Manual vs. adaptive cruise control – Can driver’s expectation be matched?

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.

In this article, we propose an extended multiclass gas-kinetic theory applicable to mixed traffic flow of manual and adaptive cruise control (ACC) vehicles. In the model, the acceleration/deceleration of ACC vehicles is specified explicitly using microscopic models. The macroscopic multiclass traffic equations are obtained from the proposed gas-kinetic model using the well-known method of moments. The influencing conditions to traffic flow stability with respect to a small perturbation are derived by the linear stability method. The analytical results show that ACC vehicles contribute to the improved stabilisation of traffic flow. The numerical simulations of our developed multiclass macroscopic model on a circular freeway support our analytical findings and indicate that increasing the penetration of ACC vehicles results in more stable traffic flow. Simulations of merging flows at an on-ramp in an open freeway are carried out to describe the effects of the penetration of ACC vehicles on the bottleneck capacity. It is found that around 30% ACC vehicles in traffic flow leads to significantly increased capacity and reduced travel time. We argue that, together with other microscopic models, our model provides a wider picture of the effects of ACC vehicles on traffic flow characteristics.

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

In this paper, a novel adaptive cruise control (ACC) algorithm based on model predictive control (MPC) and active disturbance rejection control (ADRC) is proposed. This paper uses an MPC algorithm for the upper controller of the ACC system. Through comprehensive considerations, the upper controller will output desired acceleration to the lower controller. In addition, to increase the accuracy of the predictive model in the MPC controller and to address fluctuations in the vehicle’s acceleration, an MPC aided by predictive estimation of acceleration is proposed. Due to the uncertainties of vehicle parameters and the road environment, it is difficult to establish an accurate vehicle dynamic model for the lower-level controller to control the throttle and brake actuators. Therefore, feed-forward control based on a vehicle dynamic model (VDM) and compensatory control based on ADRC is used to enhance the control precision and to suppress the influence of internal or external disturbance. Finally, the proposed optimal design of the ACC system was validated in road tests. The results show that ACC with APE can accurately control the tracking of the host vehicle with less acceleration fluctuation than that of the traditional ACC controller. Moreover, when the mass of the vehicle and the slope of the road is changed, the ACC–APE–ADRC controller is still able to control the vehicle to quickly and accurately track the desired acceleration.

<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>


 
 
 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.
 
 

ABSTRACTObjective: Adaptive cruise control (ACC), a technology that allows for automated car following, is becoming increasingly prevalent. Previous surveys have shown that drivers generally regard ACC as pleasant but that they have to intervene when the ACC reaches its operational limits. The former research has been mostly concerned with specific car brands and does not fully reflect the diversity of ACC types in traffic today. The objective of the present research was to establish the determinants of pleasure in using ACC.Methods: A 55-item online questionnaire was completed by Dutch users of diverse ACC systems.Results: Respondents (N = 182) rated their ACC highly, with a mean score of 8.0 on a scale from 1 (extraordinarily negative) to 10 (extraordinarily positive) and were most pleased with ACC on high-speed roads and in low-density traffic. Moreover, the findings point to specific operational limits such as associated with cut-in situations. Pleasure was greater for the types of ACC that are able to decelerate to a full stop, according to 48% of our sample. An analysis of the free-response items indicated that respondents who were displeased with ACC mentioned its occasional clumsiness and the dangerous situations it may evoke, whereas those who were pleased with ACC valued the complementarity of human and machine and emphasized the roles of responsibility and experience in using ACC.Conclusion: Pleasure in using ACC is a function of both technological advances and human factors.

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.

Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems

Improving traffic flow at a 2-to-1 lane reduction with wirelessly connected, adaptive cruise control vehicles

• The performance of Adaptive Cruise Control (ACC) is limited on road curves. • The Intelligent Speed Adaptation for Visibility (V-ISA) adjusts speed based on road conditions. • A driving simulation study assessed the influence of ACC and V-ISA on driver behaviour. • Integrating ACC and V-ISA significantly improved longitudinal driving performance. • The system achieved high system acceptance with no added mental workload. Advanced Driver Assistance Systems (ADAS) have become an integral part of modern vehicles, significantly changing the driving experience. Adaptive Cruise Control (ACC) assists drivers in maintaining a constant speed and a safe following distance; however, it does not moderate the speed along curves when sight limitations occur, thus compromising safety. To address these challenges, this study integrates the Intelligent Speed Adaptation for Visibility (V-ISA) system with ACC. The V-ISA system dynamically adjusts the vehicle speed based on real-time visibility conditions, thereby enhancing safety on road curves where sight distance is restricted. Additionally, this study evaluates the impact of the ACC + V-ISA system on driver performance, specifically focusing on speed management, lateral vehicle control, and driver experience with assistance systems. The study was conducted in a simulated environment with forty-five participants. The results revealed that the ACC + V-ISA system effectively reduced vehicle speed on curves with limited visibility. The ability of the system to regulate speed according to prevailing road conditions highlights its potential to improve road safety. Moreover, the findings suggest that using the V-ISA system integrated with ACC maintains user acceptance and satisfaction while imposing no additional mental workload compared to using ACC alone. These insights are crucial for the future design of ADAS and autonomous vehicles and emphasize the importance of introducing this system into vehicles. LED bar colour

Much of the existing research on drivers’ understanding of adaptive cruise control (ACC), a type of advanced driver assistance system, was conducted several years ago. Through an online survey, this study aimed to assess ACC knowledge among ACC owners and non-owners now that this system is more widely available. Along with knowledge of ACC features and limitations, demographic information, experience with technology, and experience with ACC (for owners) were also collected to investigate which factors predicted knowledge of ACC features and limitations. Results showed that owners today may have a better understanding of some of the main limitations of ACC compared with research conducted over 10 years ago. However, a large percentage of owners still had misperceptions about their ACC system. While owners had a slightly higher percentage of correct answers overall, they did not differ from non-owners in their knowledge of limitations. As this technology is becoming more common, even non-owners may be becoming aware of common limitations; owning and using ACC does not seem to result in a better system understanding. Higher income was associated with a higher percentage of correct responses on the ACC knowledge questionnaire for both owners and non-owners, and for non-owners, higher education level was also significantly associated with a higher percentage of correct responses. Future research should focus on developing training materials that are accessible to all drivers, so that drivers in lower education and income groups are also supported to understand how advanced driver assistance systems work and benefit from these technologies.

Adaptive Cruise Control (ACC) is designed for convenience to maintain a set speed and specified distance from a lead vehicle. However, use of ACC may change driving patterns and perceptions over time. Many drivers perceive safety benefits associated with ACC even though the safety implications are not always clear. This study examined the factors that can influence the frequency of ACC use with surveys distributed to ACC owners in Washington State. A cluster analysis was conducted to group drivers based on how often they activated and used ACC under various driving scenarios. Four clusters emerged that showed a range of use from those who rarely used ACC in any situation (low engagement group) to those who used it for almost all situations regardless of whether it is appropriate or not (high engagement group). An ordered logit model was used to predict the likelihood of being in one of the four clusters. Drivers that were less likely to use ACC in distracting or impaired situations tend to be older, were not willing to re-purchase a similar vehicle with ACC, and were generally confused on how to use the cruise speed setting. Drivers who reported higher overall use of ACC also used the system in situations that can be considered distracting or risky, which can negate the overall benefits of ACC.

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

In every day our daily needs we are depending on transport system. Transport mode may be two wheeler, four wheelers etc. In metro there is a huge traffic of vehicles, there may be chances of accidents. The most of the accidents are due to inability of the driver to control the vehicle. In order to improve the safety of the driver, need to add some auto control for the vehicle. The driver assistance is one of the auto control system. In driver assistance there are many subsystems. Adaptive cruise control is one of the major driver assistance sub systems. The Adaptive cruise control was implemented by using Real time operating system with preemptive priority based scheduler. It takes more memory and time. Time triggered cooperative scheduler is one of the most efficient and predictable scheduler. It is designed for controlling adaptive cruise control tasks. It is implemented using ARM7 based Microcontroller, ultrasonic sensor, Throttle valve sensor and speed sensor. The whole system is placed in host vehicle, vary the speed of the host vehicle so that to keep minimum distance between host and target vehicle. The TTC scheduler for adaptive cruise control is simulated using keil IDE tool. The application tasks are controlled by using efficient Time triggered cooperative scheduler. The adaptive cruise control is controlled by TTC scheduler is written in embedded `c'. The simulated results are compared with existing cooperative schedulers. It is proved from the results that the TTC scheduler takes less time and less memory.