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

2021 ◽  
pp. 095440702110192
Author(s):  
Liangyao Yu ◽  
Ruyue Wang
Keyword(s):  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
System A ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Energy Economy ◽  
Machine Interaction

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.

Adaptive cruise control system based on improved variable time headway spacing strategy

2021 ◽  
Vol 40 (1) ◽  
pp. 1471-1479
Author(s):  
Jin Mao ◽  
Lei Yang ◽  
Kai Liu ◽  
Jinfu Du ◽  
Yahui Cui
Keyword(s):  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Variable Time ◽  
Time Headway ◽  
System A ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Deceleration Time

In the following process, in order to improve the driving safety and road utilization of the adaptive cruise control (ACC) system, a variable time headway spacing strategy was studied. In view of the fact that the variable spacing strategy cannot adapt to the complex and variable deceleration conditions, an improved variable time headway strategy is proposed, which changes with the deceleration time and deceleration of the preceding vehicle. Based on this, the upper controller of adaptive cruise control based on model predictive control is designed, and numerical simulation of the variable time headway spacing strategy is performed, which verifies the effectiveness of the improved variable time headway strategy. The results show that the spacing strategy proposed in this paper can more smoothly keep up with the preceding vehicle, and improve driving safety, comfort and road utilization.

scholarly journals Safety problems in vehicles with adaptive cruise control system

2017 ◽  
Vol 42 (1) ◽  
pp. 389-398 ◽  
Author(s):  
Arun K. Yadav ◽  
Janusz Szpytko
Keyword(s):  
Control System ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Main Concern ◽  
Cruise Control ◽  
Adaptive Cruise Control System ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Driving Stability ◽  
Autonomous Technology

Abstract In today’s world automotive industries are still putting efforts towards more autonomous vehicles (AVs). The main concern of introducing the autonomous technology is safety of driver. According to a survey 90% of accidents happen due to mistake of driver. The adaptive cruise control system (ACC) is a system which combines cruise control with a collision avoidance system. The ACC system is based on laser and radar technologies. This system is capable of controlling the velocity of vehicle automatically to match the velocity of car, bus or truck in front of vehicle. If the lead vehicle gets slow down or accelerate, than ACC system automatically matches that velocity. The proposed paper is focusing on more accurate methods of detecting the preceding vehicle by using a radar and lidar sensors by considering the vehicle side slip and by controlling the distance between two vehicles. By using this approach i.e. logic for calculation of former vehicle distance and controlling the throttle valve of ACC equipped vehicle, an improvement in driving stability was achieved. The own contribution results with fuel efficient driving and with more safer and reliable driving system, but still some improvements are going on to make it more safe and reliable.

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

2021 ◽  
Vol 11 (24) ◽  
pp. 12137
Author(s):  
Fei-Xue Wang ◽  
Qian Peng ◽  
Xin-Liang Zang ◽  
Qi-Fan Xue
Keyword(s):  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Hierarchical Control ◽  
Driving Safety ◽  
Cruise Control ◽  
City Bus ◽  
Tracking Ability ◽  
Vehicle Acceleration ◽  
Energy Economy ◽  
Road Slope

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.

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

2020 ◽  
Vol 10 (15) ◽  
pp. 5271
Author(s):  
Zifei Nie ◽  
Hooman Farzaneh
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Car Following ◽  
Preceding Vehicle ◽  
Variation Rate ◽  
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.

scholarly journals Developing a Distributed Consensus-Based Cooperative Adaptive Cruise Control System for Heterogeneous Vehicles with Predecessor Following Topology

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Ziran Wang ◽  
Guoyuan Wu ◽  
Matthew J. Barth
Keyword(s):  
Adaptive Cruise Control ◽  
Consensus Algorithm ◽  
Driving Safety ◽  
Cruise Control ◽  
Distributed Consensus ◽  
Flow Topology ◽  
Braking Performance ◽  
V2v Communication ◽  
Cooperative Adaptive Cruise Control ◽  
Cruise Control System

Connected and automated vehicle (CAV) has become an increasingly popular topic recently. As an application, Cooperative Adaptive Cruise Control (CACC) systems are of high interest, allowing CAVs to communicate with each other and coordinating their maneuvers to form platoons, where one vehicle follows another with a constant velocity and/or time headway. In this study, we propose a novel CACC system, where distributed consensus algorithm and protocol are designed for platoon formation, merging maneuvers, and splitting maneuvers. Predecessor following information flow topology is adopted for the system, where each vehicle only communicates with its following vehicle to reach consensus of the whole platoon, making the vehicle-to-vehicle (V2V) communication fast and accurate. Moreover, different from most studies assuming the type and dynamics of all the vehicles in a platoon to be homogenous, we take into account the length, location of GPS antenna on vehicle, and braking performance of different vehicles. A simulation study has been conducted under scenarios including normal platoon formation, platoon restoration from disturbances, and merging and splitting maneuvers. We have also carried out a sensitivity analysis on the distributed consensus algorithm, investigating the effect of the damping gain on convergence rate, driving comfort, and driving safety of the system.

scholarly journals Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

2021 ◽  
Vol 13 (8) ◽  
pp. 4572
Author(s):  
Jiří David ◽  
Pavel Brom ◽  
František Starý ◽  
Josef Bradáč ◽  
Vojtěch Dynybyl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Cruise Control ◽  
Control Unit ◽  
Driving Safety ◽  
Model Parameters ◽  
Cruise Control ◽  
Artificial Neural ◽  
Dynamic Obstacle ◽  
Braking Process

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

Estimating reaction time in Adaptive Cruise Control System

2018 ◽  
Author(s):  
Michail Makridis ◽  
Konstantinos Mattas ◽  
Daniele Borio ◽  
Raimondo Giuliani ◽  
Biagio Ciuffo
Keyword(s):  
Control System ◽  
Reaction Time ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Adaptive Cruise Control System ◽  
Cruise Control System
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