A Comparative Study in Fuzzy Control Systems for Cruise Control Vehicles

Adaptive Cruise Control (ACC), Cruise Control (CC), and Automatic Emergency Braking (AEB) serve as the basis of longitudinal automated driving, and as Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab’s Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.

Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control

Adaptive cruise control is one of the essential technologies of advanced driver assistance systems, which is used to maintain a safe distance between an ego vehicle and a preceding vehicle and has been extensively applied in the automotive industry and control community. Note that some vehicle manoeuvres may approach handling limits to prevent collisions under complex road conditions, which often leads to vehicle lateral instability while cruising. In this study, a T-S fuzzy model predictive control framework is applied to the problem of adaptive cruise control. Variations in the preceding vehicle velocity and road surface conditions are considered to formulate adaptive cruise control as a tracking control problem of a T-S fuzzy system subject to parameter uncertainties and external persistent perturbations. Then, a robust positively invariant set is introduced to derive an admissible T-S fuzzy controller by solving a min-max optimization problem under a series of linear matrix inequality constraints. Finally, a CarSim/MATLAB joint simulation is conducted to illustrate the effectiveness of the proposed method, which ensures longitudinal adaptive cruise control for a car-following scenario with lateral vehicle stability.

Cruise controllers are used to automatically control the speed of motor vehicles. In order to maintain a desired vehicle speed the controller takes over the throttle in a cruise control system which proves particularly useful for long drives. Adaptive Cruise Control (ACC) systems have additional capabilities such as automatic braking or dynamic set-speed type controls and hence can accommodate changes in cruise speed required to adapt to changing road conditions. We propose that further improvement in fuel economy can be achieved by considering the vehicle's longitudinal dynamics as an input-constrained system and the wheel torque as the corresponding constrained input. We effectively address the resulting input saturation nonlinearity by employing our adaptive anti-windup compensator design. Simulation results are used to compare the performance of the original cruise control system allowing for the full-range of wheel torque and the ACC system where the wheel torque is forced to remain within the user-defined limits for improved fuel economy.

<div class="htmlview paragraph">Radar based collision warning systems (CWS) for heavy trucks have been in production and on the road in the United States for approximately two years. A short description of these systems is presented with actual driver performance data and accident reduction data as experienced by various U.S. national fleets using these systems. The next application of radar sensors is for Intelligent Cruise Control (ICC) or also known as Adaptive Cruise Control (ACC). This paper describes the operation of adaptive cruise control and presents the associated benefits to the driver</div> <div class="htmlview paragraph">The radar technology has been enhanced and the application has been extended to include radar controlled adaptive cruise control for heavy trucks This has been made possible by 1) technology improvements in radar systems, plus 2) the addition of the standard SAE J1939 data bus for engine controllers on most U.S diesel engines. The SAE J1939 data bus allows electronic control of engine speed by the radar system plus in many cases, control of engine retarder braking to assist in vehicle slowing while in adaptive cruise control operation.</div> <div class="htmlview paragraph">This paper described the radar operation, radar enhancements and the benefits of adaptive cruise control as experienced by drivers. The most significant radar enhancement, called monopulse radar is presented Comparisons are made between standard cruise control operation on heavy trucks and adaptive cruise control</div>

This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

The influence of Cruise Control and Adaptive Cruise Control on driving behaviour – A driving simulator study

With the rise of Connected-and-Automated-Vehicle (CAV) technologies on roadways, transportation networks have become increasingly connected through Vehicle-to-Everything (V2X) systems. With access to the additional data from V2X, modern cruise control systems like Adaptive Cruise Control (ACC) are further improved upon to develop systems like Cooperative ACC (CACC) which reduces traffic congestion and increases driver safety and energy efficiency. With that increased connectivity, previously closed vehicle systems are now vulnerable to new security threats which pose new technical challenges. Significant research has been done to strengthen the network against external threats such as denial-of-service attacks (DoS) or passive eavesdropping attacks using network management and cryptographic strategies. Internal threats like data falsification are more challenging to address because they originate from already authenticated sources on the network. This work suggests a method to locally determine if network data can be trusted utilizing only the intra-vehicle sensors against the network data. It functions by leveraging the synchronization of CACC vehicle stream members to identify potentially malicious data. In the event the network data is determined to be untrustworthy, the vehicle will change its mode of operation to basic ACC where it will disconnect from the vehicle stream and increase the distance from the preceding vehicle. In order to test this approach, an ACC system was created and then modified into a simple CACC system that includes the V2X network data streams. Two common V2X applications were used to show the functionality of both the simple CACC system and the work: a Vehicle-to-Infrastructure (V2I) enabled traffic light and a Vehicle-to-Vehicle (V2V) vehicle stream.

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

Safety and energy are two key issues to affect the development of automotive industry. For the safety issue, the vehicle active collision avoidance system is developing gradually from a high-speed adaptive cruise control (ACC) to the current low-speed stop and go (SG), and the future research topic is the ACC system at full-speed, namely, the advanced ACC (AACC) system. The AACC system is an automatic driver assistance system, in which the driver's behavior and the complex traffic environment ranging are taken into account from high-speed to low-speed. By combining the function of the high-speed ACC and low-speed SG, the AACC system can regulate the relative distance and the relative velocity adaptively between two vehicles according to the driving condition and the external traffic environment. Therefore, not only can the driver stress and the energy consumption caused by the frequent manipulation and the traffic congestion both be reduced effectively at the urban traffic environment, but also the traffic flow and the vehicle safety will be improved on the highway. Taking the actual traffic environment into account, the velocity of vehicle changes regularly in a wide range and even frequently under SG conditions. It is also subject to various external resistances, such as the road grade, mass, as well as the corresponding impact from the rolling resistance. Therefore, the behaviors of some main components within the power transmission show strong nonlinearity, for instance, the engine operating characteristics, automatic transmission switching logic and the torque converter capacity factor. In addition, the relative distance and the relative velocity of the inter-vehicles are also interfered by the frequent acceleration/deceleration of the leading vehicle. As a result, the performance of the longitudinal vehicle full-speed cruise system (LFS) represents strong nonlinearity and coupling dynamics under the impact of the external disturbance and the internal uncertainty. For such a complex dynamic system, many effective research works have been presented. J. K. Hedrick et al. proposed an upper+lower layered control algorithm concentrating on the high-speed ACC system, which was verified through a platoon cruise control system composed of multiple vehicles [1-3]. K. Yi et al. applied some linear control methods, likes linear quadratic (LQ) and proportional–integral–derivative (PID), to design the upper and lower layer controllers independently for the high-speed ACC system [4]. In ref.[5], Omae designed the model matching control (MMC) vehicle high-speed ACC system based on the H-infinity (Hinf) robust control method. To achieve a tracking control between

This paper proposes an optimal design method for the adaptive cruise control model to enhance the string stability with the adaptive cruise control (ACC). First, the influence of control gain parameters on ACC and cooperative adaptive cruise control (CACC) systems is analyzed from theoretical and numerical perspectives. Second, we compared the ACC and CACC models. On this basis, an optimal control gain parameter is proposed to consider the string stability of the ACC platoon system. Finally, we designed numerical simulation experiments to verify the effectiveness of the proposed ACC (PACC) model. Results show that compared with the classical ACC model, the PACC model has certain advantages in recovery time, vehicle average velocity, velocity standard deviation, and vehicle collision safety. Moreover, PACC is suitable for most equilibrium velocity scenarios, and it has good string stability with different time gaps, unlike the ACC and CACC models. As a result, the PACC model has better string stability and robustness. Therefore, the PACC model can enhance the string stability and provide theoretical support for designing better ACC systems.


 
 
 Adaptive Cruise Control (ACC) is introduced to allow the subject vehicle to follow a target vehicle at a pre-selected time gap, up to a driver-selected velocity by controlling the engine and/or service brakes as defined in SAE J2399. A typical ACC ensures passenger comfort by minimizing the subject vehicle’s rate of acceleration and deceleration when following a target vehicle. However, if the ACC rate of velocity change is too slow, the subject vehicle might not be able to follow the target vehicle at the set time-gap. On the other hand, if the ACC responds too quickly to the changes in the target vehicle velocity, it might cause passenger discomfort and reduce energy efficiency due to the high rate of acceleration and deceleration. Thus, a research gap in ACC is identified, and this paper seeks to investigate an optimal ACC for driving comfort and fuel economy. This paper models the performance of a passenger vehicle with ACC using feedback control in Matlab Simulink. Three types of ACC control systems are simulated and compared; proportional-integral (PI) control, fuzzy logic control (FLC), and Linear-Quadratic Regulator (LQR). The performance of the control systems is simulated using set test scenarios, and an optimal ACC design is selected based on minimum acceleration, deceleration, and jerk performance, as defined in ISO 15622, following performance and minimum fuel consumption. Results show that the LQR controller has the best performance in meeting the ISO 15622, the lowest target vehicle the following root mean square error (RMSE), and the second-lowest New European Drive Cycle (NEDC) urban cycle fuel consumption.
 
 

Abstract: The development of modern driver assistance systems, such as Adaptive Cruise Control (ACC) and its enhanced version ACC+, represents a significant step towards improving road safety. This article analyzes the operation of these systems in the context of the causes and effects of traffic accidents. Based on the reconstruction of a specific traffic incident, a detailed assessment of the impact of ACC and ACC+ on traffic safety was conducted. The study is based on the analysis of measurable technical parameters that change under varying road conditions, both with the systems activated and deactivated. The paper also discusses examples of preventive strategies for similar incidents, utilizing new technologies applied in modern vehicles. Particular attention is given to differences in vehicle behavior under various operational scenarios of ACC and ACC+ systems, with a focus on their impact on the safety of road users. This article contributes to the development of knowledge on minimizing accident risks through advanced driver assistance systems. Keywords: ACC; ACC+; Safety; Traffic

<div class="section abstract"><div class="htmlview paragraph">Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system. And the input penalty factor of the adaptive cruise control system is designed as a variable parameter for the collaborative control model. On this basis, the longitudinal speed, reciprocal of speed and penalty factor are used as advance variables to design fuzzy rules of the system. And the nonlinear Takagi-Sugeno fuzzy model is established by fuzzifying the local linear model. Then, the vehicle following cruise controller considering the lateral stability is designed by parallel distribution compensation method. Finally, the TruckSim/Simulink co-simulation model was built for testing. The test results show that the proposed controller can improve the lateral stability of the vehicle during the following process, reduce the risk of instability of the vehicle, and improve the overall safety of the automatic driving system.</div></div>