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

Yu, Ligen; Wang, Ruyue

doi:10.1177/09544070211019254

Cited by 44 publications

(14 citation statements)

References 159 publications

(170 reference statements)

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“…ACC systems typically employ hierarchical control, which is composed of three layers: perception, decision and execution [ 6 , 7 ]. The primary role of the perception layer is to acquire the kinematic information from the vehicle and transmit it to the decision layer.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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“…The intelligent and connected vehicles are a trend in development of future automobiles, and the advanced driver assistant system (ADAS) is being applied in vehicles gradually, which can bring out great comfort and safety, and improve traffic efficiency. 1,2 ADAS and autonomous driving vehicles require a reliable perception of the vehicle environment. Hence, the problem of tracking a moving object from the measurements is the essential component of its application.…”

Section: Introductionmentioning

confidence: 99%

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

Zhang

Liu

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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The adaptive cruise control (ACC) system as a typical advanced driver assistant system (ADAS) has been commercially application in automotive industry for decades. An innovative method is proposed in this paper for scene recognition and target tracking for ACC application in some complex traffic environment. Firstly, a multi-sensor fusion method is established to estimate the curvature integrated by the quadratic programing (QP)-based lane boundaries detection, vehicle dynamics of lateral motion, and an improved Kalman filter (IKF) to introduce more measurement information into the feedback correction process. Then, the closet in-path vehicle (CIPV) can be selected according to the statistical distance between the tracked targets and the predicted driving path of ego vehicle. To distinguish the lane changing and curve driving behaviors, the trajectory models of obstacles are established as an ellipsoid domain equation and transformed into a regression model, which is recast as a standardized QP problem. Hence, the behaviors and scenes can be recognized effectively. To restrain the disturbance and improve the accuracy and robustness of target tracking, an [Formula: see text]-based switched tracking method is proposed by combining of the low pass filter (LPF) and [Formula: see text] theory. Finally, an accurate and robust tracker is provided for the CIPV by incorporating with four steps: IKF-based curvature fusion estimation, CIPV selection, QP-based scene recognition, and the [Formula: see text]-based observer. Moreover, two real car experiments are adopted and the results verify the effectiveness and real-time performance of the proposed method.

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“…As the research towards fully autonomous vehicles is still in progress [1], users now have easy access to lesser degrees of vehicle automation with a variety of driver-assist technologies called Advanced Driver Assistance Systems (ADAS) [2], these technologies include (adaptive) cruise control [3], lane-keeping assistance [4], automated emergency braking [5], and lane departure warning, among others.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Decision-Making Approach for Adaptive Cruise Control in Autonomous Vehicles

Ghraizi,

Talj,

Francis

2023

2023 21st International Conference on Advanced Robotics (ICAR)

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In the evolving automobile industry, Adaptive Cruise Control (ACC) is key for aiding autonomous traffic navigation. Ideal ACC systems can decelerate to low speeds in stop-and-go traffic, maintain a safe following distance, minimize rear-end collision risks, and lessen the driver's need to continually adjust vehicle's speed to match traffic flow. In this paper, we offer a Deep Reinforcement Learning-based adaptive cruise control (DRL-ACC) system that creates safe, flexible, and responsive car-following policies agents. Instead of using discrete incremental and decremental values or a continuous action space, we suggest constructing a discrete high-level action space to accelerate, decelerate, and hold the current speed. We also provide a comprehensive, easyto-interpret multi-objective reward function that reflects safe, responsive, and rational traffic behavior. This strategy, trained on a single steady-state flow car-following scenario, promotes steadiness, responsiveness, and shows better generalization to diverse car-following scenarios. Results are also compared to the conventional Intelligent Driver Model (IDM). We further explore the model's potential to avoid rear-end collisions and facilitate future integration of lane-change maneuvers, which will increase its effectiveness in emergency situations.

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

Cited by 44 publications

References 159 publications

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

A Deep Reinforcement Learning Decision-Making Approach for Adaptive Cruise Control in Autonomous Vehicles

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