Intelligent Cruise Control Research Articles

Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes

Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.

Identifying suitable car-following models to simulate automated vehicles on highways

This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, Intelligent Driver Model (IDM), Improved IDM (IIDM), IIDM with Constant-Acceleration Heuristic (CAH), and MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) are examined with the state-of-the-art vehicle trajectory data, Highway Drone dataset (HighD), and genetic algorithm. There is no commercial level 5 AV or CAV as of 2022; therefore, the authors generate hypothetical AV trajectories based on the actual vehicle trajectories and the assumption of an ideal AV. Based on the analysis, the calibrated IIDM with CAH shows the most fit on AV behavior.

Intelligent Adaptive Cruise Control (IACC): A CASE STUDY

Intelligent Adaptive Cruise Control (IACC): A CASE STUDY

The Bionics and its Application in Energy Management Strategy of Plug-in Hybrid Electric Vehicle Formation

A novel distributed cooperative formation control method inspired by the aggregate behaviors of fish groups is proposed for plug-in hybrid electric vehicle (PHEV) formation. Firstly, a hierarchical control architecture is established for formation keeping with fuel consumption (FC) optimization simultaneously. The top layer is to generate a leader with the optimal performance based on the nonlinear model predictive control (MPC) technique. A fish swarm optimization (FSO) algorithm is proposed to solve the nonlinear MPC problem by imitating the predation behaviors of the fish swarm. The middle layer is a decentralized intelligent cruise control (ICC) for follower vehicles to track their leader imitated the behaviors of fish swarm, and some design criteria are presented based on the Lyapunov stability theory. The under layer is to achieve a satisfying performance for the hybrid powertrain systems of followers. Finally, the bio-inspired method applied for PHEV formation is verified with a satisfying robustness, fuel economy, car-following and also real-time processing performances. According to the results, the PHEV formation using the proposed method represents a better car-following performance compared with the normal adaptive cruise control (ACC) method and a 21.26% improvement of FC compared with the rule-based energy management strategy (EMS). The computational burden is also reduced by the bio-inspired method.

Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review

This study provides a literature review of the simulation-based connected and automated intelligent-vehicle studies. Media and car-manufacturing companies predict that connected and automated vehicles (CAVs) would be available in the near future. However, society and transportation systems might not be completely ready for their implementation in various aspects, e.g., public acceptance, technology, infrastructure, and/or policy. Since the empirical field data for CAVs are not available at present, many researchers develop micro or macro simulation models to evaluate the CAV impacts. This study classifies the most commonly used intelligent-vehicle types into four categories (i.e., adaptive cruise control, ACC; cooperative adaptive cruise control, CACC; automated vehicle, AV; CAV) and summarizes the intelligent-vehicle car-following models (i.e., Intelligent Driver Model, IDM; MICroscopic Model for Simulation of Intelligent Cruise Control, MIXIC). The review results offer new insights for future intelligent-vehicle analyses: (i) the increase in the market-penetration rate of intelligent vehicles has a significant impact on traffic flow conditions; (ii) without vehicle connections, such as the ACC vehicles, the roadway-capacity increase would be marginal; (iii) none of the parameters in the AV or CAV models is calibrated by the actual field data; (iv) both longitudinal and lateral movements of intelligent vehicles can reduce energy consumption and environmental costs compared to human-driven vehicles; (v) research gap exists in studying the car-following models for newly developed intelligent vehicles; and (vi) the estimated impacts are not converted into a unified metric (i.e., welfare economic impact on users or society) which is essential to evaluate intelligent vehicles from an overall societal perspective.

Approach of Coordinated Control Method for Over-Actuated Vehicle Platoon based on Reference Vector Field

Collaborative vehicle platoon control with full drive-by-wire vehicles with four-wheel independent driving and steering (FWIDSV) has attracted broader research interests. However, the problem of cooperative vehicle platoon control in two-dimensional driving scenes remains to be solved. This paper proposes a coupling control method for path tracking and spacing-maintaining based on the reference vector field (RVF). An integrated hierarchical control structure, including the following control layer, tire force allocator layer, and an actuator controlling layer for FWIDSV is presented. Inside, the next control layer was designed according to the spacing control strategy and RVF within the limitation of the friction circle. For verifying the effectiveness of this control method, sufficient conditions for error convergence are analyzed when considering the influence of the critical parameters on the particle dynamics model. The tire force allocator layer is designed based on linear quadratic programming (LQP), which is used to distribute the total forces and yaw moment. The sliding mode control (SMC) is employed to track the desired tire forces in the actuator controlling layer. The proposed control methods are validated through simulation in intelligent cruise control (ICC) and platoon merging scenarios. The results demonstrate an effective FWIDSV platoon control approach that is based on the RVF in the 2-D driving scenes.

Closed Loop Control for a Brushless DC Motor to Run at the Exactly Entered Speed

The project is designed to control the speed of a BLDC motor using closed loop control technique. BLDC motor has various application used in industries like in drilling, lathes, spinning, elevators, electric bikes etc. The speed control of the DC motors is very essential. This proposed system provides a very precise and effective speed control system. The user can enter the desired speed and the motor will run at that exact speed. Based on the principle of PWM speed can be controlled. This is achieved by keeping BLDC motor on closed loop feedback by giving RPM reference to the microcontroller upon a shaft mounted IR reflection arrangement. The pulse width modulation would be automatically adjusted to maintain the DC power to the motor such that entered speed percentage matches the RPM. An LCD duly interfaced to the microcontroller to display the running speed. The desired speed in percentage of full speed is fed with the help of keypad. The controller delivers desired pulse width to automatically adjust the DC power to the motor for required speed. The above operation is carried out by using one thyristor for driving the BLDC motor with IR sensing for getting the speed reference to the microcontroller. The power supply consists of a step down transformer 230/12V, which step down the voltage to12V AC. This is converted to DC using a Bridge rectifier and it is then regulated to +5V using a voltage regulator. Which is required for the operation of the microcontroller and other components Further the project can be enhanced to a fully-fledged fuzzy logic control of a BLDC motor for industrial applications. It can also be developed for an intelligent cruise control used in modern auto mobiles these days..

Model-Free Optimal Control Based Intelligent Cruise Control with Hardware-in-the-Loop Demonstration [Research Frontier

It is difficult to implement optimal control for a system whose model is unknown and operation environment is uncertain, such as the intelligent cruise control of vehicles. This article will address the problem from the perspective of reinforcement learning by learning the optimal policy from the state transition data. The model-free optimal control algorithm is employed to approximate the optimal control policy for the intelligent cruise control system, which considers the comfort performance and the safety performance comprehensively by setting up a total performance index. The algorithm is implemented by two multi-layer neural networks which are the critic network and the actor network. The critic and actor networks are employed to approximate the stateaction value function and the control action, respectively. In addition, a data collecting strategy is proposed to obtain the state transition data distributed uniformly in the state action space from the running trajectory of the host car. The critic network and the action network are trained alternatively by the collected data until converging. The convergent action network is used to obtain the optimal control policy. At last, the policy is tested on a hardware-in-the-loop simulator built upon dSPACE by comparing with a linear quadratic regulator (LQR) controller and a proportion integration differentiation (PID) controller. Results show its excellent performance on both aspects of the safety and the comfort.

Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles

Energy use in internal combustion engine passenger vehicles contributes directly to CO 2 emissions and fuel consumption, as well as producing a number of air pollutants. Optimizing the vehicle velocity by utilising upcoming road information is an opportunity to minimize vehicle energy use without requiring mechanical design changes. Dynamic programming is capable of such an optimization task and is shown in simulation to produce fuel savings, on average 12%, compared to real driving data; however, in this paper it is also applied in real time on a Raspberry Pi, a low cost miniature computer, in situ in a vehicle. A test drive was undertaken with driver feedback being provided by a dynamic programming algorithm, and the results are compared to a simulated intelligent cruise control system that can follow the algorithm results precisely. An 8% reduction in fuel with no loss in time is reported compared to the test driver.

Business opportunities in automated vehicles

AV status Where are we in the development and deployment of AVs? Two types of first generation AVs are already commercially available. First, special-purpose, low-speed AVs are already being marketed. Examples include fully automated heavy haulers for use in resource extraction. Suncor has announced that it is buying 175 of these from Komatsu for use in the Alberta oil sands. Another example of special-purpose AVs is EasyMile and Navya, both based in Europe, which have both launched their second-generation, fully automated, electric shuttle buses. Second, cars with partially autonomous capability are also commercially available now. High-end cars, such as the Mercedes S-Class, Infiniti Q50, and Tesla, include intelligent cruise control, lane centering, self-parking, and pedestrian detection and automatic braking. These are often called Advanced Driver Assistance Systems (ADAS). As the car manufacturers add more features, the vehicles will evolve to become fully autonomous. As we look ahead, there are two major milestones to be achieved. First, 2020 is the year that fully autonomous cars will be available in showrooms. Some manufacturers may say a year earlier or a year later, but 2020 is a reasonable milestone. It is expected that the penetration of AVs will happen fairly quickly, especially given the expected trend to mobility-as-a-service (MaaS). Under MaaS, there will be a trend toward using driverless taxis, resulting in a decrease in private car ownership. This will accelerate the switchover from human-driven cars to AVs. The second major milestone will likely be 2025, which will be a tipping point for the deployment of AVs. The trend to AVs and MaaS should be significant and will accelerate significantly from there. A key trend as we look ahead is the existence of three vehicle technologies that are currently being developed. Automated vehicle technology will converge with connected vehicle technology and electric vehicle technology to create what I call ACE vehicles: automated, connected, and electric. Individually, each brings substantial improvements to vehicle operation, safety, convenience, and the environment. Together, ACE vehicles will be very disruptive to our lives, our cities, society, and our world.

A Comparison of two Predictive Approaches to Control the Longitudinal Dynamics of Electric Vehicles

In this contribution we compare two different approaches to the implementation of a Model Predictive Controller in an electric vehicle with respect to the quality of the solution and real-time applicability. The goal is to develop an intelligent cruise control in order to extend the vehicle range, i.e. to minimize energy consumption, by computing the optimal torque profile for a given track. On the one hand, a path-based linear model with strong simplifications regarding the vehicle dynamics is used. On the other hand, a nonlinear model is employed in which the dynamics of the mechanical and electrical subsystem are modeled.

Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System

In order to improve the active safety driving vehicle and alleviate the intension of driving fatigue, an intelligent control strategy of automobile cruise is put forward based on the throttle or braking pedal combined control adopting the fuzzy control theory. A fuzzy logic controller is presented, which consists of the two input variables, the deviation of the theoretical safe distance and relative distance and the relative velocity between the preceding vehicle and the cruise vehicle, and the single output variable, that is, the throttle opening or the braking pedal travel. Taking the test data of 1.6 L vehicle with auto-transmission as an example, the function on the intelligent cruise control system is simulated adopting MATLAB/Simulink aiming at different working conditions on the city road. The simulation results show that the control strategy possesses integrated capability of automated Stop & Go control, actively following the preceding vehicle on the conditions of keeping the safety distance and the constant velocity cruise. The research results can offer the theory and technology reference for setting dSPACE type and developing the integrated control product of automobile cruise system.

A Stochastic Car Following Model

Car-following models are an essential part of microscopic traffic simulations. For research regarding traffic safety, traffic simulations need to simulate traffic safety related aspects realistically. That means, for example, accidents and near accidents shall occur in the same quantity and in the same way as in reality. Such simulations can be used to make statements about traffic safety with respect to traffic influencing factors and conditions. However, most car-following models are deterministic and do not incorporate uncertainty and fluctuations of perception and behavior. Also, they are explicitly conflict free. Therefore, they are unsuitable for simulating traffic in the desired way. For that reason, a car following model fulfilling the requirements above was developed.Inspired by [1], our car-following model is nondeterministic and data-driven. It is based on the data set of the Intelligent Cruise Control Field Operational Test [2], in which instrumented vehicles have been used by 108 voluntary drivers for several weeks yielding trajectories of approximately 88,000 driving kilometres. In our model, the acceleration a of a vehicle depends on the following input: -v, the velocity of the vehicle, -∆v, the difference between the velocity of the preceding vehicle and its own velocity, -g, the gap between the vehicle and the preceding vehicle and -aL, the acceleration of the preceding vehicle. The acceleration a underlies a probability distribution that depends on v, ∆v, g and aL. That means, if v*, ∆v*, g* and aL* are the current values for v, ∆v, g and aL, then there exist a probability distribution F(v*,∆v*,g*,aL*) and the actual acceleration a will be drawn from F(v*,∆v*,g*,aL*). For each tuple (v,∆v,g,aL), the probability distribution F(v,∆v,g,aL) was determined by the data of the FOT. For that, the data of velocity, velocity difference, gap and acceleration of the preceding vehicle were binned, and for each tuple (v,∆v,g,aL) of binned values, the expected acceleration, the variance and the type of distribution were computed and stored in look-up tables. During a simulation, the probability distribution F(v,∆v,g,aL) to a given tuple (v,∆v,g,aL) can be recovered by these look-up tables.To achieve probability distributions that result in a well behaving car-following model, the data had to be corrected (due to erroneous sensor data) and filtered (due to situations in which the driver reacted to other influences besides the preceding vehicle, e.g. red traffic lights) in numerous steps.Here, we will present the mentioned correction and filtering steps in detail. Further, we will discuss the derived probability distributions and the calibration of the model. Finally, the model will be evaluated and compared to other existing car-following models in several scenarios with respect to various criteria, e.g. the number of accidents, the distribution of surrogate safety measures, and the fundamental diagram.

지리정보시스템 기반 경로계획을 이용한 지능형순항제어시스템 개발

Autonomous driving is no longer atechnology of the future since the development of autonomous vehicles has now been realized, and many technologies have already been developed for the convenience of drivers. For example, autonomous vehicles are one of the most important drive assistant systems. Among these many drive assistant systems, Cruise Control Systems are now a typical technology. This system constantly maintains a vehicle`s speed and distance from a vehicle in front by using Radar or LiDAR sensors in real time. Cruise Control Systems do not only serve their original role, but also fulfill another role as a `Driving Safety` measure as they can detect a situation that a driver did not predict and can intervene by assuming a vehicle`s longitude control. However, these systems have the limitation of only focusing on driver safety. Therefore, in this paper, an Intelligent Cruise Control System that utilizes the path planning method and GIS is proposed to overcome some existing limitations.

Different Control Algorithms for a Platoon of Autonomous Vehicles

This paper presents a concept of platoon movement of autonomous vehicles (smart cars). These vehicles have Adaptive or Advanced cruise control (ACC) system also called Intelligent cruise control (ICC) or Adaptive Intelligent cruise control (AICC) system. The vehicles are suitable to follow other vehicles on desired distance and to be organized in platoons. To perform a research on the control and stability of an AGV (Automated Guided Vehicles) string, we have developed a car-following model. To do this, first a single vehicle is modeled and since all cars in the platoon have the same dynamics, the single vehicle model is copied ten times to form model of platoon (string) with ten vehicles. To control this string, we have applied equal PID controllers to all vehicles, except the leading vehicle. These controllers try to keep the headway distance as constant as possible and the velocity error between subsequent vehicles - small. For control of vehicle with nonlinear dynamics combi­nation of feedforward control and feedback control approach is used. Feedforward control is based on the inverse model of nominal dynamics of the vehicle, and feedback PID control is designed based on the linearized model of the vehicle. For simulation and analysis of vehicle and platoon of vehicles – we have developed Matlab/Simulink models. Simulation results, discussions and conclusions are given at the end of the paper.

Development of an Intelligent Cruise Control Using Optimal Control Methods

In this contribution, the range extension problem of electric vehicles is addressed. To this aim, an intelligent cruise control is developed based on the formulation of an optimal control problem. Solutions of this optimal control problem are energy efficient accelerator pedal position profiles. They can be computed numerically by a direct optimal control method using sequential quadratic programming. The approach is applied to two different driving scenarios. The results show that the energy efficiency is increased by using optimal control for both an artificial and a realistic scenario.

Robust predictive cruise control for commercial vehicles

In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.

3409 ロボット群で混雑したシステムにて形成される渋滞を解決するための方法論(OS9 無人化・遠隔監視、次世代物流システム、建設機械,オーガナイズド・セッション(OS))

In order for multiple autonomous mobile robots to solve jams, this paper describes a novel methodology. In this methodology, first off, our previously-proposed cruise control technique is improved. After that, a behavior rule in connection with the intelligent cruise control technique is designed and provided on congested lanes where robots slowly move. Through simulation experiments, the influence of the improved control technique and behavior rule on the robots' movement in the system is evaluated. Finally, the effectiveness of the methodology for solving the jams is shown.

Methodology for Solving Congestion Formed by Multiple Mobile Robots -Development of Intelligent Cruise Control Technique and Behavior Rule-

In order for multiple mobile robots to solve congestion, a novel methodology that consists of two approaches, intelligent cruise control technique and behavior rule, is proposed in this paper. For this purpose, first, our previously-proposed cruise control technique is improved. This enables robots to reduce velocity not only for the congestion preceding them, but also for the decelerating robot in front, using external interaction force generated among robots with a virtual damper. After that, a behavior rule in connection with the intelligent cruise control technique is designed and provided on congested lanes where robots slowly move. Thus, stronger interaction force affects the robots, and they move more slowly on the congested lanes. In simulation experiments, the proposed methodology is compared to two other cruise control techniques. These are quantitatively evaluated on the basis of a criterion, i.e., behavioral feature, such as average velocity of all the robots and standard deviation of each velocity. Finally, the effectiveness of the methodology for solving the congestion is shown.

Auto-adaptive and string stable strategy for intelligent cruise control

This study completes the study of a longitudinal control strategy based on a non-linear acceleration profile model. The model is thoroughly defined by a set of two design parameters that make the control policy auto-adaptive. The control strategy is safe and Stop&Go consistent. The string stability property of the control policy is also analysed. Simulation results show that the control policy has a good string stability property with high string stability margin with respect to Pipes and Chandler's control strategy.