Adaptive Cruise Control System Aspects and Development Trends

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

Vehicle automation is regarded as one of the most promising technologies in transportation networks to alleviate congestion, improve safety and energy efficiency. Adaptive cruise control (ACC) systems, which serve as the first step of automation, are already standard equipment in many commercially available vehicles. Therefore, the observation-based assessment of such systems individually and in platoon formations is very appealing. The thematic focus of this study is laid on investigations into the impact of ACC systems on energy and fuel consumption inside the platoon. High-resolution data from two experimental car-following campaigns consisted of platoons with ACC-equipped vehicles are collected. Two driving modes are considered, human- and ACC-driven vehicles. Results are presented with four independent energy consumption models. The findings reveal that an upstream energy propagation was observed inside the platoon by the ACC participants, indicating that ACC systems are less efficient than human drivers. On the positive side, ACC systems do not generally fail inside a platoon, keeping steady time-gaps. They seem to operate based on a constant headway policy, and their performance is conditioned to the environment. ACC drivers in protected environments and campaigns might perform better but should be (ideally) tested in adverse environments.

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

A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinforcement learning approach is presented to obtain the desired acceleration controller. In the lower level, throttle and brake controllers calculate the required throttle and/or brake signals based on the desired longitudinal acceleration. Feed-forward neural networks are used to implement the actor and critic components of the SAC learning algorithm. An online learning mechanism is introduced to implement the SAC training process. dSPACE simulator is used to verify the effectiveness of the ACC system. Typical emergency braking scenario is simulated to test the adaptability of the ACC system. Road condition change (e.g. wintry or wet conditions) simulation is first investigated to evaluate the robustness of the ACC system. Performance of the proposed ACC system is proved to be very practical.

<div class="htmlview paragraph">The results from this field operational test (FOT) show differences in the longitudinal control of headway between manual driving and driving with an adaptive cruise control (ACC) system. Based on measurements made during naturalistic driving by laypersons, it is found that the ACC mode of driver assistance contributes to longer following distances and is perceived to improve driver comfort and convenience. Observations on desirable improvements for ACC systems are presented.</div>

To address the problem caused by a conventional adaptive cruise control (ACC) system, which hinders drivers from changing lanes, in this study we propose a novel coordinated ACC system with a lane-change assistance function, which enables dual-target tracking, safe lane change, and longitudinal ride comfort. We first analyze lane-change risk by calculating minimum safety spacing between the host vehicle and surrounding vehicles and then develop a coordinated control algorithm using model predictive control theory. Tracking performance is designed on the basis of tracking errors of the host car and two leading vehicles, safety performance is realized by considering the safe distance between the host car and surrounding vehicles, and ride comfort performance is realized by limiting the vehicle's longitudinal acceleration. Driver-in-the-loop tests performed on a driving simulator confirm that the proposed ACC system can overcome the disadvantages of conventional ACC and achieves multiobjective coordination in the lane-change process.

This letter addresses the important issue of energy inefficiency and air pollution resulting from stop-and-go waves on highways by introducing a novel controller called the Attenuative Kerner's Model (AKM). The objective of AKM is to enhance an existing Adaptive Cruise Control (ACC) system to improve vehicle following in stop-and-go waves. It is designed as a hybrid controller that is compatible with a wide range of commercial vehicles equipped with ACC. The article demonstrates the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local</i> string stability of the controller. Next, it presents a comparative analysis of AKM against two benchmarks: a human driver and a commercial ACC system, through numerical simulations and physical experiments using a 2022 Cadillac XT5. The findings reveal that AKM substantially outperforms both the human driver and the ACC in controlling low-speed, stop-and-go waves. The results indicate that AKM could act as an additional control layer for existing ACC systems, potentially improving their operational efficiency and reducing pollution emissions, thus contributing to more sustainable highway transportation.

Why anticipatory sensing matters in commercial ACC systems under cut-in scenarios: A perspective from stochastic safety analysis.

Current adaptive cruise control (ACC) systems adopt a fixed desired time headway, which often leads to abrupt speed changes in response to a close new leader after a cut-in or a lane change. These are disruptive maneuvers. In contrast, human drivers are willing to accept spacings much smaller than equilibrium values at the onset of these maneuvers and then gradually increase the spacing until they again reach equilibrium. This process, which typically lasts around 20 s or 30 s, is known as driver relaxation, which improves comfort and capacity. Therefore, this paper aims to incorporate relaxation into ACC systems. Based on the open-source factory-level ACC platform Openpilot by Comma.ai, the paper proposes a feasible relaxation model compatible with recent market ACC systems. The model is tested using simulation and road tests using a 2019 Honda Civic with its stock ACC hardware. The study further investigates the potential benefits of relaxation ACC on traffic operations. Comparative simulations suggest that incorporating relaxation into ACC can help: (i) reduce the magnitude of speed perturbations in both cut-in vehicles and followers; (ii) stabilize the lane-changing (LC) traffic by reducing the speed variance and preventing the lateral propagation of congestion; and (iii) increase the average flow speed and capacity after a bottleneck occurs.

This article is motivated by the lack of empirical data on the performance of commercially available Society of Automotive Engineers level one automated driving systems. To address this, a set of car following experiments are conducted to collect data from a 2015 luxury electric vehicle equipped with a commercial adaptive cruise control (ACC) system. Velocity, relative velocity, and spacing data collected during the experiments are used to calibrate an optimal velocity relative velocity car following model for both the minimum and maximum following settings. The string stability of both calibrated models is assessed, and it is determined that the best-fit models are string unstable, indicating they are not able to prevent all traffic disturbances from amplifying into phantom jams. Based on the calibrated models, we identify the consequences of the string unstable ACC system on synthetic and empirical lead vehicle disturbances, highlighting that commercial ACC platoons of moderate size can dampen some disturbances even while being string unstable. The primary contributions of this article are the development of a data-driven approach to calculate string stability of ACC systems, and the collection and interpretation of a dataset to understand the car following behavior of a commercial ACC system.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle. Some high precision sensors were mounted in both test vehicles, such as RT 3002&lt;sup&gt;®&lt;/sup&gt;, RT range&lt;sup&gt;®&lt;/sup&gt;, radar and pedal pressure sensor. The traffic vehicle was set in different driving modes, include cruise mode, decelerating slowly, accelerating slowly, hard braking, hard acceleration, cutting out the lane, cutting into the line and sinusoidal movement. The host vehicle was operated behind the traffic vehicle by 84 drivers selected randomly. The drivers’ driving styles were evaluated via both subjective and objective indicators. The subjective indicators were based on the questionnaires by interviewing the drivers and the passengers. The objective indicators were based on the parameters extracted from the specific test conditions. The vehicle-following data were collected and analyzed via MATLAB&lt;sup&gt;®&lt;/sup&gt; which was further trained by Gaussian Mixture Model. With regarding to the multi-mode switching strategy, the parameters of different driver’s characteristics were considered in the fuzzy PID control method to improve the adaptivity and accuracy in the switching process. Meanwhile, different fuzzy rules and fuzzy membership functions were applied for every type of drivers to calculate the best parameters for the personalized ACC control strategy. The control modes include cruise, steady following, approaching and hard braking. The different driving characteristics were also added into every control strategy via road experimental data and analyzed results from Gaussian Mixture Model. Compared with the existing ACC, the system proposed in this paper has great advantages on not only the adaptivity and human acceptance, but also the performance of the system itself which was verified on the simulation platform PanoSim&lt;sup&gt;®&lt;/sup&gt;.&lt;/div&gt;&lt;/div&gt;

&lt;div class="section abstract"&gt; &lt;div class="htmlview paragraph"&gt;This Recommended Practice provides a taxonomy for motor vehicle &lt;i&gt;driving automation systems&lt;/i&gt; that perform part or all of the &lt;i&gt;dynamic driving task&lt;/i&gt; (&lt;i&gt;DDT&lt;/i&gt;) on a &lt;i&gt;sustained&lt;/i&gt; basis and that range in level from &lt;i&gt;no driving automation&lt;/i&gt; (level 0) to &lt;i&gt;full driving automation&lt;/i&gt; (level 5). It provides detailed definitions for these six levels of &lt;i&gt;driving automation&lt;/i&gt; in the context of &lt;i&gt;motor vehicles&lt;/i&gt; (hereafter also referred to as “&lt;i&gt;vehicle&lt;/i&gt;” or “&lt;i&gt;vehicles&lt;/i&gt;”) and their &lt;i&gt;operation&lt;/i&gt; on roadways. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of &lt;i&gt;driving automation features&lt;/i&gt; equipped on &lt;i&gt;motor vehicles&lt;/i&gt; in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve users of &lt;i&gt;vehicles&lt;/i&gt; of all classes and &lt;i&gt;driving automation&lt;/i&gt; levels (including no &lt;i&gt;driving automation&lt;/i&gt;), as well as motorcyclists, pedal cyclists, and pedestrians.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;The levels apply to the &lt;i&gt;driving automation feature(s)&lt;/i&gt; that are engaged in any given instance of on-road &lt;i&gt;operation&lt;/i&gt; of an equipped &lt;i&gt;vehicle&lt;/i&gt;. As such, although a given &lt;i&gt;vehicle&lt;/i&gt; may be equipped with a &lt;i&gt;driving automation system&lt;/i&gt; that is capable of delivering multiple &lt;i&gt;driving automation features&lt;/i&gt; that perform at different levels, the level of &lt;i&gt;driving automation&lt;/i&gt; exhibited in any given instance is determined by the &lt;i&gt;feature(s)&lt;/i&gt; that are engaged.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;This document also refers to three primary actors in &lt;i&gt;driving&lt;/i&gt;: the (human) &lt;i&gt;driver&lt;/i&gt;, the &lt;i&gt;driving automation system&lt;/i&gt;, and other &lt;i&gt;vehicle&lt;/i&gt; systems and components. These other &lt;i&gt;vehicle&lt;/i&gt; systems (or the &lt;i&gt;vehicle&lt;/i&gt; in general terms) do not include the &lt;i&gt;driving automation system&lt;/i&gt; in this model, even though as a practical matter a &lt;i&gt;driving automation system&lt;/i&gt; may actually share hardware and software components with other &lt;i&gt;vehicle&lt;/i&gt; systems, such as a processing module(s) or operating code.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;The levels of &lt;i&gt;driving automation&lt;/i&gt; are defined by reference to the specific role played by each of the three primary actors in performance of the &lt;i&gt;DDT&lt;/i&gt;. “Role” in this context refers to the expected role of a given primary actor, based on the design of the &lt;i&gt;driving automation system&lt;/i&gt; in question and not necessarily to the actual performance of a given primary actor. For example, a &lt;i&gt;driver&lt;/i&gt; who fails to &lt;i&gt;monitor&lt;/i&gt; the roadway during engagement of a level 1 adaptive cruise control (ACC) system still has the role of &lt;i&gt;driver&lt;/i&gt;, even while s/he is neglecting it.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;&lt;i&gt;Active safety systems&lt;/i&gt;, such as electronic stability control and automated emergency braking, and certain types of driver assistance systems, such as lane keeping assistance, are excluded from the scope of this &lt;i&gt;driving automation&lt;/i&gt; taxonomy because they do not perform part or all of the &lt;i&gt;DDT&lt;/i&gt; on a &lt;i&gt;sustained&lt;/i&gt; basis and, rather, merely provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of &lt;i&gt;active safety systems&lt;/i&gt;, their intervention does not change or eliminate the role of the &lt;i&gt;driver&lt;/i&gt; in performing part or all of the &lt;i&gt;DDT,&lt;/i&gt; and thus are not considered to be &lt;i&gt;driving automation&lt;/i&gt;.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;It should, however, be noted that crash avoidance features, including intervention-type &lt;i&gt;active safety systems&lt;/i&gt;, may be included in &lt;i&gt;vehicles&lt;/i&gt; equipped with &lt;i&gt;driving automation systems&lt;/i&gt; at any level. For &lt;i&gt;ADS&lt;/i&gt;-equipped &lt;i&gt;vehicles&lt;/i&gt; (i.e., levels 3-5) that perform the complete &lt;i&gt;DDT&lt;/i&gt;, crash avoidance capability is part of &lt;i&gt;ADS&lt;/i&gt; functionality.&lt;/div&gt;&lt;/div&gt;

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.

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