A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving

In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety.In this paper,we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction,which consists of a driving inference model(DIM)and a trajectory prediction model(TPM).The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network.The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information.To further improve the prediction accuracy and realize uncertainty estimation,we develop a Gaussian process-based TPM,considering both the short-term prediction results of the vehicle model and the driving motion characteristics.Afterward,the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios.The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters.Primarily,the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed,and in which the relationship between the look-ahead time and vehicle velocity is revealed.Then,in order to overcome the external disturbances,parametric uncertainties and time-varying features of vehicles,a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles,which includes an equivalent control law and an adaptive variable structure control law.In this novel automatic steering control system of vehicles,a neural network system is utilized for approximating the switching control gain of variable structure control law,and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time.The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory.Finally,the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Key Technologies in Connected Autonomous Electrified Vehicles

The rapid development of technologies such as artificial intelligence,Internet of Things,big data,and information communication has promoted the development of connected autonomous electrified vehicles with functions such as complex environment perception,intelligent decision-making,collaborative control,and execution.The main objective of this special issue aims at bringing scholars to show their latest research results in environmental perception systems.

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.

Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion

On highways,vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles.To ensure their safety,predicting the sideslip trajectories of such vehicles is crucial.However,the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction.Hence,this study uses a physical model-based approach to predict vehicle sideslip trajectories.Nevertheless,the traditional physical model-based method relies on constant input assumption,making its long-term prediction accuracy poor.To address this challenge,this study presents the time-series analysis and interacting multiple model-based(IMM)sideslip trajectory prediction(TSIMMSTP)method,which encompasses time-series analysis and multi-physical model fusion,for the prediction of vehicle sideslip trajectories.Firstly,we use the proposed adaptive quadratic exponential smoothing method with damping(AQESD)in the time-series analysis module to predict the input state sequence required by kinematic models.Then,we employ an IMM approach to fuse the prediction results of various physical models.The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories.The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios,and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.

Energy Management Optimization Based on Aging Adaptive Functional State Model of Battery for Internal Combustion Engine Vehicles

This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.

Cooperative Lane-Change Maneuver for Multiple Automated Vehicles on a Highway

With the development of vehicle-to-vehicle(V2V)communication,it is possible to share information among multiple vehicles.However,the existing research on automated lane changes concentrates only on the single-vehicle lane change with self-detective information.Cooperative lane changes are still a new area with more complicated scenarios and can improve safety and lane-change efficiency.Therefore,a multi-vehicle cooperative automated lane-change maneuver based on V2V communication for scenarios of eight vehicles on three lanes was proposed.In these scenarios,same-direction and intersectant-direction cooperative lane changes were defined.The vehicle that made the cooperative decision obtained the information of surrounding vehicles that were used to cooperatively plan the trajectories,which was called cooperative trajectory planning.The cooperative safety spacing model was proposed to guarantee and improve the safety of all vehicles,and it essentially developed constraints for the trajectory-planning task.Trajectory planning was treated as an optimization problem with the objective of maximizing safety,comfort,and lane-change efficiency under the constraints of vehicle dynamics and the aforementioned safety spacing model.Trajectory tracking based on a model predictive control method was designed to minimize tracking errors and control increments.Finally,to verify the validity of the proposed maneuver,an integrated simulation platform combining MATLAB/Simulink with CarSim was established.Moreover,a hardware-in-the-loop test bench was performed for further verification.The results indicated that the proposed multi-vehicle cooperative automated lane-change maneuver can achieve lane changes of multiple vehicles and increase lane-change efficiency while guaranteeing safety and comfort.