Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.

Predictive cruise control for heavy trucks based on slope information under cloud control system

With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

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.

Effect of a Traffic Speed Based Cruise Control on an Electric Vehicle’s Performance and an Energy Consumption Model of an Electric Vehicle

This paper proposes a cruise control system(CCS)to improve an electric vehicle's range,which is a significant hurdle in market penetration of electric vehicles.A typical driver or a conventional adaptive cruise control(ACC)controls an electric vehicle(EV)such that it follows a lead vehicle or drives close to the speed limit.This driving behaviour may cause the EV to cruise significantly above the average traffic speed.It may later require the EV to slow down due to the traffic ripples,wasting a part of the EV's kinetic energy.In addition,the EV will also waste higher speed dependent dissipative energies,which are spent to overcome the aerodynamic drag force and rolling resistance.This paper proposes a CCS to address this issue.The proposed CCS controls an EV's speed such that it prevents the vehicle from speeding significantly above the average traffic speed.In addition,it maintains a safe inter-vehicular distance from the lead vehicle.The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment.The simulation environment includes an energy consumption model of an EV,which was developed using data collected from an electric bus operation in London.In the simulation analysis,the proposed system reduced the EV's energy consumption by approximately 36.6%in urban drive cycles and 15.4%in motorway drive cycles.Finally,the experimental analysis using a Nissan e-NV200on two urban routes showed approximately 30.8%energy savings.

A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.

A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System

With the application of mobile communication technology in the automotive industry,intelligent connected vehicles equipped with communication and sensing devices have been rapidly promoted.The road and traffic information perceived by intelligent vehicles has important potential application value,especially for improving the energy-saving and safe-driving of vehicles as well as the efficient operation of traffic.Therefore,a type of vehicle control technology called predictive cruise control(PCC)has become a hot research topic.It fully taps the perceived or predicted environmental information to carry out predictive cruise control of vehicles and improves the comprehensive performance of the vehicle-road system.Most existing reviews focus on the economical driving of vehicles,but few scholars have conducted a comprehensive survey of PCC from theory to the status quo.In this paper,the methods and advances of PCC technologies are reviewed comprehensively by investigating the global literature,and typical applications under a cloud control system(CCS)are proposed.Firstly,the methodology of PCC is generally introduced.Then according to typical scenarios,the PCC-related research is deeply surveyed,including freeway and urban traffic scenarios involving traditional vehicles,new energy vehicles,intelligent vehicles,and multi-vehicle platoons.Finally,the general architecture and three typical applications of the cloud control system(CCS)on PCC are briefly introduced,and the prospect and future trends of PCC are proposed.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Cooperative Adaptive Cruise Control Using Delay-Based Spacing Policy:A Robust Adaptive Non-Singular Terminal Sliding Mode Approach

This study proposes two speed controllers based on a robust adaptive non-singular terminal sliding mode control approach for the cooperative adaptive cruise control problem in a connected and automated vehicular platoon.The delay-based spacing policy is adopted to guarantee that all vehicles in the platoon track the same target velocity profile at the same position while maintaining a predefined time gap.Factors such as nonlinear vehicle longitudinal dynamics,engine dynamics with time delay,undulating road profiles,parameter uncertainties,and external disturbances are considered in the system modeling and controller design.Different control objectives are assigned to the leading and following vehicles.Then,controllers consisting of a sliding mode controller with parameter adaptive laws based on the ego vehicle’s state deviation and linear coupled state errors,and a Smith predictor for time delay compensation are designed.Both inner stability and strong string stability are guaranteed in the case of nonlinear sliding manifolds.Finally,the effectiveness of the proposed controllers and the benefits of 44.73%shorter stabilization time,11.20%less speed overshoot,and virtually zero steady-state inner vehicle distance deviation are illustrated in a simulation study of a seven-vehicle platoon cooperative adaptive cruise control and comparison experiments with a coupled sliding mode control approach.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.

Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

This paper presents a fusion control strategy of adaptive cruise control(ACC) and collision avoidance(CA),which takes into account a driver’s behavioral style. First, a questionnaire survey was performed to identify driver type, and the corresponding driving behavioral data were collected via driving simulator experiments, which served as the template data for the online identification of driver type. Then, the driveradaptive ACC/CA fusion control strategy was designed, and its effect was verified by virtual experiments. The results indicate that the proposed control strategy could achieve the fusion control of ACC and CA successfully and improve driver adaptability and comfort.

Research on Directly Adaptive Fuzzy Algorithm Based ACC Controller

A directly adaptive fuzzy algorithm is applied in vehicle adaptive cruise control system. The basic principle of the adaptive fuzzy algorithm is analyzed. The initial value of the fuzzy based vector is given by the traditional fuzzy membership. Adaptive law of the adjustable parameters θ is also determined. The directly adaptive fuzzy ACC controller is designed based on Matlab fuzzy toolbox. Matlab-Simulink is adopted to test the function of the adaptive fuzzy ACC controller. The control system is established using a 7 DOF vehicle dynamics model. Simulation results indicate that the principle of the method is correct and it performs well both in cruise and distance keeping.

Vehicle Dynamics Modeling and Simulation for ACC

A 7 degree-of-freedom (DOF) 4 wheels vehicle dynamics model based on Matlab-Simulink is established,and 7 DOF vehicle dynamics equations in the form of nonlinear state-space standards are given.The characters of the electronic throttle and the active braking system have been analyzed.And the electronic throttle model and the active braking system model are built according to the test results respectively.Off-line simulation results indicate that the model is suitable for the vehicle adaptive cruise control system,and both of the electronic throttle and the active braking system work in a reasonable way.An adaptive cruise control (ACC) example illustrates that the model has a good performance in cruise and distance keeping.

Design of the pressure regulation algorithm for active braking in vehicle ACC system

To develop the pressure control algorithm for active braking of adaptive cruise control(ACC) system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuators,it is demonstrated that different duty of pulse-width modulation(PWM) signals could control the pressure changing rate of the wheel cylinder.To obtain that signal,a modified proportional-integral-differential(PID) control algorithm is developed using the variable parameter method,the control value reset method,the dead zone method and the integral saturation method.Experimental results show that the delay and overshoot of the pressure response could be reduced considerably using the modified PID algorithm compared with the conventional one.The proposed pressure control algorithm could be used for the further development of the ACC's controller.

Safety aware fuzzy longitudinal controller for automated vehicles

Vehicle automation holds significant opportunities in the efforts for safer road traffic.Even lower levels of automation like adaptive cruise control(ACC)systems have great potential.Apart from safety benefits,ACC systems could improve the efficiency of traffic networks.However,contemporary ACC systems are designed to be comfortable,but not guaranteed to be safe.Moreover,according to recent literature studies,they have been found to be conservative and string unstable,with traffic oscillations being amplified when traveling downstream.Due to those issues,severe problems can arise in the traffic flow and the efficiency of road traffic networks may deteriorate.Automating the vehicles’longitudinal movement through ADAS systems offers new opportunities in improving the safety conditions on the roads by designing more consistent controllers.In the present paper,a fuzzy controller is developed for automated longitudinal vehicle control.The controller is validated through simulation and real-world data of ACC systems that are currently available in the market.Using fuzzy surrogate safety metrics,the controller becomes aware of the safety level of any situation and can react properly.Moreover,the driving behavior is efficient for the traffic flow,comfortable and string stable,dampening traffic shockwaves that travel upstream.Results prove the robustness of the proposed solution.

Hybrid Adaptive Event‑Triggered Platoon Control with Package Dropout

A novel hybrid adaptive event-triggered platoon control strategy is proposed to achieve the balanced coordination between communication resource utilization and vehicle-following performance considering the effect of package dropout.To deal with the disturbance caused by the event-triggered scheme,the parameter space approach is adopted to derive the feasible region from which cooperative adaptive cruise control controller satisfies internal stability,distance accuracy,and string stability.Subsequently,the Bernoulli random distribution process is employed to depict the phenomenon of package drop-out,and the hybrid coefficient is proposed to realize the allocation between the adaptive trigger threshold strategy and the adaptive headway strategy.The simulation of a six-vehicle platoon is carried out to verify the effectiveness of the designed control strategy.Results show that about 78.76%of communication resources have been saved by applying the event-triggered scheme,while guaranteeing the desired vehicle-following performance.And in the non-ideal communication environment with frequent package dropouts,the hybrid adaptive strategy achieves the coordination among communication resource utilization,string stability margin,distance accuracy,and traffic efficiency.

Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability

Purpose–This paper aims to present a cooperative adaptive cruise control,called stable smart driving model(SSDM),for connected and autonomous vehicles(CAVs)in mixed traffic streams with human-driven vehicles.Design/methodology/approach–Considering the linear stability,SSDM is able to provide smooth deceleration and acceleration in the vehicle platoons with or without cut-in.Besides,the calibrated Virginia tech microscopic energy and emission model is applied in this study to investigate the impact of CAVs on the fuel consumption of the vehicle platoon and trafficflows.Under the cut-in condition,the SSDM outperforms ecological SDM and SDM in terms of stability considering different desired time headways.Moreover,single-lane vehicle dynamics are simulated for human-driven vehicles and CAVs.Findings–The result shows that CAVs can reduce platoon-level fuel consumption.SSDM can save the platoon-level fuel consumption up to 15%,outperforming other existing control strategies.Considering the single-lane highway with merging,the higher market penetration of SSDM-equipped CAVs leads to less fuel consumption.Originality/value–The proposed rule-based control method considered linear stability to generate smoother deceleration and acceleration curves.The research results can help to develop environmental-friendly control strategies and lay the foundation for the new methods.

Stability Design for the Homogeneous Platoon with Communication Time Delay

A hierarchical control framework is applied for the distributed cooperative vehicular platoon using vehicular ad-hoc networks.The parameter-space-approach-based cooperative adaptive cruise control(CACC)controller is proposed to guarantee the D-stability and the string stability considering the influence of the communication time delay and time lag of vehicular dynamic performance.This CACC controller combines the feedforward loop of the acceleration of the preceding vehicle with the feedback loop of the following errors,in which the gain of the feedforward loop is designed to decrease matching errors and the gains of the feedback loop are selected from the feasible region in the parameter space.To verify the effectiveness of the CACC controller,a six-vehicle platoon with a simplified vehicular dynamic is simulated under speed-up and stop scenarios.The simulation results demonstrate that the disturbance is attenuated along with the platoon and the following errors are convergent with well-designed convergent performance.A CarSim/Simulink co-simulation is designed to further verify the effectiveness of the hierarchical control framework and the rationality of the CACC controller in the real vehicular platoon application.The simulation results under the highway fuel economy test drive cycle show that the CACC controller improves the drive comfort and significantly decreases the following errors.