Modifying Adaptive Cruise Control Systems for String Stable Stop-and -Go Wave Control

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.

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

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.

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

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.

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.

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

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

Situation awareness and workload in driving while using adaptive cruise control and a cell phone

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.

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.

Adaptive cruise control (ACC) is one of the modern vehicle digitalization inventions over the world. It automatically adjusts the speed of a subject vehicle to match the speed of the vehicle in front of the subject vehicle. The ACC systems will be the more advanced driving assistant techniques in the next generation of Bangladesh. Therefore, a broad review on the ACC systems has been performed in the present study. In this study, the past researches on ACC systems over the world, the present scenario of ACC systems in Bangladesh, and future challenges in Bangladesh to implement ACC systems have been studied in detail. From the study, it is noticed that ACC systems were used in the world in the very beginning of 1995. These systems have been paid high attention to reduce the accident and accidental death rate, to save the energy, to reduce the impacts on the environment, and to fulfill the sustainable development goals (SDGs) declared by the United Nations (UN). It was first imported to Bangladesh in 2015, but most of the drivers do not feel comfortable on applying it. Studies have found some drawbacks of the ACC systems in banking of roads, turning points on roads, and making drivers less conscious. The system also has some issues in terms of traffic flow stability and control over lane positions. This article also attempts to summarize the current road condition, workability of ACC systems, drawbacks of ACC, the traffic flow stability, and existing challenges for ACC systems in Bangladesh’s road conditions.