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 Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sheng Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Energy Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Simulation Experiment ◽  
Hierarchical Control ◽  
Battery Electric Vehicle ◽  
Cruise Control ◽  
Car Following ◽  
Energy Economy

This paper studies the control strategy for adaptive cruise control (ACC) system on a battery electric vehicle (BEV) in the car-following process, and the highlight of this paper is that the regeneration braking of BEV is considered in the car-following process. The hierarchical control structure is adopted for the ACC system. And the structure contains an upper controller and a lower controller. In the upper controller, multiple objectives including the safety, tracking, comfort, and energy consumption are optimized by using the model predictive control (MPC) method. In the lower controller, the energy is recovered during braking. So the energy economy is improved by reducing energy consumption and increasing energy recovery. The proposed ACC strategy is evaluated in simulation experiment. In the simulation experiment, safe tracking for the front vehicle is guaranteed, and the comfort and the energy economy are improved greatly. So the proposed adaptive cruise control strategy can make ACC more widely used in BEVs.

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.

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.

Real time adaptive cruise control strategy for motorways

2020 ◽  
Vol 115 ◽  
pp. 102617
Author(s):  
Diamantis Manolis ◽  
Anastasia Spiliopoulou ◽  
Foteini Vandorou ◽  
Markos Papageorgiou
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Cruise Control

scholarly journals Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

2019 ◽  
Vol 9 (22) ◽  
pp. 4875 ◽  
Author(s):  
Hanwool Woo ◽  
Hirokazu Madokoro ◽  
Kazuhito Sato ◽  
Yusuke Tamura ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Individual Characteristics ◽  
Driving Safety ◽  
Cruise Control ◽  
Collision Risk ◽  
Trajectory Prediction ◽  
Lane Changes ◽  
The Individual ◽  
Near Future ◽  
Real Traffic

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers.

scholarly journals Adaptive Cruise Control Strategy Design with Optimized Active Braking Control Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wenguang Wu ◽  
Debiao Zou ◽  
Jian Ou ◽  
Lin Hu
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Hierarchical Control ◽  
Cruise Control ◽  
Safe Distance ◽  
Model Predictive Control Algorithm ◽  
Braking Control ◽  
Strategy Design

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.

Sign in / Sign up

Export Citation Format

Share Document