Adaptive Cruise Control System Research Articles

Personalized Adaptive Cruise Control Based on Online Driving Style Recognition Technology and Model Predictive Control

This paper proposes a personalized adaptive cruise control (ACC) system based on driving style recognition and model predictive control (MPC) to meet different driving styles while ensuring car-following, comfort and fuel-economy performances. To obtain the controller parameters corresponding to different driving styles, a set of real vehicle experiments are conducted to collect driving data of 66 randomly recruited drivers, then the experimental data is clustered through unsupervised machine learning method. On the basis of it, a driving style classifier is designed by supervised machine learning method, which can be used to recognize the driving style of drivers online. Then, the control problem of the personalized ACC system is described as a multi-objective optimization problem which is solved by MPC method. The simulation results show that the proposed personalized ACC system can meet the requirements of different driving styles and guarantee various performances.

Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm

An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of driving comfortability and the minimization of fuel consumption are realized in the manner of constraining the acceleration value and its variation rate, so-called the jerk, of the host vehicle. The driving safety is guaranteed by restricting the vehicle spacing always larger than minimum safe spacing from the host vehicle to the preceding vehicle. The performances of the proposed MPC-based ACC system are evaluated and compared with the conventional proportional-integral-derivative (PID) controller-based ACC system in two representative driving scenarios, through a simulation bench and an instantaneous emissions and fuel consumption model. In addition to meeting the other driving objectives mentioned above, the simulation results indicate an improvement of 13% (at the maximum) for fuel economy, which directly shows the effectiveness of the presented MPC-based ACC system.

Simultaneous Cyber-Attack Detection and Radar Sensor Health Monitoring in Connected ACC Vehicles

This paper focuses on the detection of cyber-attacks on a wireless communication channel and simultaneous radar sensor health monitoring for a connected vehicle. A semi-autonomous adaptive cruise control (SA-ACC) vehicle is considered which has wireless communication with its immediately preceding vehicle in the same lane. The wireless connectivity enables the vehicle to operate at small time-gap distances without creating string instability. However, the reliability of the wireless connectivity is critical for ensuring safe vehicle operation. The presence of two unknown inputs related to both sensor failure and cyber-attack seemingly poses a difficult estimation challenge which is addressed in this paper. The dynamic system is first represented in descriptor system form. An observer with estimation error dynamics decoupled from the cyber-attack signal is developed. The performance of the observer is first extensively evaluated in simulations. The estimation system is able to detect either a fault in the radar or a cyber-attack. Further, the proposed control strategy achieves resilient adaptive cruise control system operation by switching to larger time-gaps and a non-connected controller when a cyber-attack is detected. Finally, experimental data from a real vehicle on the I-94 Highway in Minneapolis, MN, USA is also used to evaluate the performance of the proposed algorithms. The fundamental estimation algorithm developed herein can be extended in the future to enable cyber-attack detection in more complex connected vehicle architectures.

Adaptive Cruise Control Strategy Design with Optimized Active Braking Control Algorithm

The braking quality is considered as the most important performance of the adaptive control system that influences the vehicle safety and ride comfort remarkably. This research is aimed at designing an adaptive cruise control (ACC) system based on active braking algorithm using hierarchical control. Taking into account the vehicle with safety and comfort, the upper decision-making controller is designed based on model predictive control algorithm. Throttle controller and braking controller are designed with feedforward and feedback algorithms as the bottom controller, where the braking controller is designed based on the hydraulic braking model. The whole model is simulated collaboratively with Amesim, Carsim, and Simulink. By comparison with the full deceleration model, the results show that the proposed algorithm can not only make the vehicle maintain a safe distance under the premise of following the target vehicle ahead effectively but also provide favorable driving comfort.

Control Design, Stability Analysis, and Traffic Flow Implications for Cooperative Adaptive Cruise Control Systems with Compensation of Communication Delay

Communication delay is detrimental to the performance of cooperative adaptive cruise control (CACC) systems. In this paper, we incorporate communication delay explicitly into control design and propose a delay-compensating CACC. In this new CACC system, the semi-constant time gap (Semi-CTG) policy, which is modified on the basis of the widely-used CTG policy, is employed by a linear feedback control law to regulate the spacing error. The semi-CTG policy uses historical information of the predecessor instead of its current information. By doing so, communication delay is fully compensated, which leads to better stability performance. Three stability properties—local stability, string stability, and traffic flow stability—are analyzed. The local stability and string stability of the proposed CACC system are guaranteed with the desired time gap as small as the communication delay. Both theoretical analysis and simulation results show that the delay-compensating CACC has better string stability and traffic flow stability than the widely-used CACC system. Furthermore, the proposed CACC system also shows the potential for improving traffic throughput and fuel efficiency. Robustness of the proposed system against uncertainties of sensor delay and vehicle dynamics is also verified with simulation.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial disturbances and attacks significantly restricts their applicability in safety-critical systems including cyber-physical systems (CPS) equipped with neural network components at various stages of sensing and control. This article addresses the reachable set estimation and safety verification problems for dynamical systems embedded with neural network components serving as feedback controllers. The closed-loop system can be abstracted in the form of a continuous-time sampled-data system under the control of a neural network controller. First, a novel reachable set computation method in adaptation to simulations generated out of neural networks is developed. The reachability analysis of a class of feedforward neural networks called multilayer perceptrons (MLPs) with general activation functions is performed in the framework of interval arithmetic. Then, in combination with reachability methods developed for various dynamical system classes modeled by ordinary differential equations, a recursive algorithm is developed for over-approximating the reachable set of the closed-loop system. The safety verification for neural network control systems can be performed by examining the emptiness of the intersection between the over-approximation of reachable sets and unsafe sets. The effectiveness of the proposed approach has been validated with evaluations on a robotic arm model and an adaptive cruise control system.

Cooperative Adaptive Cruise Control With Robustness Against Communication Delay: An Approach in the Space Domain

In this research, an optimal control-based Cooperative Adaptive Cruise Control (CACC) system is proposed. The proposed system is able to enforce a target time gap between platoon members and is formulated in the space domain instead of the time domain which is adopted by most optimal control-based CACC systems in the past. By having this change, its robustness against communication failure is greatly improved and thus minimum safety headway buffer is reduced which leads to better mobility. In addition, third-order vehicle dynamics are modeled into the proposed control in order to improve control precision when implemented in the field. Local stability and string stability are theoretically proven. The proposed system is evaluated by simulation. Results reveal that the proposed CACC system outperforms the state-of-the-art <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> synthesis-based controller and linear feedback-based controller. The benefit of fuel consumption reduction ranges from 0.35% to 16.11%, while the benefit of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission ranges from 0.48% to 12.40%. Furthermore, the proposed CACC improves local stability from 11.03% to 25.90%, and string stability by up to 23.82%. The computation speed of the proposed method is 1.26 ms (with prediction horizon as 1.5 s and resolution as 0.1 s) on a regular laptop which indicates the proposed system's potential to be applied in real-time.

Robust Control of Cooperative Adaptive Cruise Control in the Absence of Information About Preceding Vehicle Acceleration

Cooperative adaptive cruise control (CACC) has a potential to improve traffic throughput, fuel efficiency and vehicle safety. The CACC utilizes onboard sensors and a wireless communication to achieve vehicle stability and string stability of a platoon. The performance of CACC is degraded due to unreliable wireless communication and uncertain dynamics of the vehicle. In this paper, a disturbance observer based sliding mode control is proposed for the control of cooperative adaptive cruise control system. This scheme estimates the uncertainty present in the actuator dynamics and the acceleration of preceding vehicle as a lumped disturbance. The proposed strategy addresses practical issues such as unavailability of preceding vehicle acceleration and range of uncertainty in the vehicle dynamics. The stability of individual vehicles and the string stability of a platoon are derived. The performance of the proposed scheme is verified by considering various traffic scenarios, and it is compared with an existing method.

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle (HEV) that operates with adaptive cruise control (ACC). Real-time energy optimization is an essential issue such that the HEV powertrain system is as efficient as possible. With connected vehicle technique, ACC system shows considerable potential of high energy efficiency. Combining a classical ACC algorithm, a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon. The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle. The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.

Impact of Mixed Traffic on the Energy Savings of a Truck Platoon

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC). These scenarios are intended to address the possibility of “background aerodynamic platooning” impacting realized savings on public roads. Using calibrated J1939 fuel rate analysis, fuel savings for curved track sections versus straight track sections were also evaluated for these scenarios, highlighting differences in the implementation of the CACC control strategies compared to a stock ACC implementation. The use of different trailer types and the presence of passenger-vehicle traffic patterns showed a measurable impact on platoon performance in some conditions, but the basic fuel savings trends were retained.&lt;/div&gt;&lt;/div&gt;

Analytical studies on an optimized adaptive cruise control model of traffic flow based on self-stabilizing strategy

Cooperative adaptive cruise control (CACC) system possesses more remarkable ability to suppress disturbance and enhance the traffic capacity than adaptive cruise control (ACC). However, CACC asks for strict requirement on wireless communication and precise equipment, which remains a big difficulty to implement. This paper extends a new ACC model by introducing the self-stabilizing control with historical data, aimed at achieving the close performance of CACC and make it practicable. Substituting real-time information with pre-stored data substantially reduces the technical demand and offers high reliability to withstand the network delay. Linear stability analysis for this model points out enhancing the value of the gain or time delay of self-stabilizing control benefits to stabilize the traffic. The theories are corroborated via the simulation and further numerical simulations explicate the impact on fuel consumption and emissions and traffic capacity.

Empirical Study on the Properties of Adaptive Cruise Control Systems and Their Impact on Traffic Flow and String Stability

Adaptive cruise control (ACC) systems are standard equipment in many commercially available vehicles. They are considered the first step of automation, and their market penetration rate is expected to rise, along with the interest of researchers worldwide to assess their impact in relation to traffic flow and stability. These properties are currently discussed mainly through microsimulation studies and empirical observations, with the first being the most common. Experimental observations can draw safer conclusions about the behavior of such systems, but the literature is limited. In this work, an experimental campaign with five vehicles equipped with ACC was conducted at the proving ground of AstaZero in Sweden to improve understanding on the properties of ACC systems and their functionality under real driving conditions. The main parameters under investigation are the response time of controllers, the available time headway settings, and the stability of the car-platoon. The results show that the response time range for the controllers is between 1.7 and 2.5 s, significantly longer than the values reported in the literature. The range of the time headway settings was found to be quite broad. Finally, a dataset of perturbations on a variety of equilibrium speeds of the car-platoon and of variable magnitudes was created. Results clearly highlight the instability of the car-platoon. Instability is also displayed even for slight perturbations derived by variability in the road gradient. Numerical differentiation on the altitude shows a negative correlation with the speed trajectory of the leading vehicle.

Intelligent Traffic Flow Assist: Optimized Highway Driving Using Conditional Behavior Prediction

Many conventional systems for driver assistance on highways determine vehicle motion based on static situation configurations only. Adaptive Cruise Control systems for example react to vehicles cutting-in from neighboring lanes only after these vehicles have entered the ego-lane. Some more recent systems include prediction of future behaviors, but often neglect mutual influences between these and the best behavior for the ego vehicle. This paper introduces ?intelligent Traffic Flow Assist?, an assistance system that analyzes the current traffic situation and predicts candidate future behaviors conditioned on different options for the ego behavior. It computes vehicle trajectories optimized for safety, comfort and utility for each of the possible future situations and selects the best option for vehicle control. We describe the system architecture and implementation on a prototype vehicle with close-toproduction sensors and report on tests performed in different situations on proving ground and public highways.

Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening

In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.

Secure State Estimation for Motion Monitoring of Intelligent Connected Vehicle Systems.

This paper considers the state estimation problem of intelligent connected vehicle systems under the false data injection attack in wireless monitoring networks. We propose a new secure state estimation method to reconstruct the motion states of the connected vehicles equipped with cooperative adaptive cruise control (CACC) systems. First, the set of CACC models combined with Proportion-Differentiation (PD) controllers are used to represent the longitudinal dynamics of the intelligent connected vehicle systems. Then the notion of sparseness is employed to model the false data injection attack of the wireless networks of the monitoring platform. According to the corrupted data of the vehicles’ states, the compressed sensing principle is used to describe the secure state estimation problem of the connected vehicles. Moreover, the L1 norm optimization problem is solved to reconstruct the motion states of the vehicles based on the orthogonaldecomposition. Finally, the simulation experiments verify that the proposed method can effectively reconstruct the motion states of vehicles for remote monitoring of the intelligent connected vehicle system.

Adaptive Cruise Controller Design Without Transitional Strategy

The Adaptive cruise control (ACC) systems have been actively studied for the safety enhancement and commercialized for the last five decades. The ACC has been designed not only speed and headway distance controller, but also transition maneuver. In this study, an integrated control algorithm for the ACC is proposed to maintain the speed and the headway distance simultaneously without the transitional strategy. The proposed control algorithm also adjusts the set speed depending on the curvature of the road in coordination with the Lane Keeping (LK) system. The controller is designed based on the constant acceleration model for the real time performance. The proposed algorithm is validated in simulations first and the experimental verification is performed.

Multi-Model Adaptive Control for CACC Applications

This paper proposes a multi-model adaptive control (MMAC) algorithm based on Youla-Kucera (YK) theory to deal with heterogeneity in cooperative adaptive cruise control (CACC) systems. The main idea of MMAC is to choose the plant in a predefined set that best approximates the system dynamics, applying the corresponding predesigned controller. A set of linear plants describing different vehicle dynamics is defined. Different CACC controllers are designed depending on these linear plants. Simulation and experimental results prove how MMAC determines the closest plant in the set, choosing the CACC system able to ensure string stability.

Robust optimal control and identification of adaptive cruise control systems in the presence of time delay and parameter uncertainties

This article deals with the robust optimal control and identification of uncertain adaptive cruise control systems. Both measurement delay and engine’s lag are investigated in system modeling and control design. The control structure consists of two steps. In the first step, by using a new approach, the uncertain parameters of longitudinal dynamics of each vehicle is identified. Afterwards, at the second step, by using the relative position and velocity measurement with respect to ahead vehicle, a robust control against size changing of platoon is presented to assure the internal stability, string stability, and safety of heterogeneous vehicular platoons. A cost function involving important metrics internal stability, maximum overshoot, and settling time is introduced, and the particle swarm optimization algorithm is used to find the optimal values of control parameters minimizing the cost function. It will be shown that the proposed robust controller guarantees the internal stability, string stability, and safety of adaptive cruise control systems in the presence of measurement and parasitic delays. Several simulation results are provided to show the effectiveness of the proposed approaches.

Active Fault Tolerant Control of Adaptive Cruise Control System Considering Vehicle-Borne Millimeter Wave Radar Sensor Failure

Vehicle-borne millimeter wave radar sensor may malfunction during signal acquisition and transmission process, which will affect the decision of adaptive cruise control (ACC) system and the safe driving of vehicles. However, adding other redundant environment-aware devices will increases costs. Therefore, in this paper, an active fault tolerant control of ACC system considering vehicle-borne millimeter wave radar sensor failure is proposed. Sensor faults are taken as discrete events, and the mixed logical dynamical (MLD) model of ACC upper control system is built which includes both the fault-free dynamics and the fault dynamics of the system. Then, the active fault tolerant control model of ACC system is established based on model predictive control (MPC) framework. Compared with the existing researches, this work emphasizes on the active fault tolerant control without adding other redundant environment-aware devices, which has not been fully revealed by the existing researches and is important to the industrial application. Moreover, the active fault tolerant control method can be easily ported to other driver assistance system besides ACC. The simulation results show that the vehicle equipped with the active fault tolerant ACC system can still drive safely and smoothly without being affected by radar sensor failures, which demonstrates its great significance to improve vehicle’s own intelligence and ensure the safe driving instead of relying entirely on sensors.