Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

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.

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.

<div class="section abstract"><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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<sup>®</sup>, RT range<sup>®</sup>, 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<sup>®</sup> 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<sup>®</sup>.</div></div>

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.

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.

Towards reducing the number of crashes during hurricane evacuation: Assessing the potential safety impact of adaptive cruise control systems

ABSTRACTThe main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.

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.

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

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.

In this paper, a pure electric vehicle (PEV) equipped with adaptive cruise control (ACC) system is studied for a vehicle‐following process. And a multiobjective optimization algorithm for ACC system is proposed in a model‐predictive control (MPC) framework for optimizing safety, tracking capability, driving comfortability and energy consumption. The longitudinal dynamics of the ACC system are modeled, which not only considers the vehicle spacing and speed, but also introduces the acceleration and the change rate of acceleration (jerk) for the host vehicle and fully considers the influence of the acceleration of the leading vehicle. The improvement of driving comfortability and the reduction of energy consumption are achieved mainly by optimizing the acceleration and jerk of host vehicle. Some optimized reference trajectories are introduced to MPC for improving driving comfortability of host vehicle. The performances of the multiobjective upper level algorithm combined with the PEV model are evaluated for three representative scenarios. The results demonstrate the effectiveness of the proposed algorithm.

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.

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.

In the Netherlands, a field operational test was conducted in 2006 to assess the impact of two advanced driver assistance systems (ADAS), namely adaptive cruise control (ACC) and lane departure warning (LDW) systems. The research goal was to estimate the effects of these systems on road capacity, safety and emissions. In this paper we focus on the interaction between driver and ACC system using the data from this FOT. It is found that drivers choose headway settings according to their manual driving behavior. Moreover, they often keep the system deactivated under dense traffic conditions. It is also observed from the data that the system, once de-activated, either automatically or manually, needs some time to become again active. These findings imply that, even with 100% cars on the road equipped with ACC, manual driving behavior will still be a determinant factor.