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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Human-like lane-changing trajectory planning algorithm for human-machine conflict mitigation

The purpose of this paper is to alleviate the potential safety problems associated with the human driver and the automatic system competing for the right of way due to different objectives by mitigating the human-machine conflict phenomenon in human-machine shared driving(HMSD)technology from the automation system.Firstly,a basic lane-changing trajectory algorithm based on the quintic polynomial in the Frenet coordinate system is developed.Then,in order to make the planned trajectory close to human behavior,naturalistic driving data is collected,based on which some lane-changing performance features are selected and analyzed.There are three aspects have been taken into consideration for the human-like lane-changing trajectory:vehicle dynamic stability performance,driving cost optimization,and collision avoidance.Finally,the HMSD experiments are conducted with the driving simulator to test the potential of the human-like lane-changing trajectory planning algorithm.The results demonstrate that the lane-changing trajectory planning algorithm with the highest degree of personalization is highly consistent with human driver behavior and consequently would potentially mitigate the human-machine conflict with the HMSD application.Furthermore,it could be further employed as an empirical trajectory prediction result.The algorithm employs the distribution state of the historical trajectory for human-like processing,simplifying the operational process and ensuring the credibility,integrity,and interpretability of the results.Moreover,in terms of optimization processing,the form of optimization search followed by collision avoidance detection is adopted to in principle reduce the calculation difficulty.Additionally,a new convex polygon collision detection method,namely the vertex embedding method,is proposed for collision avoidance detection.

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.

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.

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.

Lane-changing decision rule with the difference of traffic flow's variation in multi-lane highway for connected and autonomous vehicles

Drivers are not far-sighted when they execute lane-changing manipulation.To address this issue,this study proposes a rule to improve vehicles'lane-changing decisions with accurate information of surrounding vehicles(e.g.time headway)-More specifically,connected and autonomous vehicles(CAVs)change lanes in advance if they find severer flow reducing in the lanes,while CAVs should maintain the car-following state if the variations of traffc flow in all lanes have a similar trend.To ilustrate the idea,this study frst calibrates two classic car-following models and a lane-changing model,and then conducts numerical simulations to illustrate the short-sighted decision of drivers.The study incorporates the idea into a lane-changing decision rule by changing the lane-changing model's pa-rameter,and conducts numerical tests to evaluate the effectiveness of the lane-changing decision rule in a multi-lane highway with a bottleneck.The results of this study indicate that the new lane-changing decision rule can substantially improve the throughput of the traffic flow,especially when the inflow exceeds the remaining capacity of the road.The lane-changing rule and results can bring insights into the control of CAVs,as well as the driver assistance system in connected vehicles.

Modelling lane-changing execution behaviour in a connected environment:A grouped random parameters with heterogeneity-in-means approach

Lane-changing is performed either to follow the route to a planned destination(i.e.,mandatory lane-changing)or to achieve better driving conditions(i.e.,discretionary lane-changing).A connected environment is expected to assist during lane-changing manoeuvres,but it is not known well how driving aids in a connected environment assist lane-changing execution.As such,this study investigates the impact of a connected environment on lanechanging execution time during mandatory and discretionary lane-changing manoeuvres.To this end,this study designed an advanced driving simulator experiment where 78 drivers performed these manoeuvres on a simulated motorway in three randomised driving conditions.The conditions were baseline(without driving aids),a fully functioning connected environment with a perfect supply of driving aids,and an impaired connected environment with delayed driving aids.The lane-changing execution time has been modelled by a random parameters hazard-based duration modelling approach,which accounts for the panel nature of data and captures the unobserved heterogeneity.Results suggest that,compared to the baseline condition(i.e.,a non-connected environment),most of the drivers in the connected environment take more time to complete their lane-changing manoeuvres,indicating drivers’safer lane-changing execution behaviour in the connected environment.The communication delay driving condition has been found to have more deteriorating effects on mandatory lanechanging manoeuvres than discretionary lane-changing manoeuvres.This study concludes that(i)the connected environment increases safety margin during both lane-changing manoeuvres,and(ii)a higher magnitude of safety margin is observed during mandatory lane-changing manoeuvres whereby drivers have a higher need for assistance.

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles:cooperative velocity and lane-changing control

Purpose–This study aims to propose an enhanced eco-driving strategy based on reinforcement learning(RL)to alleviate the mileage anxiety of electric vehicles(EVs)in the connected environment.Design/methodology/approach–In this paper,an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed for connected EVs.The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving.Moreover,this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.Findings–To illustrate the performance for the EEDC-HRL,the controlled EV was trained and tested in various traffic flow states.The experimental results demonstrate that the proposed technique can effectively improve energy efficiency,without sacrificing travel efficiency,comfort,safety and lane-changing performance in different traffic flow states.Originality/value–In light of the aforementioned discussion,the contributions of this paper are two-fold.An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs.A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.