Adaptive Cruise Control System Research Articles

OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems

Adaptive Cruise Control (ACC) systems are becoming increasingly available as a standard equipment in modern commercial vehicles. Their penetration rate in the fleet is constantly increasing, as well as their use, especially under freeway conditions. At the same time, limited information is openly available on how these systems actually operate and their differences depending on the vehicle manufacturer or model. This represents an important gap because as the number of ACC vehicles on the road increases, traffic dynamics on freeways may change accordingly, and new collective phenomena, which are only marginally known at present, could emerge. Yet, as ACC systems are introduced as comfort options and their operation is entirely under the responsibility of the driver, vehicle manufacturers do not have explicit requirements to fulfill nor they have to provide any evidence about their performances. As a result, any safety implication connected to their interactions with other road users escapes any monitoring and opportunity of improvement.This work presents a set of experimental car-following campaigns, providing an overview of the behavior of commercial ACC systems under different driving conditions. Furthermore, the suggestion of a unified data structure across the different tests facilitates comparison between the different campaigns, vehicles, systems and specifications. The complete data is published as an open-access database (OpenACC), available to the research community. As more test campaigns will be carried out, OpenACC will evolve accordingly.The activity is performed in the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread introduction. In this light, OpenACC, over time, also aims at becoming a reference point to study if and how the parameters of such systems need to be regulated, how homogeneously they behave, how new ACC car-following models should be designed for traffic microsimulation purposes and what are the key differences between ACC systems and human drivers.

Investigating heterogeneous car-following behaviors of different vehicle types, traffic densities and road types

For realistic traffic flow simulation and analysis, it is essential to discuss car-following behaviors in detail, while most studies only concentrate on the heterogeneity according to vehicle types based on the unstable observable variables. To overcome such shortcoming, this paper introduces car-following properties to represent car-following behaviors in different driving conditions. Based on a naturalistic trajectory dataset named The Next Generation Simulation Program (NGSIM), vehicles are divided into 7 classes based on driving conditions. The car-following model parameters of each vehicle, which have practical meanings, are considered as the car-following properties, and are calibrated by genetic algorithm (GA). And then, the heterogeneity of property is discussed based on Kolmogorov-Smirnov test (KS-test) and the rangeability to understand car-following behaviors. Results show the heterogeneity of car-following behaviors according to vehicle type and road type, while traffic density has tiny effect on car-following behaviors. And car-following properties are also heterogeneous, as the comfortable deceleration is stable, while other properties change differently with driving conditions. Besides, there is a similar pattern of change on vehicle properties and vehicle trajectories.Furthermore, potential applications of the research are provided, for instance, model more realistic traffic simulations and give support to adaptive cruise control systems (ACC) for vehicles in different conditions.

Research on Dynamic Simulation of Vehicle ACC Based on Active Disturbance Rejection Control Technology

To improve the robustness and safety of the vehicle’s adaptive cruise control system, this paper designs an active disturbance rejection controller (ACC_ADRC) with strong robustness. The upper controller is based on the active disturbance rejection control algorithm, obtains the expected acceleration of the vehicle according to the relative speed and relative distance of the preceding vehicle. The lower controller is based on PI control, according to the expected acceleration, it realizes the adaptive cruise control of the vehicle by controlling the throttle opening and the pressure of the master cylinder. Finally, based on the ADAS HIL simulation platform, the controller of this research and a mass production controller (ACC_MP) were simulated and verified. The simulation results show that compared with the ACC_MP controller, the ACC_ADRC controller has stronger robustness and reduces the overshoot of speed, acceleration and distance, and effectively improves the comfort and safety of the vehicle.

Calibration and evaluation of the Responsibility-Sensitive Safety model of autonomous car-following maneuvers using naturalistic driving study data

Safety guarantees are vital to the dependability of the automated vehicle (AV), so are of primary concern to the AV industry and regulatory bodies. Responsibility-Sensitive Safety (RSS), proposed by Mobileye, is a rigorous mathematical model that defines the real-time safety distance that the AV needs to maintain in relation to surrounding vehicles, and helps the AV respond to dangerous situations. However, RSS’s performance has not yet been tested with real driving data. This study thus calibrates and evaluates the RSS model based on car-following scenarios created from safety-critical events (SCEs) detected in the Shanghai Naturalistic Driving Study. SCEs were identified from the raw data with a trigger method; they were manually validated and then converted into virtual scenarios in which the human-driven subject vehicle was replaced with an AV controlled by a model predictive control based adaptive cruise control (ACC) system embedded with RSS. The RSS model was calibrated with the goal of achieving optimal safety performance, that is, of generating the lowest time-integrated time-to-collision (TIT) after the AV algorithms were implemented in the safety-critical scenarios. By comparing the performance of RSS-embedded ACC, ACC-only, and the unassisted human driver, this study found that: 1) RSS-embedded ACC performed best in eliminating underlying safety-critical situations by controlling the average TIT for each 15-second event at 2.65 s2; 2) RSS-embedded ACC generated the highest average relative speed and relative distance, and the lowest standard deviation of speed; and 3) on average, RSS-embedded ACC was able to perceive the SCEs 2.31 s ahead of the human driver.

Intelligent Vehicle Automatic Stop-and-Go Task Based on Humanized Learning Control Model

The automatic stop-and-go task of intelligent vehicles can make the adaptive cruise control system achieve a full-speed range. However, the conventional design methods mostly focus on functional safety, without considering drivers’ behaviors, thereby leading to a poor driving experience. To improve the situation, a humanized learning control model is used instead of mechanical switching logic. Therefore, first, the common characteristics of human drivers with different driving styles are found by analyzing real drivers’ experiments. Then, the vehicle automatic starting function is designed based on iterative learning control with the fast Fourier transform for acceleration fitting. Next, the vehicle automatic braking function is designed based on dynamic time to collision. Finally, the simulation of the stop-and-go scenario is shown in CARSIM, and the real vehicle test is performed under the urban overpass driving condition. Results show that the proposed model can improve the humanization in the vehicle stop-and-go task.

Model predictive control for a basic adaptive cruise control

A model predictive control (MPC) approach is implemented on a basic Adaptive Cruise Control (ACC) system. A vehicle is moving with a constant velocity and a following vehicle approaches the preceding vehicle and should maintain the same velocity. Using a MPC controller, the required stability with the specified input constraints and the target velocity for a constant preceding vehicle velocity was achieved. Linear-Quadratic Regulator (LQR) controller was applied in this work for comparison purpose. An MPC controller would hence be preferred in this paper. With the implementation of state constraints as well, the controller would be able to achieve the required stability for a variety of cases, especially where the distance between the vehicles is large. This destabilizes the plant and the controller is unable to achieve the stability. Hence, in this case where the difference in distance between the host and preceding vehicle is low, the controller performs satisfactorily. The controller is also tested for different prediction horizons and performs well at a horizon of $$N = 20$$ , beyond which no significant improvement is observed. Through simulations in MATLAB, it was shown that the proposed MPC strategy was able to maintain a vehicle in designated constraints, and satisfactory results were implemented fast due to the simplicity of the proposed model.

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.

String Stability and Platoon Safety Analysis of a New Car-Following Model Considering a Stabilization Strategy

Adaptive cruise control (ACC) systems can reduce collision risk and make traffic flow more smoothly; nevertheless, improving the string stability and car-following safety in ACC systems has remained an important research topic. Based on the desired safety margin (DSM) model adopted as an ACC velocity control method, a sliding mode controller is proposed to investigate string stability and car-following safety using the time headway policy (THP), and its stability is verified by the Lyapunov stability theory. Furthermore, numerical simulations are conducted to verify the effectiveness of the proposed stabilization strategy for the stability of the DSM model. Analyzing the risk assessment indexes (time-to-collision, TTC, and time headway, TH) of the DSM model reveals that the proposed stabilization strategy can improve the traffic flow stability and avoid rear-end collision risks when the leading car exhibits a small disturbance. Therefore, the proposed stabilization strategy is valuable for designing ACC controllers to enhance traffic flow stability and car-following safety in automotive platoon driving.

An extended continuum mixed traffic model

With the development of vehicle technology, the number of vehicles equipped with the adaptive cruise control (ACC) system is increasing, and it is more and more common for ACC vehicles to drive with regular vehicles. In this work, we proposed a mixed traffic model with regular vehicles and ACC vehicles via the transformation of micro-model and macro-model. The linear stability analysis tells that the permeability of ACC vehicles has an important influence on the traffic system. Meanwhile, through the nonlinear analysis, the KdV–Burgers equation describing the density wave is obtained. Besides, the results of numerical simulations are consistent with the theory, which indicates that the increase in ACC vehicles is conducive to the stability of the transportation system.

The safety potential of automatic emergency braking and adaptive cruise control and actions to improve the potential

The study investigates the potential of Automatic Emergency Braking (AEB) and Adaptive Cruise Control (ACC) systems to prevent fatal rear-end, intersection and pedestrian crashes in Finland. The sy...

Adaptive cruise hierarchical control strategy based on MPC

In order to avoid the influence of adaptive cruise control system (ACC) on ride comfort and traffic flow, a hierarchical control strategy is proposed. The prediction model for the preceding vehicle and following vehicle is established. The safety and the ride comfort of the vehicle are taken as the optimisation targets. An upper controller based on model predictive control (MPC) algorithm is constructed to obtain the desired acceleration. In order to prevent switching error between drive torque and brake torque, and improve control accuracy, the lower controller is designed by integral separation PID control algorithm to track the preceding vehicle speed and the safe vehicle spacing distance. The simulation results show that the strategy can effectively control the vehicle speed under rapid acceleration/deceleration condition and steady-state cruising condition, and track the preceding vehicle speed near the safe spacing distance to ensure the safety, high efficiency and ride comfort.

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.

Robust control of vehicle multi‐target adaptive cruise based on model prediction

On the issue of low utilisation and acceptance of current adaptive cruise control (ACC), a multi-objective adaptive cruise control (MO-ACC) algorithm is developed in this study. Based on model predictive control theory, comprehensively considering the coordination among various conflicting objectives, the decision of desired longitudinal acceleration is transformed into online quadratic programming (QP) problem. In order to compensate for prediction error caused by modelling mismatch, the robustness of control system is improved by introducing an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non-feasible solution owing to hard constraints during the process of optimisation. Further, under different work conditions, the focusing performance index along with constraint space varies, and therefore different ACC modes are established to meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as slack relaxation. The simulations show that under the combined work conditions of the preceding vehicle, the following vehicle can realise seamless switching among various working modes, and also is able to achieve the good expectation during vehicle following, which will help to enhance the adaptability of the ACC system to the complex road traffic environment.

Truck platoon security: State-of-the-art and road ahead

Trucks form a platoon, where they align in a lane on freeways, in order to save fuel. The trucks positioned behind the first truck in the platoon can save fuel because their air drag reduces. In a truck platoon, various technologies such as Adaptive Cruise Control (ACC) system and Vehicle-to-Vehicle (V2V) communication protocol are exploited to control the trucks in the platoon effectively. The camera sensors measure distance between contiguous trucks in the platoon and these sensor measurements are provided to the ACC system. By using these sensor measurement, the ACC system controls the truck speed via in-vehicle network protocols such as the controller area network, which does not encrypt data or authenticate messages. Also, the V2V communication protocol is vulnerable to cyber attacks that disable the wireless channels among the trucks in the platoon. As a result, the ACC system and V2V communication protocol may introduce attack surfaces in a truck platoon. In this survey, we analyze the attack surfaces of a truck platoon under different perspectives. Then, we summarize the defense systems corresponding to the discussed attacks in terms of detection mechanisms, response countermeasures, and proactive defenses. Finally, we provide the research issues that need to be addressed in future.

Impact of Jamming Attacks on Vehicular Cooperative Adaptive Cruise Control Systems

Cooperative Adaptive Cruise Control (CACC) is considered as a key enabling technology to automatically regulate the inter-vehicle distances in a vehicle string, and improve the traffic throughput efficiency. In the existing CACC systems, the coupling between wireless communication uncertainty, and system states is not well modeled. In this paper, we integrate the jamming attacks, and wireless channel fading effects into the CACC state space equations such that it effectively captures the coupling impact. Then, we propose a novel time domain approach to analyze the mean string stability (MSS) of such a model. Based on the proposed model, we analyze the impact of the jammer's location on the string stability. We derive a sufficient condition for the packet successful delivery probability which indicates that the jammer has a higher probability to destabilize the string when it is closer to the first vehicle following the lead vehicle. We also propose a methodology to compute the upper, and lower bounds of the inter-vehicle distance trajectories between the lead vehicle, and its follower. Furthermore, string safety is investigated by numerically estimating the collision probability across the string. We conduct comprehensive Monte Carlo simulations to evaluate the stability, and safety of the string in various scenarios. We identify that string stability, and safety are highly influenced by the jamming attacks signal, and jammer's location. We show the consistency between the main results achieved by MSS analysis, and the Monte Carlo simulations.

A Probability-Based Short-Term Velocity Prediction Method for Energy-Efficient Cruise Control

Numerous automotive control systems depend on the prediction of the velocity of the controlled vehicle or other road users in close proximity. A forecast is indispensable for the design and operation of model predictive control strategies which are utilized for example in order to optimize fuel economy in cruise control systems. In this article, a novel velocity time series prediction method, which depends exclusively on past velocity measurements, is presented and analyzed. It is based on conditional probabilities of acceleration and deceleration, which are estimated from real driving data. The proposed velocity prediction is used in a new model predictive control algorithm for an adaptive cruise control system for heavy duty vehicles, where the predicted future velocity of a preceding vehicle is needed, as well as its probability distribution. Simulation results show that an accurate velocity prediction is crucial for achieving energy-efficiency goals.

How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

Fuzzy Surrogate Safety Metrics for real-time assessment of rear-end collision risk. A study based on empirical observations

The present paper discusses two fuzzy Surrogate Safety Metrics (SSMs) for rear-end collision, the Proactive Fuzzy SSM (PFS) and Critical Fuzzy SSM (CFS). The objective is to investigate their applicability for evaluating the real-time rear-end risk of collision of vehicles to support the operations of advanced driver assistance and automated vehicle functionalities (from driving assistance systems to fully automated vehicles). The proposed Fuzzy SSMs are evaluated and compared to other traditional metrics on the basis of empirical observations. To achieve this goal, an experimental campaign was organized in the AstaZero proving ground in Sweden. The campaign consisted of two main parts: a car-following experiment with five vehicles solely driven by Adaptive Cruise Control (ACC) systems and a safety critical experiment, testing the response of the Autonomous Emergency Braking (AEB) system to avoid collisions on a static target. The proposed PFS is compared with the safe distance defined by the well-known Responsibility Sensitive Safety (RSS) model, showing that it can produce meaningful results in assessing safety conditions also without the use of crisp safety thresholds (like in the case of RSS). The CFS outperformed the well-known Time-To-Collision (TTC) SSM in the a-priori identification of the cases, where the tested vehicles were not able to avoid the collision with the static target. Moreover, results show that CFS at the time of the first deceleration is correlated with the velocity of the vehicle at the time of collisions with the target.

Less Manual Work for Safety Engineers: Towards an Automated Safety Reasoning with Safety Patterns

The development of safety-critical systems requires the control of hazards that can potentially cause harm. To this end, safety engineers rely during the development phase on architectural solutions, called safety patterns, such as safety monitors, voters, and watchdogs. The goal of these patterns is to control (identified) faults that can trigger hazards. Safety patterns can control such faults by e.g., increasing the redundancy of the system. Currently, the reasoning of which pattern to use at which part of the target system to control which hazard is documented mostly in textual form or by means of models, such as GSN-models, with limited support for automation. This paper proposes the use of logic programming engines for the automated reasoning about system safety. We propose a domain-specific language for embedded system safety and specify as disjunctive logic programs reasoning principles used by safety engineers to deploy safety patterns, e.g., when to use safety monitors, or watchdogs. Our machinery enables two types of automated safety reasoning: (1) identification of which hazards can be controlled and which ones cannot be controlled by the existing safety patterns; and (2) automated recommendation of which patterns could be used at which place of the system to control potential hazards. Finally, we apply our machinery to two examples taken from the automotive domain: an adaptive cruise control system and a battery management system.

Evaluation and Optimization of Responsibility-Sensitive Safety Models on Autonomous Car-Following Maneuvers

Safety is an important challenge in the development of autonomous vehicles (AVs). To ensure the safety of AVs, Intel and Mobileye have proposed a model called Responsibility-Sensitive Safety (RSS). Previous studies have shown that RSS has the potential to improve the safety performance of AVs, especially for partial autonomous driving algorithms. However, it is been shown that RSS leads to a considerable car-following distance, which has a negative effect on traffic efficiency. To improve the efficiency of RSS when applied to adaptive cruise control (ACC) systems, this paper proposes an improved strategy that involves triggering conditions of RSS. Two triggers of safety distance are defined according to different car-following assumptions. To test the performance of RSS models, original and improved RSS models are embedded in ACC based on model predictive control (MPC) algorithms. Car-following scenarios with a sudden deceleration of the lead vehicle (LV) at various time headways are simulated to evaluate the performance of improved RSS models. Results show that triggering RSS at the boundary of the safety distance calculated by considering the vehicle’s intentions is a better RSS model. This improved RSS model has a similar safety improvement effect to the ACC system as the original RSS in most risk scenarios and performs better in car-following efficiency. As the improved RSS model achieves a better trade-off between safety and efficiency, it can be used to improve the safety performance of partial autonomous driving algorithms like ACC on autonomous car-following maneuvers on expressways.