Adaptive Cruise Control System Research Articles

Revolutionizing Sports Bikes with Artificial Intelligence: Safety, Performance, and Design Innovations

This study examines how artificial intelligence (AI) is incorporated into sports bikes and examines the significant impacts this has on ride quality, user experience, and safety. Adaptive cruise control and collision avoidance systems, two AI-driven technologies that improve rider safety, and engine performance improvements and predictive maintenance algorithms that improve overall bike performance and dependability. Furthermore, bike design processes are revolutionized by AI-driven design techniques, which allow for quick iterations and customisation. This study offers insights into how artificial intelligence (AI) can revolutionize sports bike technology in the future. Keywords: Artificial Intelligence (AI), Sports Bikes, Ride Quality, User Experience, Safety, Adaptive Cruise Control, Collision Avoidance Systems, Rider Safety, Engine Performance Improvements, Predictive Maintenance Algorithms, Bike Performance, Dependability, AI Driven Design Techniques.

Supervisory adaptive safety control for uncertain nonlinear systems

This paper investigates the supervisory adaptive safety control problem for uncertain nonlinear systems with unmeasurable states. In order to estimate unknown parameters and unmeasurable states, a supervisory observer framework consisting of a multi-observer bank and a supervisor unit, is proposed based on a dividing rectangles (DIRECT) algorithm. Under a desired accuracy, the parameter estimates converge to near the true parameters within finite-time while the estimation error of the system state converges to a small neighborhood near the origin. Also, an observer robust adaptive control barrier function is constructed to ensure safety for uncertain nonlinear systems by guaranteeing controlled invariance of a time-varying auxiliary set. By adjusting some parameters in the DIRECT algorithm, the time-varying auxiliary set approaches the original safe set, which reduces the conservatism generated by the compression of the safe set. Finally, two examples, including an adaptive cruise control system, are given to demonstrate the effectiveness of the proposed method.

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in industry to support IIIE. Traceability management is considered the foundation of information management in MBSE. However, a lack of integration between stakeholders, development processes, and models can decrease the effectiveness and efficiency of the system development. A modified MBSE toolchain prototype has been developed to implement traceability management; however, a lack of formal and structured specifications makes it difficult to describe the complex topology in traceability management scenarios using this MBSE toolchain, such as creating traceability between heterogeneous models, which leads to poor reusability of this MBSE toolchain in other traceability management scenarios. To formalize traceability management scenarios using the MBSE toolchain, a cognitive thread (CT) ontology is developed in this study. The CT ontology is a specification expressing the information of stakeholders, models, and development processes for traceability management, providing the cognition capability to analyze the interrelationships between them. Based on the implementation of the modified MBSE toolchain, the concepts and interrelationships in the CT ontology are identified. The CT ontology is designed to develop the MBSE toolchain prototype for building, managing, and analyzing traceability in various traceability management scenarios. A case study of an adaptive cruise control system design is used to evaluate the completeness of the CT ontology through qualitative and quantitative analyses. The results demonstrate that the proposed CT ontology formalizes the information related to traceability management while using the proposed MBSE toolchain and can also be used in common traceability management scenarios to design other complex engineered systems.

Investigation of Different Communication Topologies for Cooperative Adaptive Cruise Control Systems

After Adaptive Cruise Control (ACC) system applications, Cooperative Adaptive Cruise Control (CACC) systems are becoming an important part of automotive technology and industry in autonomous vehicles convoy applications. Together with this developing technology, CACC systems use vehicle to vehicle (V2V) communication to automatically transmit the movement information of vehicles. In this context, ACC systems use Radar or LIDAR measurements while CACC systems also consider the acceleration of the preceding vehicle. In this paper, the forms of information transmission between vehicles in autonomous vehicle convoys using CACC systems have been examined. From these forms of information transmission, the leader following, the predecessor follow-ing and the leader – predecessor following topologies have been considered. For each topology, an autonomous vehicle convoy consisting of eight vehicles was modeled in the MATLAB/Simulink environment. The feedforward and the feedback control system structure were given for CACC and ACC systems. For different communication topologies, the position-time, the speed-time, the acceleration-time and the headway time-time results were obtained. The maximum intervehicle distance error plots for each vehicle in different topology convoys were given to analyze the dynamic behavior of the convoys. The results have been analyzed in terms of the maximum intervehicle distance, the maximum speed, the minimum and the maximum acceleration, and the maximum headway time deviation from the desired headway time.

Comparative Analysis of Following Distances in Different Adaptive Cruise Control Systems at Steady Speeds

Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds.

An improved adaptive cruise control law

To increase the efficiency of road transport and reduce exhaust emissions, research has been intensified in assisted control systems, where adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) stand out. While ACC uses data from onboard sensors and radars, CACC also uses data from inter-vehicle wireless communication. In this context, this paper proposes a new control law for ACC systems that provides a competitive performance compared to CACC, despite not requiring wireless communication between vehicles. The new control law formulation employs both the vehicle acceleration and the relative speed, which generalizes similar ones. The design methodology formulated as a convex optimization problem ensures string and internal stability as well closed-loop pole placement if a set of linear matrix inequalities (LMIs) is satisfied. At the end of the paper, simulations demonstrate the effectiveness of the proposed methodology.

Adaptive cruise control system with fractional order ANFIS PD+I controller: optimization and validation

Designing the control structures of fractional order PID controllers has proven to be effective in providing adaptability in set point tracing the performance of a nonlinear cruise control system. Wheel rolling resistance, wind drag force, and road gradient are incorporated into the design to better describe the system under consideration and to show how the nonlinear cruise control system behaves. This study presents a comparative investigation using simulation between control structures such as fractional order proportional–integral–derivative, fractional order integral minus proportional derivative, and fractional order proportional integral minus derivative. By preserving integral error indices as the goal function, a genetic algorithm is used to improve the controller gain parameters and fractional scaling values. To prevent integral windup conflicts and derivative boost issues, both traditional fractional order structures and adaptive neuro-fuzzy-based fractional order structures were used to create the adaptive cruise control system. The FO ANFIS PD plus I controller for the cruise control system exceeds the competition in servo and regulatory difficulties.

Evaluating and enhancing the safety performance of automated longitudinal control at on-ramp merging bottleneck: A simulation study in the framework of Kerner’s three-phase traffic theory

The adaptive cruise control (ACC) system, an essential component of commercial autonomous driving that functions in longitudinal control, has attracted significant research interest because of its potential to reduce accident rates. By introducing a non-fixed headway concept, Kerner recently proposed the Three-traffic-Phase ACC (TPACC) model, which integrates a speed adaptation module derived from the three-phase traffic theory. However, the efficacy of such headway flexibility in safety performance remains underexplored. To fill this gap, this study aims to evaluate the longitudinal crash risk of TPACC at on-ramp merging bottleneck, a site where complicated traffic dynamics often constitutes concern, and to further bolster its safety performance. Specifically, simulations are conducted to evaluate the longitudinal crash risk of pure TPACC traffic as well as human-machine mixed traffic, by comparing it with a classical ACC model across various flow rates and market penetration rates. Enhancement of TPACC's safety performance is achieved through the multi-objective optimization in regard to a parameter set of the speed adaptation module. The results indicate that TPACC has the ability to weaken disturbances caused by merging vehicles and leads to a markedly decline in the longitudinal crash risk. Mixed traffic incorporating with TPACC vehicles also exhibits superior safety performance over that with the classical ACC vehicles. Furthermore, the proposed optimization measure effectively enhances TPACC’s longitudinal safety performance across a vast spectrum of penetration rates and flow rates at on-ramp merging area, all the while preserving throughput efficiency. On this basis, a guideline for directional tuning of the parameter set under conditions of limited information accuracy is provided, thereby heightening its practicality.

Sampled-Data Cooperative Adaptive Cruise Control for String-Stable Vehicle Platooning with Communication Delays: A Linear Matrix Inequality Approach

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.

A new driving style recognition method for personalized adaptive cruise control to enhance vehicle personalization

There are limitations of personalization in Advanced Driver Assistance Systems (ADAS) that have a serious impact on driver acceptance and satisfaction. This study investigates driving style recognition method to achieve personalization of longitudinal driving behavior. Currently, driving style recognition algorithms for Personalized Adaptive Cruise Control (PACC) rely on integrated recognition. However, disturbances in the driving cycle may lead to changes in a driver’s integrated driving style. Therefore, the integrated driving style cannot accurately and comprehensively reflect the driver’s driving style. To solve this problem, a new driving style recognition method for PACC is proposed, which considers integrated driving style and driving cycle. Firstly, the method calculates the constructed feature parameters of driving cycle and style, and then reduces the dimensionality of the feature parameter matrix by principal component analysis (PCA). Secondly, a two-stage clustering algorithm with self-organizing mapping networks and K-means clustering (SOM-K-means) is used to obtain the type labels. Then, a transient recognition model based on random forest (RF) is established and the hyperparameters of this model are optimized by sparrow search algorithm (SSA). Based on this, a comprehensive driving style recognition model is established using analytic hierarchy process (AHP). Finally, the validity of the proposed method is verified by a natural dataset. The method incorporates the driving cycle into driving style recognition and provides guidance for improving the personalization of adaptive cruise control system.

Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor

This article will focus on adaptive cruise control in autonomous automobiles. The adaptive cruise control inputs are the safety distance which determines thanks to conditions set depending on the distance value, the measured distance, the longitudinal speed of the autonomous automobile itself, the output is the desired acceleration. The objective is to follow the vehicles in front with safety, according to the distance measured by the ultrasonic sensor, and maintain a distance between the vehicles in front greater than the safety distance which we have determined. For this, we used super twisting sliding mode controller (STSMC) and non-singular terminal sliding mode controller (NTSMC) based on neural network applied to the adaptive cruise control system. The neural network is able to approximate the exponential reaching law term parameter of the NTSMC controller to compensate for uncertainties and perturbations. An autonomous automobile adaptive cruise control system prototype was produced and tested using an ultrasonic sensor to measure the distance between the two automobiles, and an Arduino board as a microcontroller to implement our program, and four DCs motors as actuators to move or stop our host vehicle. This system is processed by code and Simulink Matlab, the efficiency and robustness of these controllers are excellent, as demonstrated by the low longitudinal velocity error value. The safety of autonomous vehicles can be enhanced by improving adaptive cruise control using STSMC and NTSMC based on neural network controllers, which are chosen for their efficiency and robustness.

A cooperative interaction strategy for vehicle platoons to obtain merging gaps in connected environments

Vehicle platoons can significantly improve traffic throughput and reduce fuel consumption and emissions. In the formation of platoons, it is crucial to generate safe merging gaps. This process requires appropriate cooperative management and control strategies. This study proposes a cooperative interaction strategy for vehicle platoons, including a communication management system and vehicle control strategies. These strategies can reduce velocity fluctuation in the process of gap generation and improve traffic performance. First, a communication management system within a platoon was developed, according to the standard communication protocol (SAE J2735), ensuring that external vehicles can join the platoon efficiently and orderly. Next, a cooperative adaptive cruise control (CACC) system was designed, which adopts feedforward and feedback control. Furthermore, the influence of increasing gaps on the stability of the platoon was considered. A cooperative control strategy for a virtual guiding vehicle (VGV) was introduced to switch the following target and linearly change the distance input of the controller. In this way, the downstream vehicles were guided to smoothly generate a safe merging gap, which can reduce speed fluctuation, and ensure the stability and safety of the platoon. Finally, the entire process of interaction in a vehicle platoon was tested in a simulation environment. The results showed that, compared with the parameter adaptive control strategy, the maximum velocity overshoot of the platoon vehicles was reduced by 56%, recovery stabilization time was reduced by 47%, and vehicle jitter was reduced by 43%. The driving security and platoon stability were both within the control boundaries set for evaluation.

Effects of Adaptive Cruise Control System on Traffic Flow and Safety Considering Various Combinations of Front Truck and Rear Passenger Car Situations

Existing studies on the heterogeneity in traffic flow have considered either conventional car–truck or conventional vehicle–automated vehicle combinations. Nevertheless, these assumptions are not realistic as there will be possible combinations of cars–trucks and conventional–automated vehicles in the future. This study aims to investigate heterogeneous traffic flow with or without an adaptive cruise control (ACC) system for the pair of a front truck and a rear passenger car. Vissim microscopic traffic simulation software and the HighD dataset were applied. The major findings include the following. (1) The greater the penetration rate of ACC vehicles in the same speed interval, the smaller the space headway of the rear car. (2) Both conventional and ACC cars accelerate or decelerate frequently when their speeds exceed that of the front truck in the car-following state. In the free-flow state, a conventional car keeps a constant speed or accelerates, while an ACC car mainly adopts an acceleration strategy. (3) The variation of speed differences and space headways is smallest when both the front truck and rear car have ACC. (4) The probability of conflicts between the front truck and rear car pair decreases with the increase of market penetration of ACC vehicles. Compared to conventional cars, the rear car equipped with ACC exhibits a lower probability of conflicts. Statistical tests further confirmed the significant differences between any two of the four combinations based on the ACC equipment.

Study on multi-objective adaptive cruise control of intelligent vehicle based on multi-mode switching

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.

Coordinated Decision Control of Lane-Change and Car-Following for Intelligent Vehicle Based on Time Series Prediction and Deep Reinforcement Learning.

Adaptive cruise control and autonomous lane-change systems represent pivotal advancements in intelligent vehicle technology. To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction and deep reinforcement learning under the influence of multiple surrounding vehicles. Firstly, we analyze the lane-change behavior and establish boundary conditions for safe lane-change, and divide the lane-change trajectory planning problem into longitudinal velocity planning and lateral trajectory planning. LSTM network is introduced to predict the driving states of surrounding vehicles in multi-step time series, combining D3QN algorithm to make decisions on lane-change behavior. Secondly, based on the following state between the ego vehicle and the leader vehicle in the initial lane, as well as the relationship between the initial distance and the expected distance with the leader vehicle in the target lane, with the primary objective of maximizing driving efficiency, longitudinal velocity is planned based on driving conditions recognition. The lateral trajectory and conditions recognition are then planned using the GA-LSTM-BP algorithm. In contrast to conventional adaptive cruise control systems, the DDPG algorithm serves as the lower-level control model for car-following, enabling continuous velocity control. The proposed model is subsequently simulated and validated using the NGSIM dataset and a lane-change scenarios dataset. The results demonstrate that the algorithm facilitates intelligent vehicle lane-change and car-following coordinated control while ensuring safety and stability during lane-changes. Comparative analysis with other decision control models reveals a notable 17.58% increase in driving velocity, underscoring the algorithm's effectiveness in improving driving efficiency.

Longitudinal Control for Cruise Control Features in Vehicle Automation

This research focuses on longitudinal control methods implemented in Cruise Control (CC) and Adaptive Cruise Control (ACC) systems, as well as the mechanism for switching between these two modes. Two primary longitudinal control methods are examined: Constant Space Headway Control, which maintains a constant distance between vehicles, and Constant Time Headway Control, which maintains a constant time gap between vehicles.
 The noteworthy feature of this research focuses on incorporating a switching mechanism between Cruise Control and Adaptive Cruise Control through the utilization of the virtual target method.

Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles

As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems.

Performance Comparison Between Centralized and Hierarchical Predictive Functional Control for Adaptive Cruise Control Application

This paper presents a performance comparison between two different control structures of Predictive Functional Control (PFC), namely centralized and hierarchical architectures for an Adaptive Cruise Control (ACC) system. Centralized PFC utilizes a linearized model of the vehicle longitudinal dynamics to compute the control law, while hierarchical PFC uses a simple kinematic model with inverse vehicle longitudinal dynamics. Based on the simulation results, both control structures produced a satisfactory performance. However, in the presence of disturbances such as road slopes and wind gusts, the centralized PFC becomes more sensitive and conservative than the hierarchical PFC. The main reason is that the linearization model used in centralized PFC is only effective around the selected nominal operating points. Since the decision-making process depends on the internal model performance, centralized PFC cannot compensate for this effect. Besides, it also takes a longer computation time than hierarchical PFC since more mathematical operations must be solved than the kinematic model, especially when physical and operational constraints are considered. The standard performance parameters such as Root Mean Squared Error (RMSE), settling time, and rise time are also used for the analysis. These findings can become a solid justification for using the hierarchical PFC structure in designing an ACC system for future work.

Model Predictive Control Design for Electric Vehicle Based on Improved Physics-Inspired Optimization Algorithms

Abstract: This paper introduces the mathematical model of the leader-follower electric vehicle (EV). Consequently, the system was analyzed to obtain stability and performance. Model Predictive Control (MPC) is also proposed to fix the EV system issues. Moreover, two optimization algorithms are applied to optimize the performance of the MPC: electrically charged particle optimization (ECPO) and improved chaotic electromagnetic field optimization (ICEFO). The MPC scheme is based on the Adaptive Cruise Control System (ACCS), applied to two vehicles: the leader and follower. In this context, the simulation results of both optimization methods with the MPC scheme are presented in the result section. Finally, a comparison is made to show the proposed controller’s effectiveness with the improved optimization algorithms. Also, the ACC electric vehicle tracking system was achieved at 98% with the reference input.

Fault-Tolerant Safety-Critical Control for Nonlinear Affine System by Using High-Order Control Barrier Function

A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into the conventional HOCBF to obtain a new type of HOCBF with faults. Secondly, in the case of a strictly required range of control inputs, a sufficient condition is proposed to satisfy the existing constraints, where the control inputs are always within the restricted range and the sufficient condition is expressed as feasibility constraints. Next, based on the quadratic programming (QP) method, the control Lyapunov function, fault HOCBF, and feasibility constraints are incorporated together to ensure that the overall feasibility, stabilization, and safety are guaranteed of the considered closed-loop system. Finally, the adaptive cruise control system is taken as an example to verify the effectiveness of the proposed method.