Adaptive Cruise Control Controller Research Articles

Stability of Adaptive Cruise Control of Automated Vehicle Platoon under Constant Time Headway Policy

Traffic congestion is becoming more prevalent as the number of vehicles on the roads continues to rise.To shorten travel times and enhance driver comfort, a range of Advanced Driver Assistance Systems (ADAS) has been developed to assist drivers in urban areas and on highways. The demand for increasing road capacity has introduced the concept of vehicle platooning, where the use of the Adaptive Cruise Control (ACC) system is a key function within the advanced driver assistance (ADAS) technology, this technology manages the vehicle's longitu-dinal control in specific driving conditions. Cars equipped with ACC can efficiently maintain a set distance from the vehicle ahead, easing the driver’s workload while offering advantages such as improved road capacity, lower fuel consumption, and reduced pollution emissions. However, they can be susceptible to string instability, resulting in the amplification of oscillations caused by speed variations along the platoon's rear. This paper presents a string stability analysis of car platoons equipped with ACC system based on a heuristic method by choosing of the constant time headway policy (CTHP). The constant time headway policy selection for string stability is based on the Nyquist diagram of the transfer function of the spacing errors between two cars. A platoon operated by using distance-based ACC control structures is implemented. These structures employ a linear quadratic regulator (LQR) using a dual integrator. The simulation results were acquired by modeling and simulating the studied platoon within Matlab/Simulink.

Analysis of a Simplified Predictive Function Control Formulation Using First Order Transfer Function for Adaptive Cruise Control

This paper presents a formulation and analysis of a low computation Predictive Functional Control (PFC), which is a simplified version of the more advanced Model Predictive Control (MPC) for an Adaptive Cruise Control (ACC) system by using a representation of first order closed-loop transfer function. In this work, a non-linear mathematical model of vehicle longitudinal dynamics is considered as a control plant. Then, a simple Proportional Integral (PI) controller is employed as an inner loop to identify the first-order relationship between its actual and desired trajectory speed according to the reasonable time constant based on the logical response of pedals pressing. To directly control the whole plant, the PFC is formulated as an outer loop to track the desired speed together with the convergence rate based on a user preference while satisfying constraints related to acceleration and safe distancing. Since PFC is formulated based on the first-order transfer function, the prediction and tuning processes are straightforward and specific to this system. The simulation results confirm that the proposed controller managed to track the desired speed while maintaining a comfortable driving response. Besides, the controller also can retain safe distancing during the car following application, even in the presence of unmeasured disturbance. In summary, this framework can avoid the need to formulate an inverse non-linear model that is typically used when deploying a hierarchical control structure to compute the throttle and brake pedals pressing as it has been replaced with an inner loop PI controller. The performance also is comparable yet more conservative due to the simplification. These findings can become a good reference for designing and improving the ACC controller, as the framework can be easily generalized for any type of vehicle for future work.

Adaptive Cruise Control System: A Literature Survey

Adaptive cruise control (ACC) assists automobiles in preserving a safe following distance and adhering to speed limits. This advanced driver-assistance system (ADAS) modifies the car's speed to keep a safe gap from oncoming traffic. All vehicle types include combustion engines, pure electric vehicles, hybrid electric vehicles, and methods of operation; controllers are designed to react to cruise control signals and provide an efficient route profile according to the surrounding environment and instantaneous vehicle performance characteristics. ACC uses a perception system to measure the forward vehicle's current distance, speed, and acceleration relative to the host vehicle. Some of these systems use lasers, radar, cameras, or a combination of these sensors to determine the distance and speed of the leading vehicle. Other systems even use wireless communication to collect data from surrounding vehicles. ACC can help reduce stress on long drives, increase road safety, prevent accidents, and enhance traffic flow energy efficiency. This paper aims to introduce a comprehensive study of the research on ACC and mention different controlling techniques used to deal with the problem. Furthermore, a discussion of each method with its cons and pros is mentioned too. First, an introduction to the ACC system and control approaches with a brief discussion of their main principle is presented. Next, various application cases of ACC are presented. These applications include lateral dynamics, wireless technology, energy vehicles, navigation data, and practical experimental tests. At last, future guidance and challenges are discussed.

Adaptive Cruise Control Based on Safe Deep Reinforcement Learning.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

Comparison of Fuzzy Logic Control and Model Predictive Control for a Smart Adaptive Cruise Control Vehicle System

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.

Coordinated adaptive cruise control with integration of driving behaviors based on prediction for surrounding vehicles status

The adaptive cruise is an important vehicle control process for unmanned vehicles. This paper studies coordinated adaptive cruise control (ACC) of energy-saving in an intelligent networked environment, aiming to integrate control for common driving behaviors (cruising, following, lane-changing) by hierarchical control. The decision-making layer for integrating three driving behaviors is designed to improve the economy and passing time. Respectively, cruising strategy is optimized by considering efficient working area of motor; following strategy is constructed by dynamically adjusting the following distance with the state changes of the surrounding vehicles; lane-changing strategy is developed by considering the motor working efficiency of target speed. To improve the predictability of decision-making, non-linear autoregressive neural network (NARX) is introduced to predict the surrounding vehicle state. Furthermore, receiving the control reference from the decision layer, the controller is designed to realizes the tracking control of vehicle speed and following distance based on model predictive control (MPC), which is co-optimized for tracking, economy, and comfort. Eventually, the effectiveness of the coordinated ACC control is verified based on the hardware in the loop (HiL) test. The experiment results show that the vehicles with coordinated ACC can shorten the passing time and reduce the driving energy consumption to a certain extent, especially in the medium and low speed conditions.

Lane-changing control for hybrid electric vehicles with dedicated hybrid transmission based on robust model predictive control

Car-following and lane-changing are extremely important for vehicles. The traditional adaptive cruise control (ACC) strategy has various drawbacks due to the complicated driving conditions. A new control strategy of robust model predictive control (RMPC) for car-following and lane-changing is proposed based on a new type of hybrid electric vehicle (HEV) with dedicated hybrid transmission (DHT-HEV). The control model includes a vehicle model, a following and lane-changing model and an RMPC controller. The vehicle model integrates the DHT-HEV and dynamic models with a longitudinal dynamic model, seven degrees of freedom (DOF) vehicle dynamic model and three DOF control model. The following and lane-changing model is described as car-following and lane-changing models of two-quintic function. The RMPC controller is used to balance the control of vehicle longitudinal and lateral dynamics with the lateral acceleration disturbance. Simulation results demonstrate that the RMPC controller can track the reference trajectory during two lane-changing process when the test scenarios of different accelerations are set compared with the conventional ACC control. Different Np (predictive horizon) and Nc (control horizon) are also analysed to improve the solving performance of RMPC. Results indicate that the solving performance of the system is optimal when Np = 30 and Nc = 1. Furthermore, different weight coefficient matrixes ( Q and R) are taken into the consideration by coordinating the car-following performance and lateral stability. Accordingly, the controllers of RMPC-LC, RMPC-ACC and RMPC-LA are set, demonstrating that the control of RMPC-LA best balances two aspects and is always within a safe car-following distance. Meanwhile, the hardware in the loop (HIL) is utilised to verify the effectiveness of the proposed RMPC control strategy, which comprises model compilation, the establishment connection between the D2P ECU and the simulator and control input.

Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

Reducing fuel consumption in platooning systems through reinforcement learning

Fuel efficiency in platooning systems is a central topic of interest because of its significant economic and environmental impact on the transportation industry. In platoon systems, Adaptive Cruise Control (ACC) is widely adopted because it can guarantee string stability while requiring only radar or lidar measurements. A key parameter in ACC is the desired time gap between the platoon's neighboring vehicles. A small time gap results in a short inter-vehicular distance, which is fuel efficient when the vehicles are moving at constant speeds due to air drag reductions. On the other hand, when the vehicles accelerate and brake a lot, a bigger time gap is more fuel efficient. This motivates us to find a policy that minimizes fuel consumption by conveniently switching between two desired time gap parameters. Thus, one can interpret this formulation as a dynamic system controlled by a switching ACC, and the learning problem reduces to finding a switching rule that is fuel efficient. We apply a Reinforcement Learning (RL) algorithm to find a time switching policy between two desired time gap parameters of an ACC controller to reach our goal. We adopt the proximal policy optimization (PPO) algorithm to learn the appropriate transient shift times that minimize the platoon's fuel consumption when it faces stochastic traffic conditions. Numerical simulations show that the PPO algorithm outperforms both static time gap ACC and a threshold-based switching control in terms of the average fuel efficiency

Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

Binary search tree-based explicit MPC controller design with Kalman filter for vehicular adaptive cruise system

The adaptive cruise control (ACC) system has received significant attention due to traffic safety improvement, traffic throughput increment, and energy conservation. Model Predictive Control (MPC) has been successfully applied in the control of multi-objective vehicular ACC. However, as a state-feedback policy, MPC requires full state measurement. Meanwhile, the real-time performance of MPC is intractable. This paper proposes to estimate the state value and disturbance value with an extended state Kalman filter to deal with measurement uncertainty. The Kalman filter is based on an augmented state-space model which takes the disturbance term as a new state. To improve real-time performance, this paper suggests employing an explicit MPC (EMPC) based on binary search tree to move the online computational burden of MPC to offline computation by multi-parametric quadratic programming (MPQP). An improved algorithm to solve the MPQP problem offline is proposed, which is initialized discarding the requirement of parameters range, while previous methods need. In the simulated measurement process, the extended state Kalman filter can effectively reduce noise and accurately estimate the value of state and disturbance in the car-following model. Simulations in different scenarios are performed to test the effectiveness of the proposed ACC controller. Results show that the proposed EMPC for the ACC system can improve the real-time performance of the MPC with little loss of performance. On average, the EMPC via binary search is 95.8 times faster than the MPC with the same parameters as EMPC for the studied ACC system. And it has better overall performance compared with the ACC with collision avoidance (CA-ACC) method.

Coordinated String Stability Control for Vehicle Platoons with Faster Settling

Coordinated adaptive cruise control (CACC) is a type of adaptive cruise control (ACC) that enables the driver to set the vehicle speed and the distance between current and preceding vehicles as well as helps to gather the data from vehicles around it and form a platoon. The main objectives for the paper are to implement coordinated cruise control, to minimize steady state spacing error while maintaining the string stability and to get earlier settling of string stability under external disturbances. Design, simulation, and testing of CACC under various conditions have been performed. Both feedback and feed forward controls have been employed for implementing the system model with controllers. It is assumed that all vehicles are having wireless access to share the speed and distance related information. It is observed that settling time for the string stability has been improved. The model has been simulated under disturbances and results are compared with existing ACC control. The simulation results validate proposed model in terms of settling of string stability.

Researches on Adaptive Cruise Control system: A state of the art review

Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC

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.

Optimal Design of an Adaptive Cruise Control System for Driving Comfort and Fuel Economy


 
 
 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.
 
 

Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments

Emerging automated vehicle (AV) technologies are increasingly being deployed around the world and it is only a matter of time before the transportation landscape changes dramatically. Unfortunately, those changes cannot be well predicted due to the lack of empirical data. But adaptive cruise control (ACC) vehicles are common in the market and can be used to fill this gap. In this paper, we aim to characterize the empirical car-following behaviors of a commercial ACC system and understand how ACC behaves in different conditions and the underlying impact mechanism. It is found that for a single ACC: (i) the ACC response time is comparable to human drivers but much larger than the ACC controller time gap and it exhibits small variance, (ii) the ACC response can amplify or dampen an oscillation, (iii) after the oscillation, the stabilization process can exhibit overshooting or undershooting, and (iv) these CF behaviors depend largely on the ACC headway setting, speed level, and leader stimulus, which produce the impacts directly and/or indirectly through the mediation of earlier ACC behaviors. For a three-vehicle platoon, our main finding is that the change from one ACC vehicle to the next is progressive for oscillation growth, and regressive for deceleration, acceleration, and overshooting. This implies that in long platoons, oscillation amplitude tends to exacerbate very quickly, which forces ACC vehicles further upstream to apply very strong braking followed by a strong acceleration. This can cause significant overshooting and safety hazards.

Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of them is the adaptive cruise control (ACC) system. The ACC system is a continuation of research on cruise control (CC) system, which integrates spacing control with the existing velocity control on the CC system. The vehicles with an ACC system guarantee traffic safety while at the same time ensure a well-driving sense. Many studies have demonstrated numerous control techniques applied as ACC controllers to accomplish uncertainty and perturbation issues. Nevertheless, most of the existing papers assumed the model vehicle dynamics as a linear time-invariant (LTI) system while designing the ACC controller. This paper proposed an ACC controller using the gain scheduling technique to deal with the model vehicle dynamics as a linear parameter varying (LPV) system. The passenger vehicle’s mass varies during ACC operation depending on how many passengers or loads on the vehicle’s trunks. Later, the vehicle’s mass is estimated by recursive least square (RLS) with a forgetting factor. Then, the disk margin is utilized to provide the high-level robustness at each operating or “frozen” point. The robustness performance will be analyzed using the worst-case gain metric while the uncertainty is modeled by integral quadratic constraints (IQC). The LPV system behavior, such as the rate vehicle’s mass, is also considered in the analysis. The effectiveness algorithm is validated through joint simulation between Matlab/Simulink and PreScan. The last, the comparison performance between gain scheduling and fixed gain ACC controller is evaluated.

Effect of the Uncertainty Level of Vehicle-Position Information on the Stability and Safety of the Car-Following Process

In recent years, the adaptive cruise control (ACC) system has become widely adopted. The vehicle-positioning system plays an important role in automotive platoon driving and can provide vehicle-position information through vehicle-to-vehicle communication. However, the acquired vehicle-position information often presents uncertainties given the existence of variations in the traffic environment and of noise in the vehicle-positioning system. These uncertainties, in turn, influence the car-following performance of a vehicle platoon. In this study, we employ the desired safety margin (DSM) model, an ACC control strategy, to investigate the influence of the uncertainty level of vehicle-position information (ULVPI) on string stability and car-following safety. The stability criterion of the DSM model with ULVPI is derived through linear stability theory. Theoretical analysis results are verified by numerical simulations. Analytical results indicate that a negative ULVPI value can expand the stable region and improve string stability and that a positive ULVPI value can reduce delay time. Moreover, a negative ULVPI value can improve car-following safety during the stopping and evolution processes, whereas a positive ULVPI values can increase the safety margins of vehicles during the starting process. In the car-following process, a negative ULVPI value can improve car-following safety and reduce rear-end collision risk. The variation in ULVPI values can improve string stability and reduce the risk of rear-end collisions when mean and standard deviation are reasonable. Therefore, the improvement of a vehicle’s dynamic performance, string stability, and safety relies on the effect of ULVPI in different traffic scenarios. These results are useful in designing different control strategies that stabilize traffic flow and improve traffic safety for vehicles with ACC systems.

Drivers’ driving style and their take-over-control judgment

Advanced driver assistance systems (ADASs), which help a driver drive a car safely and easily (e.g., warning alerts, steering control, and brake/acceleration pedal operation), have increased in popularity. However, such systems have not yet been perfected. Sometimes, humans must take over control from the systems; otherwise, they can cause an accident. In this study, we focused on one of the ADASs, adaptive cruise control (ACC), which automatically maintains a selected distance from the preceding car, and investigated individual differences in take-over-control judgment and related factors. The candidate factors included driver’s manual driving style, driving performance without the ACC, and the usability evaluation of ACC. Ten participants repeated the short, strictly controlled trials in a driving simulator (DS), with a varying value of only one parameter (deceleration of the preceding car) affecting the need for intervention. First, we confirmed that the participants made the judgment based on the dangerousness of the situation and that there were individual differences in the take-over-control judgments. Some participants intervened in the ACC control in less dangerous trials, whereas other participants did not, even if their own car got very close to the preceding car. We conducted a correlation analysis and confirmed the results with the estimation of the confidence interval using a bootstrap method. As a result, we found that driving style and driving performance without ACC had a stronger relationship to the number of interventions, rather than the usability evaluation. In particular, methodical drivers, who obeyed traffic rules and manners, began to intervene in less dangerous situations. The tendency to avoid utilizing brake operations was also related to take-over-control judgment. This might be because the participants intervened by pressing the brake pedal. Our study showed that drivers’ driving style could affect the usage of ACC independently from the performance of the ACC.

Spacing Policies for Adaptive Cruise Control: A Survey

Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance for drivers to enhance safety and reduce workload. As the core of all ACC control algorithms, the spacing policy plays a crucial role in various aspects. This paper presents a comprehensive survey on spacing policies for existing ACC solutions in the literature. The objectives of this paper are to clarify the operating mechanisms and characteristics of the common spacing policies, and to reveal their advantages and shortcomings by means of a comparative study. In this survey, the general evaluation criteria for spacing policies are first introduced. Then, the existing spacing policies are categorized into different types according to their operating mechanisms, and their characteristics are carefully reviewed and explained. A comparative study is followed to analyze the performances of five typical spacing policies in the literature, including the constant spacing policy, constant time headway, traffic flow stability, constant safety factor and human driving behavior spacing policies. The contents provided in this paper serve as a tool for understanding current ACC spacing policies, and pave the way for future ACC enhancement.