scholarly journals Integrated Communication and Control Design for Fuel-Efficient Vehicle Platooning

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3117
Author(s):  
Qingji Wen ◽  
Bin-Jie Hu
Keyword(s):  
Resource Allocation ◽  
Fuel Consumption ◽  
Minimum Weight ◽  
Ergodic Capacity ◽  
Autonomous Driving ◽  
High Density ◽  
Distributed Model ◽  
Safe Driving ◽  
Vehicle Platooning ◽  
And Control

As a promising application for autonomous driving, vehicle platooning aims at increasing traffic throughput, improving road safety, and reducing air pollution and fuel consumption. However, frequent traffic perturbations will bring more fuel consumption because vehicles driving in a platoon require more control to ensure safe driving, especially in high-density scenes. In this paper, considering the traffic perturbations and high-density scenes, we integrate communication and control systems to reduce the fuel consumption of a platoon. By obtaining the velocities of multiple vehicles ahead through a long-term evolution-vehicle (LTE-V) network, we propose a modified distributed model predictive control (DMPC) method to smooth traffic perturbations and handle the constraints of vehicle state and control. In addition, considering a limited number of uplink channels that can be reused in the platoon and the uncertainty of wireless channels, a radio resource allocation optimization problem in the LTE-V network is modeled. This problem is solved in two steps including maximum vehicle-to-vehicle (V2V) broadcast distance and minimum weight matching. This resource allocation scheme increases the platoon-based V2V broadcast distance while ensuring the ergodic capacity requirement of the cellular user (CUE) uplink communication and the reliability of platoon-based V2V communication. Simulation results show that the proposed method improves fuel efficiency compared to the existing schemes.

Joint Radio Resource Allocation and Control for Vehicle Platooning in LTE-V2V Network

2018 ◽  
Vol 67 (12) ◽  
pp. 12218-12230 ◽  
Author(s):  
Jie Mei ◽  
Kan Zheng ◽  
Long Zhao ◽  
Lei Lei ◽  
Xianbin Wang
Keyword(s):  
Resource Allocation ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Vehicle Platooning ◽  
And Control

scholarly journals Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5053 ◽  
Author(s):  
Saba Arshad ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Dinh Van Nam ◽  
Gon-Woo Kim
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unified Framework ◽  
Human Errors ◽  
Safe Driving ◽  
Planning And Control ◽  
Starting Point ◽  
And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

scholarly journals Braking performance oriented multi–objective optimal design of electro–mechanical brake parameters

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251714
Author(s):  
Tong Wu ◽  
Jing Li ◽  
Xuan Qin
Keyword(s):  
Optimal Design ◽  
Lateral Displacement ◽  
Autonomous Driving ◽  
The Body ◽  
Parameters Optimization ◽  
Connected Vehicle ◽  
Safe Driving ◽  
Braking Performance ◽  
Multi Objective ◽  
And Control

Excellent braking performance is the premise of safe driving, and improve the braking performance by upgrading structures and optimizing parameters of braking systems has become the pursuit of engineers. With the development of autonomous driving and intelligent connected vehicle, new structural schemes such as electro–mechanical brakes (EMBs) have become the future of vehicle braking systems. Meanwhile, many scholars have dedicated to the research on the parameters optimization of braking systems. While, most of the studies focus on reducing the brake size and weight, improving the brake responses by optimizing the parameters, almost not involving the braking performance, and the optimization variables are relatively single. On these foundations, a multi–objective optimal design of EMB parameters is proposed to enhance the vehicle’s braking performance. Its objectives and constraints were defined based on relevant standards and regulations. Subsequently, the decision variables were set, and optimal math model was established. Furthermore, the co–simulation platform was constructed, and the optimal design and simulation analyses factoring in the crucial structural and control parameters were performed. The results confirmed that the maximum braking pressure response time of the EMB is decreased by approximately 0.3 s, the stopping distance (SD) of 90 km/h–0 is shortened by about 3.44 m. Moreover, the mean fully developed deceleration (MFDD) is increased by 0.002 g, and the lateral displacement of the body (LD) is reduced by about 0.037 m. Hence, the vehicle braking performance is improved.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

High-density cochlear implants with position sensing and control

2008 ◽  
Vol 242 (1-2) ◽  
pp. 22-30 ◽  
Author(s):  
Kensall D. Wise ◽  
Pamela T. Bhatti ◽  
Jianbai Wang ◽  
Craig R. Friedrich
Keyword(s):  
Cochlear Implants ◽  
High Density ◽  
Position Sensing ◽  
And Control

Integrated Steering and Braking Control System for Collision Avoidance by Using Virtual Repulsive Force Field Method

2018 ◽  
Author(s):  
Atsushi Yokoyama ◽  
Pongsathorn Raksincharoensak ◽  
Naoto Yoshikawa
Keyword(s):  
Force Field ◽  
Collision Avoidance ◽  
Autonomous Driving ◽  
Field Method ◽  
Repulsive Force ◽  
Driver Assistance Systems ◽  
Mass Model ◽  
Braking Control ◽  
And Control ◽  
Control Activation

Advanced Driver Assistance Systems (ADAS) and autonomous driving systems are being enhanced to deal with various types of collision avoidance use-case scenarios. To handle those complicated scenarios, a unified two-dimensional planar motion control methodology assuming virtual repulsive force from obstacles is introduced, which is physically interpretable and comprehensible. The direction and magnitude of virtual repulsive force are determined considering the orientation of obstacle surface planes and the friction limit between tires and road surface respectively. Applying the concept of virtual repulsive force field, the collision avoidance path can be derived from geometrical relationship and the control activation points can be obtained as algebraic solutions. By using a simple particle mass model, the formulation for path and control activation point is described. The simulation is conducted against not only in the case of a straight roadway but also in the case of a curve roadway. By designing feedforward and feedback controllers based on a two-wheel vehicle dynamics model, the effectiveness of the proposed method is verified and the feasibility of controller implementation for actual vehicle is also investigated.

LV100 AIPS Technology—For Future Army Propulsion

1992 ◽  
Author(s):  
Walter Brockett ◽  
Angelo Koschier
Keyword(s):  
Control Systems ◽  
Gas Turbine ◽  
Fuel Consumption ◽  
Cooling System ◽  
Gas Turbine Engine ◽  
Air Filtration ◽  
Turbine Engine ◽  
Overall Design ◽  
Armored Vehicle ◽  
And Control

The overall design of and Advanced Integrated Propulsion System (AIPS), powered by an LV100 gas turbine engine, is presented along with major test accomplishments. AIPS was a demonstrator program that included design, fabrication, and test of an advanced rear drive powerpack for application in a future heavy armored vehicle (54.4 tonnes gross weight). The AIPS design achieved significant improvements in volume, performance, fuel consumption, reliability/durability, weight and signature reduction. Major components of AIPS included the recuperated LV100 turbine engine, a hydrokinetic transmission, final drives, self-cleaning air filtration (SCAF), cooling system, signature reduction systems, electrical and hydraulic components, and control systems with diagnostics/prognostics and maintainability features.

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