traffic congestion caused by mandatory lane-changing on urban roads,this paper applies the non-cooperative game theory to describe the game behaviour of the two parties,the lane-changing vehicle and the vehicle behind the target lane,in the connected and traditional environments respectively,and constructs the model considering the safety gain,speed gain and lane-changing gain to obtain a game model and the Nash equilibrium solution.Themodel is calibrated and tested using NGSIM data,and the results of the study show that themodel has a good performance for the decision behaviour of lane-changing vehicles and lag vehicles for mandatory lane-changing behaviour on urban roads.