scholarly journals Molecular Dynamics Characteristics and Model of Vehicle-Following Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yanfeng Jia ◽  
Dayi Qu ◽  
Xiaolong Ma ◽  
Lu Lin ◽  
Jiale Hong
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Cruise Control ◽  
Dynamic Balance ◽  
Model Parameters ◽  
Cruise Control ◽  
Long Distance ◽  
The Road ◽  
Preceding Vehicle ◽  
Acceleration And Deceleration ◽  
On The Road

The vehicle-following behavior is a self-organizing behavior that restores dynamic balance under the stimulation of external environmental factors. In fact, there are asymmetric problems in the process of acceleration and deceleration of drivers. The existing traditional models ignored the differences between acceleration and deceleration of vehicles. In order to solve this problem, the vehicles driving on the road are compared to interacting molecules. Vehicle-following characteristics are studied, and the molecular following model is established based on molecular dynamics. The model parameters under different conditions are calibrated considering the required safety distance by the vehicle and the reaction time of the driver. With the help of the vehicle running track graphs, speed, and acceleration graphs, the numerical simulations of the molecular following model and the classical optimal speed vehicle-following model are carried out. The results of the comparative analysis show that the acceleration in the process of acceleration and deceleration is not constant but more sensitive to the deceleration of the preceding vehicle than to the acceleration and more sensitive to the acceleration/deceleration of the short-distance vehicle than to the acceleration/deceleration of the long-distance vehicle. Therefore, the molecular following model can better describe the vehicle-following behavior, and the research results can provide a theoretical basis and a technical reference for the analysis of traffic flow dynamic characteristics and adaptive cruise control technology.

A First Investigation of Truck Drivers’ Preferences and Behaviors using a Prototype Cooperative Adaptive Cruise Control System

2018 ◽  
Vol 2672 (34) ◽  
pp. 39-48 ◽  
Author(s):  
Shiyan Yang ◽  
Steven E. Shladover ◽  
Xiao-Yun Lu ◽  
Hani Ramezani ◽  
Aravind Kailas ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Aerodynamic Drag ◽  
Truck Drivers ◽  
Cruise Control ◽  
Fuel Savings ◽  
The Road ◽  
Cooperative Adaptive Cruise Control ◽  
And Behaviors ◽  
On The Road ◽  
And Performance

Cooperative adaptive cruise control (CACC) is a driver-assist technology that uses vehicle-to-vehicle wireless communication to realize faster braking responses in following vehicles and shorter headways compared with adaptive cruise control. This technology not only enhances road safety, but also offers fuel savings benefits as a result of reduced aerodynamic drag. The amount of fuel savings is dictated by the following distances and the driving speeds. So, the overarching goal of this work is to explore driving preferences and behaviors when following in “CACC mode,” an area that remains largely unexplored. While in CACC mode, the brake and throttle actions are automated. A human factors study was conducted to investigate truck drivers’ experiences and performance using CACC at shorter-than-normal vehicle following time gaps. “On-the-road” experiments were conducted by recruiting drivers from commercial fleets to operate the second and third trucks in a three-truck CACC string. The driving route spanned 160 miles on freeways in Northern California and five different time gaps between 0.6 and 1.8 seconds were tested. Factors such as cut-ins by other vehicles, road grades, and traffic conditions were found to influence the drivers’ opinions about use of CACC. The findings presented in this paper provide insights into the factors that will influence driver reactions to the deployment of CACC in their truck fleets.

Driver Preferences for Adjustable Distance Control Labels for an Adaptive Cruise Control (ACC) System

1997 ◽  
Vol 41 (2) ◽  
pp. 934-938 ◽  
Author(s):  
Colleen Serafin
Keyword(s):  
Adaptive Cruise Control ◽  
Cruise Control ◽  
The Road ◽  
Usability Tests ◽  
Distance Control ◽  
Separate Control ◽  
On The Road

This study investigated driver preferences for labels of adjustable distance controls for an adaptive cruise control (ACC) system. Thirty-six participants were introduced to the concept of ACC by using a computer prototype of an ACC system. Participants were asked to provide their preferences for labels for two types of adjustable distance controls: one type that adjusts both speed and distance (shared controls) and another that adjusts only distance (separate control). For shared controls, participants preferred the labels ACC/DEC over ‘+/-’ and ACC/COAST. The labels preferred for the separate control were NEAR/FAR as opposed to symbols (arrows or chevrons). Due to some confusion that may arise from the abbreviations on the shared controls, additional investigations into appropriate labels, with the emphasis on international use (i.e., symbols), are recommended. Finally, because these preferences were obtained through the use of a computer prototype of ACC, usability tests should be conducted on the road to validate the results.

Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Mehmet Fatih Ozkan ◽  
Yao Ma
Keyword(s):  
Fuel Consumption ◽  
Traffic Safety ◽  
Fuel Economy ◽  
Vehicle Speed ◽  
Cruise Control ◽  
The Road ◽  
Comparison Results ◽  
Preceding Vehicle ◽  
Speed Prediction ◽  
On The Road

Abstract The development of vehicle connectivity and autonomy in the ground transportation sector is not only able to enhance traffic safety and driving comfort as well as fuel economy. This study presents a receding-horizon optimization-based control strategy integrated with the preceding vehicle speed prediction model to achieve an eco-driving strategy for connected and automated vehicles (CAVs). In the real traffic scenario where the CAV follows the preceding vehicle on the road, a gated recurrent unit (GRU) network is used to predict the behavior of the preceding vehicle by utilizing the historical inter-vehicle information collected through on-board sensors. Then, a nonlinear model predictive control (NMPC) algorithm is adopted for CAV to minimize the accumulated fuel consumption within the preview horizon. The NMPC approach solves the fuel-optimal speed profile of the CAV, considering a predicted short-term speed preview of the preceding vehicle. With the awareness of the preview speed conditions, the fuel consumption of the CAV is reduced by avoiding unnecessary braking and acceleration, especially during transient traffic conditions. The Pareto front framework is used to examine a trade-off between the vehicle speed prediction accuracy, computational burden, and the fuel consumption of the CAV in the proposed GRU-NMPC design. To analyze the effectiveness of the GRU-NMPC design, adaptive cruise control with constant time headway policy (ACC-CTH) is adopted as a benchmark control design. Comparison results show significant fuel economy improvement of the proposed design and expose possible fuel benefits from vehicle autonomy and sensor fusion technology.

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.

scholarly journals Determination of the Dynamic Vehicle Model Parameters by Means of Computer Vision

2019 ◽  
Vol 21 (3) ◽  
pp. 28-34
Author(s):  
Daniil A. Loktev ◽  
Alexey A. Loktev ◽  
Alexandra V. Salnikova ◽  
Anna A. Shaforostova
Keyword(s):  
Computer Vision ◽  
Road Traffic ◽  
Deformable Body ◽  
Model Parameters ◽  
Vehicle Model ◽  
The Road ◽  
Complex Approach ◽  
On The Road ◽  
The Individual

This study is devoted to determining the geometric, kinematic and dynamic characteristics of a vehicle. To this purpose, it is proposed to use a complex approach applying the models of deformable body mechanics for describing the oscillatory movements of a vehicle and the computer vision algorithms for processing a series of object images to determine the state parameters of a vehicle on the road. The model of the vehicle vertical oscillations is produced by means of the viscoelastic elements and the dry friction element that fully enough represent the behavior of the sprung masses. The introduced algorithms and models can be used as a part of a complex system for monitoring and controlling the road traffic. In addition, they can determine both the speed of the car and its dynamic parameters and the driving behavior of the individual drivers.

scholarly journals Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Jun Yao ◽  
Guoying Chen ◽  
Zhenhai Gao
Keyword(s):  
Adaptive Cruise Control ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Lane Change ◽  
Selection Algorithm ◽  
Cruise Control ◽  
Lane Changing ◽  
Target Vehicle ◽  
Preceding Vehicle ◽  
The Subject

AbstractTo improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle.

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.

Sign in / Sign up

Export Citation Format

Share Document