Search Results (88)

Adaptive cruise control (ACC) performance is affected by multiple coupled factors, including safety margin, dynamic response, spacing regulation, target-transition behavior, and ride comfort. A single indicator is therefore insufficient for comprehensive ACC evaluation. This study proposes an adaptive cruise control comprehensive performance evaluation index model (ACC-CPEIM) for scenario-oriented ACC testing and diagnosis. The model links functional objectives, six typical ACC scenarios, measurable longitudinal indicators, hierarchical weights, and scenario-specific scoring rules into a unified evaluation chain. Time-domain response data are converted into scenario-level, criterion-level, and overall performance scores, while the results remain traceable to specific weak scenarios and performance dimensions. The proposed model was evaluated using a CarSim/Simulink co-simulation platform and further applied to vehicle-test data. The co-simulation results yielded an overall score of approximately 3.32 and identified weak acceleration-following response, insufficient spacing reserve during deceleration, and limited cut-in safety margin as the main limitations. The vehicle-test application produced an overall score of approximately 2.98 and showed that comfort and steady-state control were relatively stronger, whereas target-transition adaptability, safety margin, and dynamic response remained limiting dimensions. The results indicate that the ACC-CPEIM can provide quantitative, interpretable, and engineering-oriented support for ACC performance testing and diagnosis. Full article

Cooperative adaptive cruise control (CACC), as an extension of adaptive cruise control (ACC), is an intelligent transportation approach for connected and automated vehicles. By using vehicle-to-vehicle information, CACC improves longitudinal tracking performance, traffic throughput, and string-stable platoon behavior. However, controller tuning remains sensitive to vehicle-dynamics parameters, spacing-policy selection, fractional-order dynamics, and communication delay. This paper presents an analytical parameter-space-based fractional-order PD (FOPD) controller synthesis framework for string-stable CACC systems. For the constant-time headway spacing policy, the controller parameters are investigated in the $(k_p,k_d,\mu )$ parameter space, where the fractional differentiation order $\mu$ is considered as an additional design variable. To obtain the feasible stabilizing regions, the fractional-order characteristic equation is evaluated on the imaginary axis, and the delay-dependent stability boundaries are derived through a frequency-domain boundary-locus formulation. The stabilizing gain regions are constructed through the complex-root boundary (CRB), real-root boundary (RRB), and infinite-root boundary (IRB), which provide an interpretable graphical basis for controller-gain and fractional-order selection. In addition, the effect of the headway time on the admissible stability region is examined jointly with the fractional order. The proposed structure is implemented with a feedforward controller that uses the acceleration information of the preceding vehicle under a predecessor-vehicle-following communication topology. The selected fractional-order CACC (FO-CACC) controller is validated in an eight-vehicle platoon simulation environment and compared with integer-order ACC (IO-ACC), fractional-order ACC (FO-ACC), and integer-order CACC (IO-CACC) configurations. The results show that the proposed parameter-space approach enables systematic FOPD tuning and that the selected FO-CACC controller satisfies the frequency-domain string-stability requirement while maintaining smooth time-domain responses in position, velocity, acceleration, headway time, spacing error, and control input. Additional simulations under the New European Driving Cycle (NEDC) and the FTP-75 (Federal Test Procedure 1975) driving cycles further indicate that the proposed FO-CACC structure maintains accurate spacing regulation and bounded acceleration behavior under standard drive-cycle conditions. Overall, the results indicate that the fractional-order parameter provides an effective design freedom for improving string-stable cooperative platoon performance. Full article

Adaptive cruise control (ACC) is a key component of advanced driver assistance systems, as it maintains a safe distance from preceding vehicles by regulating speed and spacing. However, vehicle dynamics, measurement uncertainty, and traffic variability pose significant challenges for conventional control methods. In this context, fuzzy logic (FL) has been widely explored for its ability to handle uncertainty and incorporate expert knowledge via linguistic rules. This article presents a systematic literature review on the application of FL in ACC systems, proposing a functional taxonomy based on the role of the fuzzy system within the control architecture. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 103 initial records were identified, of which 87 studies were included in the final analysis. Four main categories are defined: Direct Fuzzy Control/Learning-Based, Fuzzy Supervisory Decision Control, Fuzzy Adaptive Robust Control, and Fuzzy Model-Based Control. Results indicate that Direct Fuzzy Control/Learning-Based and Fuzzy Supervisory Decision Control dominate the literature, accounting for 35.6% and 28%, respectively, while Fuzzy Adaptive Robust Control and Fuzzy Model-Based Control represent 20.7% and 14.9%. Mamdani-type systems predominate (78.16%), followed by Takagi-Sugeno (T–S) systems (17.24%), while type-2 fuzzy systems remain limited (4.60%) due to higher computational complexity. Recent trends highlight growing interest in adaptive and robust FL-based strategies. Full article

Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (Sim2Real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles. Full article

Advanced Driver Assistance Systems (ADAS) are increasingly shaping urban mobility and road safety, yet their benefits depend not only on technical performance, but also on driver acceptance. This study examines how Hungarian drivers perceive and evaluate key ADAS functions, Adaptive Cruise Control (ACC), Lane Keeping/Centering Assist (LKA/LCA), and Forward Cross Traffic Alert (FCTA), in urban driving contexts. The research is based on qualitative focus group discussions conducted in Győr, Hungary, involving drivers aged 20–50 from different age cohorts. Data were analyzed using thematic analysis. The findings show that the acceptance of ADAS is strongly context-dependent and function specific. ACC was perceived primarily as a comfort-enhancing tool, especially on longer or more monotonous routes, while LCA was often regarded intrusive and less reliable in urban conditions due to poor road markings, potholes, and frequent stop-and-go situations. On the contrary, blind spot and cross-traffic-related functions were evaluated more positively due to their direct safety benefits. Trust, perceived risk, and control emerged as key dimensions of acceptance, with many participants emphasising the importance of warning-based support rather than a strong autonomous intervention. In general, the study concludes that urban acceptance of ADAS is shaped by the interaction of infrastructure conditions, perceived usefulness, and driver trust, highlighting the need for more transparent, context sensitive, and user-centered system design in support of safer urban mobility. Full article

It is well known that the features of self-driving vehicles depend on communication technologies to demonstrate their benefits. Since these technologies are still under development and face numerous obstacles, this highlights the need to develop a modern approach to solving congestion during the transitional phase. In this study, we worked on developing an integrated new operational strategy that maximizes the benefits of the variable speed limit strategy and expands its impact by coordinating its operation with the spacing policy mechanism in vehicles equipped with adaptive cruise control (ACC) to provide an innovative approach aims to operate vehicles with low levels of autonomy and leverage their ability to maintain short time gaps to operate as an effective category to improve traffic conditions, aided by existing transportation systems. To achieve this, we employed PTV-VISSIM to develop the VSL algorithm, which was coded using the VisVAP interface. We also used VISSIM features to model and develop the characteristics of the ACC vehicles and the spacing policy. Different control strategies were tested individually and in combination at various penetration rates, and the results demonstrated the superiority of our proposal to integrate the VSL mechanism with the short-time gap recommendation strategy. The strategy’s effect was also evident in emissions reductions of 52% to 86% and in fuel consumption decreases of 52% to 87% compared to the no-control scenario, and of 56% to 28% compared to the typical VSL scenario, supporting an environmental sustainability approach in traffic strategies. Full article

This paper addresses the observer-based controller design for adaptive cruise control (ACC) systems using a linear matrix inequality (LMI) framework, considering both input saturation and disturbance attenuation performance. To formulate the controller design problem as LMIs, the nonlinear input saturation is represented as a convex combination of linear state feedback controllers. Unlike conventional approaches that only reformulate input saturation, this work further incorporates the estimated state and the decay rate of a Lyapunov function to establish an invariant level set condition, leading to a novel LMI-based design criterion. The proposed method incorporates level set conditions to handle input constraints and employs an ${\mathcal{H}}_{\infty }$ criterion to ensure disturbance attenuation. Since the resulting design conditions are non-convex due to bilinear matrix terms, a two-step approach is applied to derive the controller design conditions in the form of LMIs. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method. Full article

Autonomous Vehicle (AV) platooning is a promising solution for enhancing traffic efficiency and safety. However, real-world deployment faces challenges due to uncertainties and delays, which can impact platoon stability and safety. This study analyzes AV platoon stability and safety, considering control parameter uncertainty, mechanical delay, and perception delay. The research also extends the analysis to mixed traffic environments, where AVs interact with human-driven vehicles (HVs). A modified Adaptive Cruise Control (ACC) model is used, incorporating delays and uncertainties. Time–domain and frequency–domain stability analyses evaluate the impact of these factors on platoon stability, while Time-to-Collision (TIT) and Collision Probability Index (CPI) metrics assess safety. Results show that delays and uncertainty significantly degrade platoon stability, with the damping ratio falling below critical levels. Mixed traffic environments further increase collision risks. Increasing AV penetration improves safety, but HV behavior remains a challenge. The study emphasizes the need for adaptive control strategies to ensure stable and safe AV platoon operations in real-world conditions. Full article

The rapid increase in global vehicle usage has intensified challenges such as traffic congestion, frequent accidents, and energy consumption, highlighting the need for safe and efficient platooning strategies. Conventional adaptive cruise control (ACC), while widely adopted, suffers from string instability that amplifies disturbances along a platoon. Communication-based cooperative ACC (CACC) can theoretically guarantee string stability at short headways, but its dependence on costly and unreliable vehicle-to-vehicle (V2V) links limits large-scale deployment. Radar-only CACC using single-model Kalman Filter (KF) alleviates this dependency, yet its estimation accuracy degrades under abrupt maneuvers due to model mismatch. To overcome these limitations, this paper proposes a Multi-Q Interacting Multiple Model Kalman Filter (Multi-Q IMM-KF) approach that adaptively blends multiple motion models to ensure robust acceleration estimation across diverse driving conditions. A four-vehicle platoon simulation in CarSim–Simulink demonstrates that the Multi-Q IMM-KF CACC significantly reduces spacing error propagation and improves velocity tracking compared with ACC and Nominal KF-CACC, offering a cost-effective and communication-resilient solution for practical platoon control. Full article

In this paper, we propose systematic controller design guidelines to ensure both individual vehicle stability and string stability in cooperative adaptive cruise control (CACC)-based platoon systems, assuming a homogeneous platoon where all vehicles share identical dynamic models. We rigorously demonstrate that the limitation of conventional adaptive cruise control (ACC) in maintaining the target inter-vehicle distance can be effectively overcome by incorporating the desired acceleration of the preceding vehicle as a static feedforward input. Furthermore, by formulating transfer functions in the frequency domain, we analytically derive the conditions required to ensure both individual vehicle stability and string stability of the CACC system. Building on this insight, we propose a practical and theoretically well-founded design guideline for determining the proportional, derivative, and feedforward gains of control input under a constant time gap spacing policy. The proposed guidelines are validated through simulations conducted in a realistic platooning scenario involving multiple vehicles. Full article

As automation technologies continue to advance within the automotive industry, urban road traffic is gradually shifting from conventional driving toward fully autonomous. This transition is supported by the progressive integration of partially automated functions, such as Adaptive Cruise Control (ACC) and lane-keeping assistance, which are already implemented in commercial vehicles and increasingly affect both individual driving behavior and overall traffic flow dynamics. The main purpose of this research is to evaluate the impact of automated vehicles presence in a complex signalized intersection under mixed traffic conditions, considering different penetration rates and demand levels. A review of previous modeling approaches from the literature was conducted, highlighting critical aspects to be considered in the design and simulation of road traffic. Field traffic data were collected and used as input for a microsimulation model developed in AIMSUN. A base scenario and a 20% growth scenario were analyzed to assess the impact of AV-ACC penetration, varying the AV-ACC’s rates in traffic composition. The results indicate that increased AV-ACC penetration rates, especially beyond 50%, contribute significantly to improving traffic stability and efficiency. Full article

This paper proposes a dynamic weight model predictive control (DWMPC) strategy for adaptive cruise control (ACC) in pure electric vehicles, aiming to enhance robustness, energy efficiency, and ride comfort under complex traffic conditions. Unlike conventional MPC with static weights, the proposed method integrates a fuzzy inference system that evaluates driving urgency based on real-time spacing and velocity errors. The resulting emergency coefficient is mapped through a nonlinear function to dynamically adjust the velocity tracking weight in the MPC cost function. Additionally, a four-mode coordination mechanism adaptively modifies acceleration and jerk penalties according to risk levels, enabling balanced responses between safety and comfort. A composite performance evaluation index (PEI) is formulated to quantitatively assess energy consumption, ride comfort, spacing accuracy, and emergency responsiveness. Simulation results under WLTC and typical urban driving scenarios demonstrate that DWMPC outperforms fixed-weight MPC and PI controllers, reducing energy consumption by 6.5%, jerk by 42.9%, and response time by 41.8% while improving coordination in speed tracking, inter-vehicle distance regulation, and energy-efficient control. Full article

This paper proposes a practical design guideline for selecting control parameters in adaptive cruise control (ACC) systems to ensure both individual vehicle stability and string stability in vehicle following systems with homogeneous longitudinal dynamics. The primary control objective is to regulate spacing errors under a constant time-gap policy, which is commonly adopted in ACC applications. By employing a simple proportional-derivative (PD) controller, we present a clear methodology for tuning the proportional and derivative gains. The proposed approach demonstrates that string stability can be effectively achieved using this straightforward control structure, making it highly applicable for assisting practitioners in selecting appropriate parameters for real-world platooning scenarios. We provide a rigorous analysis of the necessary and sufficient conditions for selecting PD gains, along with practical guidelines for implementation. The effectiveness of the design guideline is further validated through simulations conducted in realistic driving scenarios. Full article

The need for autonomous vehicles has started rising rapidly. Many autonomous technologies, such as Cruise Control, the self-parking system, and the emergency braking system, are implemented in contemporary cars. These systems do not make the car fully autonomous; however, they allow people to get used to the idea of self-driving cars. Due to a surge of interest in autonomous systems, the development of these technologies has begun. This paper presents a model of Adaptive Cruise Control with a control system, which consists of two PID regulators. Using two PID regulators provides the possibility of more advanced regulation characteristics than using the classical one-PID regulation system. One of them regulates the powertrain model, the other the braking system model. The simulations are carried out using a vehicle dynamic system, whose thrust is determined by a real engine maximum torque curve that is approximated by combinations of polynomial functions. Due to the non-linearity, caused by the motor’s curve and the use of two regulators, the PID tuning algorithm has been created. The algorithm provides satisfying results, followed by a marginal difference between the requested safe distance and actual distance value. The Active Cruise Control system has been tested using normalized driving cycles, which simulate the real behaviour of a car. The simulation results prove double-PID-regulated ACC’s accuracy and speed of response in different states of motion. Full article

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios. Full article