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.

Potential field–based hierarchical adaptive cruise control for semi-autonomous electric vehicle

2018 ◽  
Vol 233 (10) ◽  
pp. 2479-2491 ◽  
Author(s):  
Yue Ren ◽  
Ling Zheng ◽  
Wei Yang ◽  
Yinong Li
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Potential Field ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Tracking Error ◽  
Driving Safety ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Collision Risk

Adaptive cruise control, as a driver assistant system for vehicles, can adjust the vehicle speed to keep the appropriate distance from other vehicles, which highly increases the driving safety and driver’s comfort. This paper presents hierarchical adaptive cruise control system that could balance the driver’s expectation, collision risk, and ride comfort. In the adaptive cruise control structure, there are two controllers to achieve the function. The one is the upper controller which is established based on the model predictive control theory and used to calculate the desirable longitudinal acceleration. The collision risk is described by the Gaussian distribution. A quadratic cost function for model predictive control is formulated based on the potential field method through the contradictions between the tracking error, collision risk, and the longitudinal ride comfort. The other one is the lower optimal torque vectoring controller which is constructed based on the vehicle longitudinal dynamics. And it can generate the desired acceleration considering the anti-wheel slip limitations. Several simulations under different road conditions demonstrate that the proposed adaptive cruise control has significant performance on balancing the tracking ability, collision avoidance, ride comfort, and adhesion utilization. It also maintains vehicle stability for the complex road conditions.

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.

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.

A non-linear disturbance observer-based cooperative adaptive cruise control for real traffic scenarios

2021 ◽  
pp. 095440702110518
Author(s):  
Jaswandi Sawant ◽  
Uttam Chaskar
Keyword(s):  
Disturbance Observer ◽  
Adaptive Cruise Control ◽  
Initial Conditions ◽  
Control Input ◽  
Highway Traffic ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control ◽  
Non Linear ◽  
Linear Disturbance ◽  
Real Traffic

Cooperative adaptive cruise control (CACC) has a strong potential to improvise highway traffic capacity and ease traffic disturbances. Extensive exploration is not carried out in the area of CACC for a cut-in maneuver. Contemporary control strategies proposed for CACC cannot regulate the peaking of control input and thus the acceleration/deceleration of following vehicles when applied for various real traffic scenarios. This paper aims to develop a non-linear disturbance observer-based sliding mode control to control a CACC system for various traffic scenarios. The proposed observer estimates the uncertainty present in the actuator dynamics and the preceding vehicle’s acceleration as the lumped disturbance at the same time, it adjusts the observer gain to alleviate the peaking of control input. The stability of individual vehicles and the string stability of vehicle platoon are derived The performance of the proposed scheme is validated with various traffic scenarios, that is, cut-in maneuver, cut-out maneuver, and non-zero initial conditions. The effectiveness of the proposed scheme is demonstrated by comparing it with a linear disturbance observer-based control.

scholarly journals Cooperative Adaptive Cruise Control in Real Traffic Situations

2014 ◽  
Vol 15 (1) ◽  
pp. 296-305 ◽  
Author(s):  
Vicente Milanes ◽  
Steven E. Shladover ◽  
John Spring ◽  
Christopher Nowakowski ◽  
Hiroshi Kawazoe ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control ◽  
Real Traffic

scholarly journals Efficient predictive cruise control of autonomous vehicles with improving ride comfort and safety

2020 ◽  
Vol 53 (1-2) ◽  
pp. 18-28
Author(s):  
Defeng He ◽  
Wentao He ◽  
Xiulan Song
Keyword(s):  
Control Problem ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Main Idea ◽  
Driving Safety ◽  
Decision Variable ◽  
Cruise Control ◽  
Constrained Optimal Control ◽  
Control Approach

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.

scholarly journals Assessment of Driving Proficiency When Drivers Utilize Assistance Systems—The Case of Adaptive Cruise Control

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 33
Author(s):  
Arie P. van den Beukel ◽  
Cornelie J. G. van Driel ◽  
Anika Boelhouwer ◽  
Nina Veders ◽  
Tobias Heffelaar
Keyword(s):  
Adaptive Cruise Control ◽  
Road Traffic ◽  
Negative Influence ◽  
Qualitative Assessment ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Novice Drivers ◽  
Lane Changes ◽  
Important Means ◽  
Assistance Systems

Driver assistance systems (ADAS), and especially those containing driving automation, change the role of drivers to supervisors who need to safeguard the system’s operation. Despite the aim to increase safety, the new tasks (supervision and intervention) may jeopardize safety. Consequently, safety officers address the need for specific training on ADAS. However, these tasks are not assessed in driver licensing today. Therefore, we developed a framework to assess in-practice driving proficiency when drivers utilize ADAS. This study evaluated whether the proposed framework is able to identify meaningful differences in driving proficiency between driving with and without assistance. We applied the framework to perform a qualitative assessment of driving proficiency with 12 novice drivers in a field experiment, comparable to a license test. The assistance system concerned Adaptive Cruise Control (ACC). The test showed that driving with ACC has a negative influence on self-initiated manoeuvres (especially lane changes) and sometimes led to improved adaptations to manoeuvres initiated by other road users (like merging in traffic). These results are in line with previous research and demonstrate the framework’s successfulness to assess novice drivers’ proficiency to utilize ADAS in road-traffic. Therewith, the proposed framework provides important means for driving instructors and examiners to address the safe operation of ADAS.

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